tirana-survey-cyber-dl
Query: deep learning cyber security Indonesian survey
Results: 50
Date: 2026-07-08T07:35:23.025Z
1. Assessing Cyber-Physical Security in Industrial Control Systems
Authors: Martín Barrère, Chris Hankin, Demetrios G. Eliades, Nicolas Nicolau, Thomas Parisini
Categories: cs.CR, cs.NI, eess.SY
Published: 2019-11-21
arXiv: 1911.09404v1
Abstract:
Over the last years, Industrial Control Systems (ICS) have become increasingly exposed to a wide range of cyber-physical threats. Efficient models and techniques able to capture their complex structure and identify critical cyber-physical components are therefore essential. AND/OR graphs have proven very useful in this context as they are able to semantically grasp intricate logical interdependencies among ICS components. However, identifying critical nodes in AND/OR graphs is an NP-complete problem. In addition, ICS settings normally involve various cyber and physical security measures that simultaneously protect multiple ICS components in overlapping manners, which makes this problem even harder. In this paper, we present an extended security metric based on AND/OR hypergraphs which efficiently identifies the set of critical ICS components and security measures that should be compromised, with minimum cost (effort) for an attacker, in order to disrupt the operation of vital ICS assets. Our approach relies on MAX-SAT techniques, which we have incorporated in META4ICS, a Java-based security metric analyser for ICS. We also provide a thorough performance evaluation that shows the feasibility of our method. Finally, we illustrate our methodology through a case study in which we analyse the security posture of a realistic Water Transport Network (WTN).
2. Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security
Authors: Alec Wilson, Ryan Menzies, Neela Morarji, David Foster, Marco Casassa Mont, Esin Turkbeyler, Lisa Gralewski
Categories: cs.LG, cs.CR, cs.MA
Published: 2024-01-18
arXiv: 2401.10149v1
Abstract:
This paper demonstrates the potential for autonomous cyber defence to be applied on industrial control systems and provides a baseline environment to further explore Multi-Agent Reinforcement Learning’s (MARL) application to this problem domain. It introduces a simulation environment, IPMSRL, of a generic Integrated Platform Management System (IPMS) and explores the use of MARL for autonomous cyber defence decision-making on generic maritime based IPMS Operational Technology (OT). OT cyber defensive actions are less mature than they are for Enterprise IT. This is due to the relatively brittle nature of OT infrastructure originating from the use of legacy systems, design-time engineering assumptions, and lack of full-scale modern security controls. There are many obstacles to be tackled across the cyber landscape due to continually increasing cyber-attack sophistication and the limitations of traditional IT-centric cyber defence solutions. Traditional IT controls are rarely deployed on OT infrastructure, and where they are, some threats aren’t fully addressed. In our experiments, a shared critic implementation of Multi Agent Proximal Policy Optimisation (MAPPO) outperformed Independent Proximal Policy Optimisation (IPPO). MAPPO reached an optimal policy (episode outcome mean of 1) after 800K timesteps, whereas IPPO was only able to reach an episode outcome mean of 0.966 after one million timesteps. Hyperparameter tuning greatly improved training performance. Across one million timesteps the tuned hyperparameters reached an optimal policy whereas the default hyperparameters only managed to win sporadically, with most simulations resulting in a draw. We tested a real-world constraint, attack detection alert success, and found that when alert success probability is reduced to 0.75 or 0.9, the MARL defenders were still able to win in over 97.5% or 99.5% of episodes, respectively.
3. Ground-Based Optical Deep Pencil Beam Surveys
Authors: P. C. Boeshaar, V. E. Margoniner, the Deep Lens Survey Team
Categories: astro-ph
Published: 2002-08-09
arXiv: astro-ph/0208209v1
Abstract:
The initial results of the Deep Lens Survey (http://dls.bell-labs.com) to identify possible brown dwarfs and extremely metal poor red halo subdwarfs near the hydrogen burning limit are presented. Individual deep CCD high galactic latitude survey fields appear to offer a low probability of discovering field BD’s, but taken collectively offer an opportunity to begin addressing questions regarding the scale height and distribution of these objects. In all likelihood, the very depth of such surveys will greatly increase our knowledge of the coolest extreme halo objects, which currently are known in far fewer numbers than T dwarfs. Ultimately, the large volume surveyed by the Large Synoptic Survey Telescope will identify vast numbers of such objects, providing a more complete picture of their spatial distribution.
4. Shear-selected clusters from the Deep Lens Survey
Authors: V. E. Margoniner, the Deep Lens Survey Team
Categories: astro-ph
Published: 2003-03-17
arXiv: astro-ph/0303381v1
Abstract:
Weak gravitational lensing has the potential to select clusters independently of their baryon content, dynamical state, and star formation history. We present steps toward the first shear-selected sample of clusters, from the Deep Lens Survey (DLS), a deep BVRz’ imaging survey of 28 square degrees. Cluster redshifts are determined from photometric redshifts of members and from lensing tomography, and in some cases have been confirmed spectroscopically. DLS imaging data are also used to derive mass-to-light ratios, and upcoming Chandra and XMM time will yield X-ray luminosities and temperatures for a subsample of 12 clusters. Thus we can begin to address any baryon or luminous-matter bias which may be present in current optical and X-ray samples. When the DLS is complete, we expect to have a sample of ~ 100 shear-selected clusters from z ~ 0.2-1.
5. Learn to Accumulate Evidence from All Training Samples: Theory and Practice
Authors: Deep Pandey, Qi Yu
Categories: cs.LG, cs.AI, cs.CV
Published: 2023-06-19
arXiv: 2306.11113v2
Abstract:
Evidential deep learning, built upon belief theory and subjective logic, offers a principled and computationally efficient way to turn a deterministic neural network uncertainty-aware. The resultant evidential models can quantify fine-grained uncertainty using the learned evidence. To ensure theoretically sound evidential models, the evidence needs to be non-negative, which requires special activation functions for model training and inference. This constraint often leads to inferior predictive performance compared to standard softmax models, making it challenging to extend them to many large-scale datasets. To unveil the real cause of this undesired behavior, we theoretically investigate evidential models and identify a fundamental limitation that explains the inferior performance: existing evidential activation functions create zero evidence regions, which prevent the model to learn from training samples falling into such regions. A deeper analysis of evidential activation functions based on our theoretical underpinning inspires the design of a novel regularizer that effectively alleviates this fundamental limitation. Extensive experiments over many challenging real-world datasets and settings confirm our theoretical findings and demonstrate the effectiveness of our proposed approach.
6. The Modern Mathematics of Deep Learning
Authors: Julius Berner, Philipp Grohs, Gitta Kutyniok, Philipp Petersen
Categories: cs.LG, stat.ML
Published: 2021-05-09
arXiv: 2105.04026v2
Abstract:
We describe the new field of mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful optimization performance despite the non-convexity of the problem, understanding what features are learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects of an architecture affect the behavior of a learning task in which way. We present an overview of modern approaches that yield partial answers to these questions. For selected approaches, we describe the main ideas in more detail.
7. Deep Reinforcement Learning for Cyber Security
Authors: Thanh Thi Nguyen, Vijay Janapa Reddi
Categories: cs.CR, cs.AI, cs.LG, stat.ML
Published: 2019-06-13
arXiv: 1906.05799v4
Abstract:
The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed widely to address these issues. By incorporating deep learning into traditional RL, DRL is highly capable of solving complex, dynamic, and especially high-dimensional cyber defense problems. This paper presents a survey of DRL approaches developed for cyber security. We touch on different vital aspects, including DRL-based security methods for cyber-physical systems, autonomous intrusion detection techniques, and multiagent DRL-based game theory simulations for defense strategies against cyber attacks. Extensive discussions and future research directions on DRL-based cyber security are also given. We expect that this comprehensive review provides the foundations for and facilitates future studies on exploring the potential of emerging DRL to cope with increasingly complex cyber security problems.
8. Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification
Authors: Scott E. Coull, Christopher Gardner
Categories: cs.LG, cs.CR, stat.ML
Published: 2019-03-12
arXiv: 1903.04717v2
Abstract:
Feature engineering is one of the most costly aspects of developing effective machine learning models, and that cost is even greater in specialized problem domains, like malware classification, where expert skills are necessary to identify useful features. Recent work, however, has shown that deep learning models can be used to automatically learn feature representations directly from the raw, unstructured bytes of the binaries themselves. In this paper, we explore what these models are learning about malware. To do so, we examine the learned features at multiple levels of resolution, from individual byte embeddings to end-to-end analysis of the model. At each step, we connect these byte-oriented activations to their original semantics through parsing and disassembly of the binary to arrive at human-understandable features. Through our results, we identify several interesting features learned by the model and their connection to manually-derived features typically used by traditional machine learning models. Additionally, we explore the impact of training data volume and regularization on the quality of the learned features and the efficacy of the classifiers, revealing the somewhat paradoxical insight that better generalization does not necessarily result in better performance for byte-based malware classifiers.
9. Perspectives of Non-Expert Users on Cyber Security and Privacy: An Analysis of Online Discussions on Twitter
Authors: Nandita Pattnaik, Shujun Li, Jason R. C. Nurse
Categories: cs.CR, cs.LG
Published: 2022-06-05
arXiv: 2206.02156v2
Abstract:
Current research on users
perspectives of cyber security and privacy related to traditional and smart devices at home is very active, but the focus is often more on specific modern devices such as mobile and smart IoT devices in a home context. In addition, most were based on smaller-scale empirical studies such as online surveys and interviews. We endeavour to fill these research gaps by conducting a larger-scale study based on a real-world dataset of 413,985 tweets posted by non-expert users on Twitter in six months of three consecutive years (January and February in 2019, 2020 and 2021). Two machine learning-based classifiers were developed to identify the 413,985 tweets. We analysed this dataset to understand non-expert userscyber security and privacy perspectives, including the yearly trend and the impact of the COVID-19 pandemic. We applied topic modelling, sentiment analysis and qualitative analysis of selected tweets in the dataset, leading to various interesting findings. For instance, we observed a 54% increase in non-expert userstweets on cyber security and/or privacy related topics in 2021, compared to before the start of global COVID-19 lockdowns (January 2019 to February 2020). We also observed an increased level of help-seeking tweets during the COVID-19 pandemic. Our analysis revealed a diverse range of topics discussed by non-expert users across the three years, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, and security and privacy issues involving different stakeholders. Overall negative sentiment was observed across almost all topics non-expert users discussed on Twitter in all the three years. Our results confirm the multi-faceted nature of non-expert usersperspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on different facets of such perspectives.
10. Towards Automated Cyber Range Design: Characterizing and Matching Demands to Supplies
Authors: Ekzhin Ear, Jose L. C. Remy, Shouhuai Xu
Categories: cs.CR
Published: 2023-07-10
arXiv: 2307.04416v1
Abstract:
Cyber ranges mimic real-world cyber environments and are in high demand. Before building their own cyber ranges, organizations need to deeply understand what construction supplies are available to them. A fundamental supply is the cyber range architecture, which prompts an important research question: Which cyber range architecture is most appropriate for an organization’s requirements? To answer this question, we propose an innovative framework to specify cyber range requirements, characterize cyber range architectures (based on our analysis of 45 cyber range architectures), and match cyber range architectures to cyber range requirements.
11. Security Modelling for Cyber-Physical Systems: A Systematic Literature Review
Authors: Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar
Categories: cs.CR
Published: 2024-04-11
arXiv: 2404.07527v3
Abstract:
Cyber-physical systems are at the intersection of digital technology and engineering domains, rendering them high-value targets of sophisticated and well-funded cybersecurity threat actors. Prominent cybersecurity attacks on CPS have brought attention to the vulnerability of these systems and the inherent weaknesses of critical infrastructure reliant on them. Security modelling for CPS is an important mechanism to systematically identify and assess vulnerabilities, threats, and risks throughout system life cycles, and to ultimately ensure system resilience, safety, and reliability. This survey delves into state-of-the-art research on CPS security modelling, encompassing both threat and attack modelling. While these terms are sometimes used interchangeably, they are different concepts. This paper elaborates on the differences between threat and attack modelling, examining their implications for CPS security. We conducted a systematic search that yielded 449 papers, from which 32 were selected and categorised into three clusters: those focused on threat modelling methods, attack modelling methods, and literature reviews. Specifically, we sought to examine what security modelling methods exist today, and how they address real-world cybersecurity threats and CPS-specific attacker capabilities throughout the life cycle of CPS, which typically span longer durations compared to traditional IT systems. This paper also highlights several limitations in existing research, wherein security models adopt simplistic approaches that do not adequately consider the dynamic, multi-layer, multi-path, and multi-agent characteristics of real-world cyber-physical attacks.
12. Oriented object detection in optical remote sensing images using deep learning: a survey
Authors: Kun Wang, Zi Wang, Zhang Li, Ang Su, Xichao Teng, Erting Pan, Minhao Liu, Qifeng Yu
Categories: cs.CV
Published: 2023-02-21
arXiv: 2302.10473v6
Abstract:
Oriented object detection is a fundamental yet challenging task in remote sensing (RS), aiming to locate and classify objects with arbitrary orientations. Recent advancements in deep learning have significantly enhanced the capabilities of oriented object detection methods. Given the rapid development of this field, a comprehensive survey of the recent advances in oriented object detection is presented in this paper. Specifically, we begin by tracing the technical evolution from horizontal object detection to oriented object detection and highlighting the specific related challenges, including feature misalignment, spatial misalignment, oriented bounding box (OBB) regression problems, and common issues encountered in RS. Subsequently, we further categorize the existing methods into detection frameworks, OBB regression techniques, feature representation approaches, and solutions to common issues and provide an in-depth discussion of how these methods address the above challenges. In addition, we cover several publicly available datasets and evaluation protocols. Furthermore, we provide a comprehensive comparison and analysis involving the state-of-the-art methods. Toward the end of this paper, we identify several future directions for oriented object detection research.
13. Post-quantum Federated Learning: Secure And Scalable Threat Intelligence For Collaborative Cyber Defense
Authors: Prabhudarshi Nayak, Gogulakrishnan Thiyagarajan, Ritunsa Mishra, Vinay Bist
Categories: cs.CR
Published: 2026-03-08
arXiv: 2603.07726v1
Abstract:
Collaborative threat intelligence via federated learning (FL) faces critical risks from quantum computing, which can compromise classical encryption methods. This study proposes a quantum-secure FL framework using post-quantum cryptography (PQC) to protect cross-organizational data sharing. We expose vulnerabilities in traditional FL through simulated quantum attacks on RSA encrypted gradients and introduce a hybrid architecture integrating NIST-standardized algorithms CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication. Testing on APT attack datasets demonstrated 97.6% threat detection accuracy with minimal latency overhead (18.7%), validating real-world viability. A healthcare consortium case study confirmed secure ransomware indicator sharing without breaching privacy regulations. The work highlights the urgency of quantum ready defenses and provides technical guidelines for deploying PQC in FL systems, alongside policy recommendations for standardizing quantum resilience in threat-sharing networks.
14. The VIMOS VLT Deep Survey: Public release of 1599 redshifts to IAB<=24 across the Chandra Deep Field South
Authors: O. Le Fevre, G. Vettolani, S. Paltani, L. Tresse, G. Zamorani, V. Le Brun, C. Moreau, VIMOS VLT Deep Survey team
Categories: astro-ph
Published: 2004-03-26
arXiv: astro-ph/0403628v1
Abstract:
{This paper presents the VIMOS VLT Deep Survey around the Chandra Deep Field South (CDFS). We have measured 1599 new redshifts with VIMOS on the European Observatory Very Large Telescope - UT3, in an area 21x21.6 arcmin^2, including 784 redshifts in the Hubble Space Telescope - Advanced Camera for Surveys GOODS area. 30% of all objects with I_AB=24 have been observed independently of magnitude, indicating that the sample is purely magnitude limited. We have reached an unprecedented completeness level of 88% in terms of the ratio of secure measurements vs. observed objects, while 95% of all objects have a redshift measurement. A total of 1452 galaxies, 139 stars, 8 QSOs have a redshift identification, 141 of these being unsecure measurements. The redshift distribution down to I_AB=24 is peaked at a median redshift z=0.73, with a significant high redshift tail extending up to ~4. Several high density peaks in the distribution of galaxies are identified. In particular, the strong peak at z=0.735 contains more than 130 galaxies in a velocity range +/-2000 km/s distributed all across the transverse ~20 h^-1 Mpc of the survey. We are releasing all redshifts to the community, along with the cross identification with HST-ACS GOODS sources on the CENCOS database environment http://cencosw.oamp.fr.
15. Deep Learning and Computational Physics (Lecture Notes)
Authors: Deep Ray, Orazio Pinti, Assad A. Oberai
Categories: cs.LG, math-ph
Published: 2023-01-03
arXiv: 2301.00942v1
Abstract:
These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics. The main objective of these notes is to introduce a student who is familiar with concepts in linear algebra and partial differential equations to select topics in deep learning. These lecture notes exploit the strong connections between deep learning algorithms and the more conventional techniques of computational physics to achieve two goals. First, they use concepts from computational physics to develop an understanding of deep learning algorithms. Not surprisingly, many concepts in deep learning can be connected to similar concepts in computational physics, and one can utilize this connection to better understand these algorithms. Second, several novel deep learning algorithms can be used to solve challenging problems in computational physics. Thus, they offer someone who is interested in modeling a physical phenomena with a complementary set of tools.
16. Assembling a Cyber Range to Evaluate Artificial Intelligence / Machine Learning (AI/ML) Security Tools
Authors: Jeffrey A. Nichols, Kevin D. Spakes, Cory L. Watson, Robert A. Bridges
Categories: cs.CR
Published: 2022-01-20
arXiv: 2201.08473v1
Abstract:
In this case study, we describe the design and assembly of a cyber security testbed at Oak Ridge National Laboratory in Oak Ridge, TN, USA. The range is designed to provide agile reconfigurations to facilitate a wide variety of experiments for evaluations of cyber security tools – particularly those involving AI/ML. In particular, the testbed provides realistic test environments while permitting control and programmatic observations/data collection during the experiments. We have designed in the ability to repeat the evaluations, so additional tools can be evaluated and compared at a later time. The system is one that can be scaled up or down for experiment sizes. At the time of the conference we will have completed two full-scale, national, government challenges on this range. These challenges are evaluating the performance and operating costs for AI/ML-based cyber security tools for application into large, government-sized networks. These evaluations will be described as examples providing motivation and context for various design decisions and adaptations we have made. The first challenge measured end-point security tools against 100K file samples (benignware and malware) chosen across a range of file types. The second is an evaluation of network intrusion detection systems efficacy in identifying multi-step adversarial campaigns – involving reconnaissance, penetration and exploitations, lateral movement, etc. – with varying levels of covertness in a high-volume business network. The scale of each of these challenges requires automation systems to repeat, or simultaneously mirror identical the experiments for each ML tool under test. Providing an array of easy-to-difficult malicious activity for sussing out the true abilities of the AI/ML tools has been a particularly interesting and challenging aspect of designing and executing these challenge events.
17. A rich bounty of AGN in the 9 square degree Bootes survey: high-z obscured AGN and large-scale structure
Authors: R. C. Hickox, C. Jones, W. R. Forman, S. S. Murray, A. Kenter, M. Brodwin, the Chandra XBootes, NOAO Deep Wide-Field Survey, Spitzer IRAC Shallow Survey, AGES Teams
Categories: astro-ph
Published: 2006-11-21
arXiv: astro-ph/0611654v1
Abstract:
We use observations from the 9 square degree multiwavelength survey in Bootes to identify hundreds of obscured active galactic nuclei (AGN) with high redshifts (z > 0.7), luminosities (L_bol > 10^45 ergs/s), and moderate obscuring columns (N_H > 10^22 cm^-2), and to measure the clustering properties of X-ray AGN at z > 1. In the Bootes region, shallow (5 ks) Chandra X-ray observations have detected ~4,000 X-ray sources, and the same region has been mapped with deep optical imaging and by Spitzer IRAC, which detects ~300,000 point sources, of which ~30,000 have detections in all four IRAC bands, for which we can select AGN on the basis of their mid-IR colors. With the MMT/Hectospec we have obtained modest resolution optical spectra for about half the X-ray sources (out to z > 3) and ~20,000 galaxies (out to z = 0.7). With this multiwavelength data we select >400 AGN per square degree (compared to 12 per square degree from SDSS). Among a sample of IRAC-selected AGN we identify 641 candidate obscured objects based on their R band and IRAC luminosities. We use X-ray stacking techniques to verify that they are obscured AGN and measure their absorbing column densities. We also measure the three-dimensional two-point correlation function for X-ray selected AGN.
18. From Cyber Security Incident Management to Cyber Security Crisis Management in the European Union
Authors: Jukka Ruohonen, Kalle Rindell, Simone Busetti
Categories: cs.CR, cs.CY
Published: 2025-04-19
arXiv: 2504.14220v2
Abstract:
Incident management is a classical topic in cyber security. Recently, the European Union (EU) has started to consider also the relation between cyber security incidents and cyber security crises. These considerations and preparations, including those specified in the EU’s new cyber security laws, constitute the paper’s topic. According to an analysis of the laws and associated policy documents, (i) cyber security crises are equated in the EU to large-scale cyber security incidents that either exceed a handling capacity of a single member state or affect at least two member states. For this and other purposes, (ii) the new laws substantially increase mandatory reporting about cyber security incidents, including but not limited to the large-scale incidents. Despite the laws and new governance bodies established by them, however, (iii) the working of actual cyber security crisis management remains unclear particularly at the EU-level. With these policy research results, the paper advances the domain of cyber security incident management research by elaborating how European law perceives cyber security crises and their relation to cyber security incidents, paving the way for many relevant further research topics with practical relevance, whether theoretical, conceptual, or empirical.
19. Six Potential Game-Changers in Cyber Security: Towards Priorities in Cyber Science and Engineering
Authors: Alexander Kott, Ananthram Swami, Patrick McDaniel
Categories: cs.CR
Published: 2015-11-02
arXiv: 1511.00509v1
Abstract:
The fields of study encompassed by cyber science and engineering are broad and poorly defined at this time. As national governments and research communities increase their recognition of the importance, urgency and technical richness of these disciplines, a question of priorities arises: what specific sub-areas of research should be the foci of attention and funding? In this paper we point to an approach to answering this question. We explore results of a recent workshop that postulated possible game-changers or disruptive changes that might occur in cyber security within the next 15 years. We suggest that such game-changers may be useful in focusing attention of research communities on high-priority topics. Indeed, if a drastic, important change is likely to occur, should we not focus our research efforts on the nature and ramifications of the phenomena pertaining to that change? We illustrate each of the game-changers examples of related current research, and then offer recommendations for advancement of cyber science and engineering with respect to each of the six game-changers.
20. Identifying Security-Critical Cyber-Physical Components in Industrial Control Systems
Authors: Martín Barrère, Chris Hankin, Nicolas Nicolau, Demetrios G. Eliades, Thomas Parisini
Categories: cs.CR, cs.NI, eess.SY
Published: 2019-05-12
arXiv: 1905.04796v1
Abstract:
In recent years, Industrial Control Systems (ICS) have become an appealing target for cyber attacks, having massive destructive consequences. Security metrics are therefore essential to assess their security posture. In this paper, we present a novel ICS security metric based on AND/OR graphs that represent cyber-physical dependencies among network components. Our metric is able to efficiently identify sets of critical cyber-physical components, with minimal cost for an attacker, such that if compromised, the system would enter into a non-operational state. We address this problem by efficiently transforming the input AND/OR graph-based model into a weighted logical formula that is then used to build and solve a Weighted Partial MAX-SAT problem. Our tool, META4ICS, leverages state-of-the-art techniques from the field of logical satisfiability optimisation in order to achieve efficient computation times. Our experimental results indicate that the proposed security metric can efficiently scale to networks with thousands of nodes and be computed in seconds. In addition, we present a case study where we have used our system to analyse the security posture of a realistic water transport network. We discuss our findings on the plant as well as further security applications of our metric.
21. Traffic-aware Patching for Cyber Security in Mobile IoT
Authors: Shin-Ming Cheng, Pin-Yu Chen, Ching-Chao Lin, Hsu-Chun Hsiao
Categories: cs.CR
Published: 2017-03-15
arXiv: 1703.05400v1
Abstract:
The various types of communication technologies and mobility features in Internet of Things (IoT) on the one hand enable fruitful and attractive applications, but on the other hand facilitates malware propagation, thereby raising new challenges on handling IoT-empowered malware for cyber security. Comparing with the malware propagation control scheme in traditional wireless networks where nodes can be directly repaired and secured, in IoT, compromised end devices are difficult to be patched. Alternatively, blocking malware via patching intermediate nodes turns out to be a more feasible and practical solution. Specifically, patching intermediate nodes can effectively prevent the proliferation of malware propagation by securing infrastructure links and limiting malware propagation to local device-to-device dissemination. This article proposes a novel traffic-aware patching scheme to select important intermediate nodes to patch, which applies to the IoT system with limited patching resources and response time constraint. Experiments on real-world trace datasets in IoT networks are conducted to demonstrate the advantage of the proposed traffic-aware patching scheme in alleviating malware propagation.
22. Learning-Assisted Secure End-to-End Network Slicing for Cyber-Physical Systems
Authors: Qiang Liu, Tao Han, Nirwan Ansari
Categories: cs.NI
Published: 2019-10-29
arXiv: 1910.13537v1
Abstract:
There is a pressing need to interconnect physical systems such as power grid and vehicles for efficient management and safe operations. Owing to the diverse features of physical systems, there is hardly a one-size-fits-all networking solution for developing cyber-physical systems. Network slicing is a promising technology that allows network operators to create multiple virtual networks on top of a shared network infrastructure. These virtual networks can be tailored to meet the requirements of different cyber-physical systems. However, it is challenging to design secure network slicing solutions that can efficiently create end-to-end network slices for diverse cyber-physical systems. In this article, we discuss the challenges and security issues of network slicing, study learning-assisted network slicing solutions, and analyze their performance under the denial-of-service attack. We also present a design and implementation of a small-scale testbed for evaluating the network slicing solutions.
23. A short review on Applications of Deep learning for Cyber security
Authors: Mohammed Harun Babu R, Vinayakumar R, Soman KP
Categories: cs.CR, cs.AI, cs.LG
Published: 2018-12-15
arXiv: 1812.06292v2
Abstract:
Deep learning is an advanced model of traditional machine learning. This has the capability to extract optimal feature representation from raw input samples. This has been applied towards various use cases in cyber security such as intrusion detection, malware classification, android malware detection, spam and phishing detection and binary analysis. This paper outlines the survey of all the works related to deep learning based solutions for various cyber security use cases. Keywords: Deep learning, intrusion detection, malware detection, Android malware detection, spam & phishing detection, traffic analysis, binary analysis.
24. Comparative Analysis of AutoML and BiLSTM Models for Cyberbullying Detection on Indonesian Instagram Comments
Authors: Raihana Adelia Putri, Aisyah Musfirah, Anggi Puspita Ningrum, Luluk Muthoharoh, Ardika Satria, Martin Clinton Tosima Manullang
Categories: cs.CL
Published: 2026-04-29
arXiv: 2604.26229v1
Abstract:
This study compares machine learning and deep learning approaches for cyberbullying detection in Indonesian-language Instagram comments. Using a balanced dataset of 650 comments labeled as Bullying and Non-Bullying, the study evaluates Naive Bayes, Logistic Regression, and Support Vector Machine with TF-IDF features, as well as BiLSTM and BiLSTM with Bahdanau Attention. A preprocessing pipeline tailored to informal Indonesian text is applied, including slang normalization, stopword removal, and stemming. The results show that Logistic Regression performs best among the machine learning models, while BiLSTM with Attention achieves the strongest overall deep learning performance. The findings highlight the value of domain-specific preprocessing and show that although deep learning captures contextual patterns more effectively, machine learning remains a competitive option for resource-constrained deployments.
25. Monodense Deep Neural Model for Determining Item Price Elasticity
Authors: Lakshya Garg, Sai Yaswanth, Deep Narayan Mishra, Karthik Kumaran, Anupriya Sharma, Mayank Uniyal
Categories: cs.LG, cs.AI
Published: 2026-03-31
arXiv: 2603.29261v1
Abstract:
Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue management. Sectors such as store retail, e-commerce, and consumer goods rely on elasticity information derived from historical sales and pricing data. This elasticity provides an understanding of purchasing behavior across different items, consumer discount sensitivity, and demand elastic departments. This information is particularly valuable for competitive markets and resource-constrained businesses decision making which aims to maximize profitability and market share. Price elasticity also uncovers historical shifts in consumer responsiveness over time. In this paper, we model item-level price elasticity using large-scale transactional datasets, by proposing a novel elasticity estimation framework which has the capability to work in an absence of treatment control setting. We test this framework by using Machine learning based algorithms listed below, including our newly proposed Monodense deep neural network. (1) Monodense-DL network – Hybrid neural network architecture combining embedding, dense, and Monodense layers (2) DML – Double machine learning setting using regression models (3) LGBM – Light Gradient Boosting Model We evaluate our model on multi-category retail data spanning millions of transactions using a back testing framework. Experimental results demonstrate the superiority of our proposed neural network model within the framework compared to other prevalent ML based methods listed above.
26. Audit Analysis Models, Security Frameworks and Their Relevance for VoIP
Authors: Oscar Gavilanez, Franklin Gavilanez, Glen Rodriguez
Categories: cs.CR
Published: 2017-04-08
arXiv: 1704.02440v1
Abstract:
Voice over IP (VoIP) is the transmission of voice and multimedia content over Internet Protocol (IP) networks, this paper reviews models, frameworks and auditing standards proposed to this date to manage VoIP security through a literature review, with descriptions of both the historical and philosophical evolution reflecting an adequate knowledge of related research. Three research questions are raised here: RQ1. What are the requirements to be met by a model of security audit in VoIP systems to achieve their goals? RQ2. Today, are there additional attacks that previous works have not considered? RQ3. Which security requirements in the VoIP systems are covered (and which are not covered) by security frameworks? After some discussion about VoIP Protocols, Attacks on VoIP, Information Technology (IT) audit, IT security audits, Frameworks and auditing standards, we present a unified view of VoIP Security Requirements; as well as considering the contributions and disadvantages of frameworks and auditing standards toward achieving those requirements through a comparative evaluation. It was determined that there is no security framework which considers social engineering attacks in spite of being an important aspect to consider in security management VoIP; also there is no specific framework that covers all categories of security requirements for VoIP system, therefore, a more extensive model is needed.
27. Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning
Authors: Etienne Salimbeni, Nina Mainusch, Dario Pasquini
Categories: cs.CR
Published: 2023-06-14
arXiv: 2306.08638v2
Abstract:
In this work, we investigate the effectiveness of deep-learning-based password guessing models for targeted attacks on human-chosen passwords. In recent years, service providers have increased the level of security of users’passwords. This is done by requiring more complex password generation patterns and by using computationally expensive hash functions. For the attackers this means a reduced number of available guessing attempts, which introduces the necessity to target their guess by exploiting a victim’s publicly available information. In this work, we introduce a context-aware password guessing model that better capture attackers’behavior. We demonstrate that knowing a victim’s email address is already critical in compromising the associated password and provide an in-depth analysis of the relationship between them. We also show the potential of such models to identify clusters of users based on their password generation behaviour, which can spot fake profiles and populations more vulnerable to context-aware guesses. The code is publicly available at https://github.com/spring-epfl/DCM_sp
28. A multitask deep learning model for real-time deployment in embedded systems
Authors: Miquel Martí, Atsuto Maki
Categories: cs.CV, cs.LG
Published: 2017-10-31
arXiv: 1711.00146v1
Abstract:
We propose an approach to Multitask Learning (MTL) to make deep learning models faster and lighter for applications in which multiple tasks need to be solved simultaneously, which is particularly useful in embedded, real-time systems. We develop a multitask model for both Object Detection and Semantic Segmentation and analyze the challenges that appear during its training. Our multitask network is 1.6x faster, lighter and uses less memory than deploying the single-task models in parallel. We conclude that MTL has the potential to give superior performance in exchange of a more complex training process that introduces challenges not present in single-task models.
29. Cyber Protection Applications of Quantum Computing: A Review
Authors: Ummar Ahmed, Tuomo Sipola, Jari Hautamäki
Categories: cs.CR, cs.ET
Published: 2024-06-19
arXiv: 2406.13259v3
Abstract:
Quantum computing is a cutting-edge field of information technology that harnesses the principles of quantum mechanics to perform computations. It has major implications for the cyber security industry. Existing cyber protection applications are working well, but there are still challenges and vulnerabilities in computer networks. Sometimes data and privacy are also compromised. These complications lead to research questions asking what kind of cyber protection applications of quantum computing are there and what potential methods or techniques can be used for cyber protection? These questions will reveal how much power quantum computing has and to what extent it can outperform the conventional computing systems. This scoping review was conducted by considering 815 papers. It showed the possibilities that can be achievedif quantum technologies are implemented in cyber environments. This scoping review discusses various domains such as algorithms and applications, bioinformatics, cloud and edge computing, the organization of complex systems, application areas focused on security and threats, and the broader quantum computing ecosystem. In each of these areas, there is significant scope for quantum computing to be implemented and to revolutionize the working environment. Numerous quantum computing applications for cyber protection and a number of techniques to protect our data and privacy were identified. The results are not limited to network security but also include data security. This paper also discusses societal aspects, e.g., the applications of quantum computing in the social sciences. This scoping review discusses how to enhance the efficiency and security of quantum computing in various cyber security domains. Additionally, it encourages the reader to think about what kind of techniques and methods can be deployed to secure the cyber world.
30. A Brief Survey of Deep Reinforcement Learning
Authors: Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath
Categories: cs.LG, cs.AI, cs.CV, stat.ML
Published: 2017-08-19
arXiv: 1708.05866v2
Abstract:
Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep $Q$-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.
31. Applying Action Masking and Curriculum Learning Techniques to Improve Data Efficiency and Overall Performance in Operational Technology Cyber Security using Reinforcement Learning
Authors: Alec Wilson, William Holmes, Ryan Menzies, Kez Smithson Whitehead
Categories: cs.CR, cs.LG
Published: 2024-09-13
arXiv: 2409.10563v1
Abstract:
In previous work, the IPMSRL environment (Integrated Platform Management System Reinforcement Learning environment) was developed with the aim of training defensive RL agents in a simulator representing a subset of an IPMS on a maritime vessel under a cyber-attack. This paper extends the use of IPMSRL to enhance realism including the additional dynamics of false positive alerts and alert delay. Applying curriculum learning, in the most difficult environment tested, resulted in an episode reward mean increasing from a baseline result of -2.791 to -0.569. Applying action masking, in the most difficult environment tested, resulted in an episode reward mean increasing from a baseline result of -2.791 to -0.743. Importantly, this level of performance was reached in less than 1 million timesteps, which was far more data efficient than vanilla PPO which reached a lower level of performance after 2.5 million timesteps. The training method which resulted in the highest level of performance observed in this paper was a combination of the application of curriculum learning and action masking, with a mean episode reward of 0.137. This paper also introduces a basic hardcoded defensive agent encoding a representation of cyber security best practice, which provides context to the episode reward mean figures reached by the RL agents. The hardcoded agent managed an episode reward mean of -1.895. This paper therefore shows that applications of curriculum learning and action masking, both independently and in tandem, present a way to overcome the complex real-world dynamics that are present in operational technology cyber security threat remediation.
32. Deep learning observables in computational fluid dynamics
Authors: Kjetil O. Lye, Siddhartha Mishra, Deep Ray
Categories: physics.comp-ph, cs.LG, math.NA, physics.flu-dyn, stat.ML
Published: 2019-03-07
arXiv: 1903.03040v2
Abstract:
Many large scale problems in computational fluid dynamics such as uncertainty quantification, Bayesian inversion, data assimilation and PDE constrained optimization are considered very challenging computationally as they require a large number of expensive (forward) numerical solutions of the corresponding PDEs. We propose a machine learning algorithm, based on deep artificial neural networks, that predicts the underlying \emph{input parameters to observable} map from a few training samples (computed realizations of this map). By a judicious combination of theoretical arguments and empirical observations, we find suitable network architectures and training hyperparameters that result in robust and efficient neural network approximations of the parameters to observable map. Numerical experiments are presented to demonstrate low prediction errors for the trained network networks, even when the network has been trained with a few samples, at a computational cost which is several orders of magnitude lower than the underlying PDE solver. Moreover, we combine the proposed deep learning algorithm with Monte Carlo (MC) and Quasi-Monte Carlo (QMC) methods to efficiently compute uncertainty propagation for nonlinear PDEs. Under the assumption that the underlying neural networks generalize well, we prove that the deep learning MC and QMC algorithms are guaranteed to be faster than the baseline (quasi-) Monte Carlo methods. Numerical experiments demonstrating one to two orders of magnitude speed up over baseline QMC and MC algorithms, for the intricate problem of computing probability distributions of the observable, are also presented.
33. DILIE: Deep Internal Learning for Image Enhancement
Authors: Indra Deep Mastan, Shanmuganathan Raman
Categories: cs.CV
Published: 2020-12-11
arXiv: 2012.06469v1
Abstract:
We consider the generic deep image enhancement problem where an input image is transformed into a perceptually better-looking image. Recent methods for image enhancement consider the problem by performing style transfer and image restoration. The methods mostly fall into two categories: training data-based and training data-independent (deep internal learning methods). We perform image enhancement in the deep internal learning framework. Our Deep Internal Learning for Image Enhancement framework enhances content features and style features and uses contextual content loss for preserving image context in the enhanced image. We show results on both hazy and noisy image enhancement. To validate the results, we use structure similarity and perceptual error, which is efficient in measuring the unrealistic deformation present in the images. We show that the proposed framework outperforms the relevant state-of-the-art works for image enhancement.
34. Ontologies for Network Security and Future Challenges
Authors: Danny Velasco, Glen Rodriguez
Categories: cs.CR
Published: 2017-04-08
arXiv: 1704.02441v1
Abstract:
Efforts have been recently made to construct ontologies for network security. The proposed ontologies are related to specific aspects of network security. Therefore, it is necessary to identify the specific aspects covered by existing ontologies for network security. A review and analysis of the principal issues, challenges, and the extent of progress related to distinct ontologies was performed. Each example was classified according to the typology of the ontologies for network security. Some aspects include identifying threats, intrusion detection systems (IDS), alerts, attacks, countermeasures, security policies, and network management tools. The research performed here proposes the use of three stages: 1. Inputs; 2. Processing; and 3. Outputs. The analysis resulted in the introduction of new challenges and aspects that may be used as the basis for future research. One major issue that was discovered identifies the need to develop new ontologies that relate to distinct aspects of network security, thereby facilitating management tasks.
35. Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation
Authors: Deep Shankar Pandey, Hyomin Choi, Qi Yu
Categories: cs.LG, cs.AI
Published: 2025-12-27
arXiv: 2512.23753v1
Abstract:
Evidential deep learning (EDL) models, based on Subjective Logic, introduce a principled and computationally efficient way to make deterministic neural networks uncertainty-aware. The resulting evidential models can quantify fine-grained uncertainty using learned evidence. However, the Subjective-Logic framework constrains evidence to be non-negative, requiring specific activation functions whose geometric properties can induce activation-dependent learning-freeze behavior: a regime where gradients become extremely small for samples mapped into low-evidence regions. We theoretically characterize this behavior and analyze how different evidential activations influence learning dynamics. Building on this analysis, we design a general family of activation functions and corresponding evidential regularizers that provide an alternative pathway for consistent evidence updates across activation regimes. Extensive experiments on four benchmark classification problems (MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet), two few-shot classification problems, and blind face restoration problem empirically validate the developed theory and demonstrate the effectiveness of the proposed generalized regularized evidential models.
36. Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models
Authors: Haoyue Zhang, Jennifer S. Polson, Eric J. Yang, Kambiz Nael, William Speier, Corey W. Arnold
Categories: eess.IV, cs.CV
Published: 2023-02-08
arXiv: 2302.04143v2
Abstract:
For acute ischemic stroke (AIS) patients with large vessel occlusions, clinicians must decide if the benefit of mechanical thrombectomy (MTB) outweighs the risks and potential complications following an invasive procedure. Pre-treatment computed tomography (CT) and angiography (CTA) are widely used to characterize occlusions in the brain vasculature. If a patient is deemed eligible, a modified treatment in cerebral ischemia (mTICI) score will be used to grade how well blood flow is reestablished throughout and following the MTB procedure. An estimation of the likelihood of successful recanalization can support treatment decision-making. In this study, we proposed a fully automated prediction of a patient’s recanalization score using pre-treatment CT and CTA imaging. We designed a spatial cross attention network (SCANet) that utilizes vision transformers to localize to pertinent slices and brain regions. Our top model achieved an average cross-validated ROC-AUC of 77.33 $\pm$ 3.9%. This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.
37. Evaluating Explanation Methods for Deep Learning in Security
Authors: Alexander Warnecke, Daniel Arp, Christian Wressnegger, Konrad Rieck
Categories: cs.LG, cs.CR, stat.ML
Published: 2019-06-05
arXiv: 1906.02108v4
Abstract:
Deep learning is increasingly used as a building block of security systems. Unfortunately, neural networks are hard to interpret and typically opaque to the practitioner. The machine learning community has started to address this problem by developing methods for explaining the predictions of neural networks. While several of these approaches have been successfully applied in the area of computer vision, their application in security has received little attention so far. It is an open question which explanation methods are appropriate for computer security and what requirements they need to satisfy. In this paper, we introduce criteria for comparing and evaluating explanation methods in the context of computer security. These cover general properties, such as the accuracy of explanations, as well as security-focused aspects, such as the completeness, efficiency, and robustness. Based on our criteria, we investigate six popular explanation methods and assess their utility in security systems for malware detection and vulnerability discovery. We observe significant differences between the methods and build on these to derive general recommendations for selecting and applying explanation methods in computer security.
38. Global Pandemics Influence on Cyber Security and Cyber Crimes
Authors: Somya Khatri, Aswani Kumar Cherukuri, Firuz Kamalov
Categories: cs.CR, cs.CY
Published: 2023-02-24
arXiv: 2302.12462v1
Abstract:
COVID-19 has caused widespread damage across many areas of life and has made humans more dependent on the internet and technology making us realize the importance of secure remote working environments. While social separation is encouraged during moments of lockdown, online infrastructure has become the central focus for communication, commerce, working, and learning, creating a new challenge and trend for companies to adopt new methods and operating models. The cases of cyber-attacks increased, and fraudsters and cybercriminals took use of this to intensify their illegal activities by taking advantage of remote workers’ vulnerabilities and the public’s interest in information about the coronavirus. This paper examines the different types of security threats and cyber crimes that people faced in the pandemic time and the need for a safe and secure cyber infrastructure. This paper attempts to analyze the security implications of the issues.
39. Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense
Authors: Sayak Mukherjee, Samrat Chatterjee, Emilie Purvine, Ted Fujimoto, Tegan Emerson
Categories: cs.LG, cs.AI, cs.MA
Published: 2025-11-20
arXiv: 2511.16483v1
Abstract:
Designing rewards for autonomous cyber attack and defense learning agents in a complex, dynamic environment is a challenging task for subject matter experts. We propose a large language model (LLM)-based reward design approach to generate autonomous cyber defense policies in a deep reinforcement learning (DRL)-driven experimental simulation environment. Multiple attack and defense agent personas were crafted, reflecting heterogeneity in agent actions, to generate LLM-guided reward designs where the LLM was first provided with contextual cyber simulation environment information. These reward structures were then utilized within a DRL-driven attack-defense simulation environment to learn an ensemble of cyber defense policies. Our results suggest that LLM-guided reward designs can lead to effective defense strategies against diverse adversarial behaviors.
40. Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
Authors: Ihai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach
Categories: cs.LG, cs.CR, stat.ML
Published: 2020-07-05
arXiv: 2007.02407v3
Abstract:
In recent years machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the application of machine learning, especially in non-stationary, adversarial environments, such as the cyber security domain, where actual adversaries (e.g., malware developers) exist. This paper comprehensively summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques and illuminates the risks they pose. First, the adversarial attack methods are characterized based on their stage of occurrence, and the attacker’s goals and capabilities. Then, we categorize the applications of adversarial attack and defense methods in the cyber security domain. Finally, we highlight some characteristics identified in recent research and discuss the impact of recent advancements in other adversarial learning domains on future research directions in the cyber security domain. This paper is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain, map them in a unified taxonomy, and use the taxonomy to highlight future research directions.
41. DeepCFL: Deep Contextual Features Learning from a Single Image
Authors: Indra Deep Mastan, Shanmuganathan Raman
Categories: cs.CV
Published: 2020-11-07
arXiv: 2011.03712v1
Abstract:
Recently, there is a vast interest in developing image feature learning methods that are independent of the training data, such as deep image prior, InGAN, SinGAN, and DCIL. These methods are unsupervised and are used to perform low-level vision tasks such as image restoration, image editing, and image synthesis. In this work, we proposed a new training data-independent framework, called Deep Contextual Features Learning (DeepCFL), to perform image synthesis and image restoration based on the semantics of the input image. The contextual features are simply the high dimensional vectors representing the semantics of the given image. DeepCFL is a single image GAN framework that learns the distribution of the context vectors from the input image. We show the performance of contextual learning in various challenging scenarios: outpainting, inpainting, and restoration of randomly removed pixels. DeepCFL is applicable when the input source image and the generated target image are not aligned. We illustrate image synthesis using DeepCFL for the task of image resizing.
42. Cyber Security Awareness Campaigns: Why do they fail to change behaviour?
Authors: Maria Bada, Angela M. Sasse, Jason R. C. Nurse
Categories: cs.CR, cs.CY, cs.HC
Published: 2019-01-09
arXiv: 1901.02672v1
Abstract:
The present paper focuses on Cyber Security Awareness Campaigns, and aims to identify key factors regarding security which may lead them to failing to appropriately change people’s behaviour. Past and current efforts to improve information-security practices and promote a sustainable society have not had the desired impact. It is important therefore to critically reflect on the challenges involved in improving information-security behaviours for citizens, consumers and employees. In particular, our work considers these challenges from a Psychology perspective, as we believe that understanding how people perceive risks is critical to creating effective awareness campaigns. Changing behaviour requires more than providing information about risks and reactive behaviours - firstly, people must be able to understand and apply the advice, and secondly, they must be motivated and willing to do so - and the latter requires changes to attitudes and intentions. These antecedents of behaviour change are identified in several psychological models of behaviour. We review the suitability of persuasion techniques, including the widely used ‘fear appeals’. From this range of literature, we extract essential components for an awareness campaign as well as factors which can lead to a campaign’s success or failure. Finally, we present examples of existing awareness campaigns in different cultures (the UK and Africa) and reflect on these.
43. PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis
Authors: Raghavendra Selvan, Bob Pepin, Christian Igel, Gabrielle Samuel, Erik B Dam
Categories: cs.LG, cs.AI, stat.ML
Published: 2024-03-19
arXiv: 2403.12562v2
Abstract:
The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry barrier to researchers and practitioners with limited access to resources at such scale, particularly in the Global South. In this work, we take a comprehensive look at the landscape of existing DL models for medical image analysis tasks and demonstrate their usefulness in settings where resources are limited. To account for the resource consumption of DL models, we introduce a novel measure to estimate the performance per resource unit, which we call the PePR score. Using a diverse family of 131 unique DL architectures (spanning 1M to 130M trainable parameters) and three medical image datasets, we capture trends about the performance-resource trade-offs. In applications like medical image analysis, we argue that small-scale, specialized models are better than striving for large-scale models. Furthermore, we show that using existing pretrained models that are fine-tuned on new data can significantly reduce the computational resources and data required compared to training models from scratch. We hope this work will encourage the community to focus on improving AI equity by developing methods and models with smaller resource footprints.
44. Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography
Authors: Pingzhi Li, Tianlong Chen, Junyu Liu
Categories: quant-ph, cs.AI, cs.CR, cs.LG
Published: 2024-09-06
arXiv: 2409.04637v1
Abstract:
Federated learning (FL) has become one of the standard approaches for deploying machine learning models on edge devices, where private training data are distributed across clients, and a shared model is learned by aggregating locally computed updates from each client. While this paradigm enhances communication efficiency by only requiring updates at the end of each training epoch, the transmitted model updates remain vulnerable to malicious tampering, posing risks to the integrity of the global model. Although current digital signature algorithms can protect these communicated model updates, they fail to ensure quantum security in the era of large-scale quantum computing. Fortunately, various post-quantum cryptography algorithms have been developed to address this vulnerability, especially the three NIST-standardized algorithms - Dilithium, FALCON, and SPHINCS+. In this work, we empirically investigate the impact of these three NIST-standardized PQC algorithms for digital signatures within the FL procedure, covering a wide range of models, tasks, and FL settings. Our results indicate that Dilithium stands out as the most efficient PQC algorithm for digital signature in federated learning. Additionally, we offer an in-depth discussion of the implications of our findings and potential directions for future research.
45. A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection
Authors: Rahul Kale, Zhi Lu, Kar Wai Fok, Vrizlynn L. L. Thing
Categories: cs.CR, cs.AI, cs.LG
Published: 2022-12-02
arXiv: 2212.00966v1
Abstract:
Cyber intrusion attacks that compromise the users’ critical and sensitive data are escalating in volume and intensity, especially with the growing connections between our daily life and the Internet. The large volume and high complexity of such intrusion attacks have impeded the effectiveness of most traditional defence techniques. While at the same time, the remarkable performance of the machine learning methods, especially deep learning, in computer vision, had garnered research interests from the cyber security community to further enhance and automate intrusion detections. However, the expensive data labeling and limitation of anomalous data make it challenging to train an intrusion detector in a fully supervised manner. Therefore, intrusion detection based on unsupervised anomaly detection is an important feature too. In this paper, we propose a three-stage deep learning anomaly detection based network intrusion attack detection framework. The framework comprises an integration of unsupervised (K-means clustering), semi-supervised (GANomaly) and supervised learning (CNN) algorithms. We then evaluated and showed the performance of our implemented framework on three benchmark datasets: NSL-KDD, CIC-IDS2018, and TON_IoT.
46. The Everyday Security of Living with Conflict
Authors: Jessica McClearn, Reem Talhouk, Rikke Bjerg Jensen
Categories: cs.CR
Published: 2025-06-11
arXiv: 2506.09580v1
Abstract:
When
cyber' is used as a prefix, attention is typically drawn to the technological and spectacular aspects of war and conflict -- and, by extension, security. We offer a different approach to engaging with and understanding security in such contexts, by foregrounding the everyday -- mundane -- experiences of security within communities living with and fleeing from war. We do so through three vignettes from our field research in Colombia, Lebanon and Sweden, respectively, and by highlighting the significance of ethnography for security research with communities living in regions afflicted by war. We conclude by setting out a call to action for security researchers and practitioners to consider such lived experiences in the design of security technology that aims to cater to the needs of communities inglobal conflict and disaster regions’.
47. Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors
Authors: Dhruv V Patel, Deep Ray, Assad A Oberai
Categories: stat.ML, cs.LG
Published: 2021-07-06
arXiv: 2107.02926v2
Abstract:
Inverse problems are ubiquitous in nature, arising in almost all areas of science and engineering ranging from geophysics and climate science to astrophysics and biomechanics. One of the central challenges in solving inverse problems is tackling their ill-posed nature. Bayesian inference provides a principled approach for overcoming this by formulating the inverse problem into a statistical framework. However, it is challenging to apply when inferring fields that have discrete representations of large dimensions (the so-called “curse of dimensionality”) and/or when prior information is available only in the form of previously acquired solutions. In this work, we present a novel method for efficient and accurate Bayesian inversion using deep generative models. Specifically, we demonstrate how using the approximate distribution learned by a Generative Adversarial Network (GAN) as a prior in a Bayesian update and reformulating the resulting inference problem in the low-dimensional latent space of the GAN, enables the efficient solution of large-scale Bayesian inverse problems. Our statistical framework preserves the underlying physics and is demonstrated to yield accurate results with reliable uncertainty estimates, even in the absence of information about underlying noise model, which is a significant challenge with many existing methods. We demonstrate the effectiveness of proposed method on a variety of inverse problems which include both synthetic as well as experimentally observed data.
48. A Survey on Concept Factorization: From Shallow to Deep Representation Learning
Authors: Zhao Zhang, Yan Zhang, Mingliang Xu, Li Zhang, Yi Yang, Shuicheng Yan
Categories: cs.LG, cs.CV, stat.ML
Published: 2020-07-31
arXiv: 2007.15840v3
Abstract:
The quality of learned features by representation learning determines the performance of learning algorithms and the related application tasks (such as high-dimensional data clustering). As a relatively new paradigm for representation learning, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade. Lots of effective CF based methods have been proposed based on different perspectives and properties, but note that it still remains not easy to grasp the essential connections and figure out the underlying explanatory factors from exiting studies. In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing the current methods. Specifically, we first re-view the root CF method, and then explore the advancement of CF-based representation learning ranging from shallow to deep/multilayer cases. We also introduce the potential application areas of CF-based methods. Finally, we point out some future directions for studying the CF-based representation learning. Overall, this survey provides an insightful overview of both theoretical basis and current developments in the field of CF, which can also help the interested researchers to understand the current trends of CF and find the most appropriate CF techniques to deal with particular applications.
49. Statistical Reliability of 10 Years of Cyber Security User Studies (Extended Version)
Authors: Thomas Groß
Categories: cs.HC, cs.CR
Published: 2020-10-05
arXiv: 2010.02117v1
Abstract:
Background. In recent years, cyber security security user studies have been appraised in meta-research, mostly focusing on the completeness of their statistical inferences and the fidelity of their statistical reporting. However, estimates of the field’s distribution of statistical power and its publication bias have not received much attention. Aim. In this study, we aim to estimate the effect sizes and their standard errors present as well as the implications on statistical power and publication bias. Method. We built upon a published systematic literature review of $146$ user studies in cyber security (2006–2016). We took into account $431$ statistical inferences including $t$-, $χ^2$-, $r$-, one-way $F$-tests, and $Z$-tests. In addition, we coded the corresponding total sample sizes, group sizes and test families. Given these data, we established the observed effect sizes and evaluated the overall publication bias. We further computed the statistical power vis-{à}-vis of parametrized population thresholds to gain unbiased estimates of the power distribution. Results. We obtained a distribution of effect sizes and their conversion into comparable log odds ratios together with their standard errors. We, further, gained funnel-plot estimates of the publication bias present in the sample as well as insights into the power distribution and its consequences. Conclusions. Through the lenses of power and publication bias, we shed light on the statistical reliability of the studies in the field. The upshot of this introspection is practical recommendations on conducting and evaluating studies to advance the field.
50. Towards Assessing Critical Infrastructures Cyber-Security Culture During Covid-19 Crisis: A Tailor-Made Survey
Authors: Anna Georgiadou, Spiros Mouzakitis, Dimitrios Askounis
Categories: cs.CR
Published: 2020-12-26
arXiv: 2012.13718v1
Abstract:
This paper outlines the design and development of a survey targeting the cyber-security culture assessment of critical infrastructures during the COVID-19 crisis, when living routine was seriously disturbed and working reality fundamentally affected. Its foundations lie on a security culture framework consisted of 10 different security dimensions analysed into 52 domains examined under two different pillars: organizational and individual. In this paper, a detailed questionnaire building analysis is being presented while revealing the aims, goals and expected outcomes of each question. It concludes with the survey implementation and delivery plan following a number of pre-survey stages each serving a specific methodological purpose.