cyber-xai-intrusion
Query: explainable AI intrusion detection
Results: 50
Date: 2026-07-07T18:53:10.018Z
1. Explainable AI for Blind and Low-Vision Users: Navigating Trust, Modality, and Interpretability in the Agentic Era
Authors: Abu Noman Md Sakib, Protik Dey, Zijie Zhang, Taslima Akter
Categories: cs.HC, cs.AI, cs.ET
Published: 2026-03-31
arXiv: 2604.00187v2
Abstract:
Explainable Artificial Intelligence (XAI) is critical for ensuring trust and accountability, yet its development remains predominantly visual. For blind and low-vision (BLV) users, the lack of accessible explanations creates a fundamental barrier to the independent use of AI-driven assistive technologies. This problem intensifies as AI systems shift from single-query tools into autonomous agents that take multi-step actions and make consequential decisions across extended task horizons, where a single undetected error can propagate irreversibly before any feedback is available. This paper investigates the unique XAI requirements of the BLV community through a comprehensive analysis of user interviews and contemporary research. By examining usage patterns across environmental perception and decision support, we identify a significant modality gap. Empirical evidence suggests that while BLV users highly value conversational explanations, they frequently experience “self-blame” for AI failures. The paper concludes with a research agenda for accessible Explainable AI in agentic systems, advocating for multimodal interfaces, blame-aware explanation design, and participatory development.
2. A Semi-distributed Reputation Based Intrusion Detection System for Mobile Adhoc Networks
Authors: Animesh Kr Trivedi, Rajan Arora, Rishi Kapoor, Sudip Sanyal, Sugata Sanyal
Categories: cs.NI, cs.MA
Published: 2010-06-10
arXiv: 1006.1956v2
Abstract:
A Mobile Adhoc Network (MANET) is a cooperative engagement of a collection of mobile nodes without any centralized access point or infrastructure to coordinate among the peers. The underlying concept of coordination among nodes in a cooperative MANET has induced in them a vulnerability to attacks due to issues like lack of fixed infrastructure, dynamically changing network topology, cooperative algorithms, lack of centralized monitoring and management point, and lack of a clear line of defense. We propose a semi-distributed approach towards Reputation Based Intrusion Detection System (IDS) that combines with the DSR routing protocol for strengthening the defense of a MANET. Our system inherits the features of reputation from human behavior, hence making the IDS socially inspired. It has a semi-distributed architecture as the critical observation results of the system are neither spread globally nor restricted locally. The system assigns maximum weightage to self observation by nodes for updating any reputation values, thus avoiding the need of a trust relationship between nodes. Our system is also unique in the sense that it features the concepts of Redemption and Fading with a robust Path Manager and Monitor system. Simulation studies show that DSR fortified with our system outperforms normal DSR in terms of the packet delivery ratio and routing overhead even when up to half of nodes in the network behave as malicious. Various parameters introduced such as timing window size, reputation update values, congestion parameter and other thresholds have been optimized over several simulation test runs of the system. By combining the semi-distributed architecture and other design essentials like path manager, monitor module, redemption and fading concepts; Our system proves to be robust enough to counter most common attacks in MANETs.
3. 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.
4. Online Feature Ranking for Intrusion Detection Systems
Authors: Buse Gul Atli, Alexander Jung
Categories: cs.CR, cs.LG, cs.NI, stat.ML
Published: 2018-03-01
arXiv: 1803.00530v2
Abstract:
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental learning of support vector machines in order to rank the features in real time within a streaming model for network data. Some illustrative numerical experiments with two popular benchmark datasets show that our approach allows to adapt to the changes in normal network behaviour and novel attack patterns which have not been experienced before.
5. Road Context-aware Intrusion Detection System for Autonomous Cars
Authors: Jingxuan Jiang, Chundong Wang, Sudipta Chattopadhyay, Wei Zhang
Categories: cs.CR, cs.CV
Published: 2019-08-02
arXiv: 1908.00732v1
Abstract:
Security is of primary importance to vehicles. The viability of performing remote intrusions onto the in-vehicle network has been manifested. In regard to unmanned autonomous cars, limited work has been done to detect intrusions for them while existing intrusion detection systems (IDSs) embrace limitations against strong adversaries. In this paper, we consider the very nature of autonomous car and leverage the road context to build a novel IDS, named Road context-aware IDS (RAIDS). When a computer-controlled car is driving through continuous roads, road contexts and genuine frames transmitted on the car’s in-vehicle network should resemble a regular and intelligible pattern. RAIDS hence employs a lightweight machine learning model to extract road contexts from sensory information (e.g., camera images and distance sensor values) that are used to generate control signals for maneuvering the car. With such ongoing road context, RAIDS validates corresponding frames observed on the in-vehicle network. Anomalous frames that substantially deviate from road context will be discerned as intrusions. We have implemented a prototype of RAIDS with neural networks, and conducted experiments on a Raspberry Pi with extensive datasets and meaningful intrusion cases. Evaluations show that RAIDS significantly outperforms state-of-the-art IDS without using road context by up to 99.9% accuracy and short response time.
6. Cyber Situation Awareness with Active Learning for Intrusion Detection
Authors: Steven McElwee, James Cannady
Categories: cs.CR
Published: 2019-12-29
arXiv: 1912.12673v1
Abstract:
Intrusion detection has focused primarily on detecting cyberattacks at the event-level. Since there is such a large volume of network data and attacks are minimal, machine learning approaches have focused on improving accuracy and reducing false positives, but this has frequently resulted in overfitting. In addition, the volume of intrusion detection alerts is large and creates fatigue in the human analyst who must review them. This research addresses the problems associated with event-level intrusion detection and the large volumes of intrusion alerts by applying active learning and cyber situation awareness. This paper includes the results of two experiments using the UNSW-NB15 dataset. The first experiment evaluated sampling approaches for querying the oracle, as part of active learning. It then trained a Random Forest classifier using the samples and evaluated its results. The second experiment applied cyber situation awareness by aggregating the detection results of the first experiment and calculating the probability that a computer system was part of a cyberattack. This research showed that moving the perspective of event-level alerts to the probability that a computer system was part of an attack improved the accuracy of detection and reduced the volume of alerts that a human analyst would need to review.
7. Revealing Method for the Intrusion Detection System
Authors: M. Sadiq Ali Khan
Categories: cs.NI
Published: 2010-04-26
arXiv: 1004.4598v3
Abstract:
The goal of an Intrusion Detection is inadequate to detect errors and unusual activity on a network or on the hosts belonging to a local network by monitoring network activity. Algorithms for building detection models are broadly classified into two categories, Misuse Detection and Anomaly Detection. The proposed approach should be taken into account, as the security system violations caused by both incompliance with the security policy and attacks on the system resulting in the need to describe models. However, it is based on unified mathematical formalism which is provided for subsequent merger of the models. The above formalism in this paper presents a state machine describing the behavior of a system subject. The set of intrusion description models is used by the evaluation module and determines the likelihood of undesired actions the system is capable of detecting. The number of attacks which are not described by models determining the completeness of detection by the IDS linked to the ability of detecting security violations.
8. Explainable Attention-Based LSTM Framework for Early Detection of AI-Assisted Ransomware via File System Behavioral Analysis
Authors: Prabhudarshi Nayak, Gogulakrishnan Thiyagarajan, Debashree Priyadarshini, Vinay Bist, Rohan Swain
Categories: cs.CR
Published: 2026-04-19
arXiv: 2604.17522v1
Abstract:
Ransomware continues to evolve as one of the most disruptive cyber threats, with recent variants increasingly leveraging automated and AI-assisted techniques to evade traditional signature-based defenses. Early detection of such attacks remains a significant challenge, particularly when malicious behavior closely resembles legitimate system activity. This study proposes an explainable attention-based Long Short-Term Memory (LSTM) framework for the early detection of AI assisted ransomware variants through analysis of file system behavioral patterns. The proposed model captures temporal dependencies in file operation sequences, while an attention mechanism highlights critical behavioral indicators associated with ransomware activity. To improve transparency and trust in automated detection systems, explainable artificial intelligence (XAI) techniques are incorporated to interpret model predictions and identify influential behavioral features. Experimental evaluation using ransomware behavioral traces demonstrates that the proposed framework can effectively distinguish malicious activity at early stages of execution with high detection performance and low false-positive rates. The findings suggest that combining sequence-aware deep learning models with explainability mechanisms can significantly enhance the reliability and interpretability of next-generation ransomware defense systems. This work contributes toward the development of intelligent and transparent cyber-defense mechanisms capable of addressing emerging AI-driven malware threats.
9. Faith in AI can narrow the futures individuals consider
Authors: Aoi Naito, Hirokazu Shirado
Categories: cs.HC
Published: 2026-03-30
arXiv: 2603.28944v2
Abstract:
Artificial intelligence (AI) predictions are increasingly used to inform human decisions. Here, using a behavioral implementation of the classic Newcomb’s paradox in 1,305 participants, we show that AI predictions can also shape the reasoning people use to make a decision. In this paradigm, perceived predictive authority can alter how people reason about their future actions, leading them to forgo a guaranteed reward. Over 40% of participants treated AI as such a predictive authority about their own behavior, significantly increasing the odds of forgoing the guaranteed reward by a factor of 3.39 (95% CI: 2.45-4.70) and reducing earnings by 10.7-42.9%. The effect appeared across AI presentations and decision contexts and remained detectable even when predictions repeatedly failed. When people perceive AI as capable of predicting their personal behavior, the mere presence of AI predictions may shape their decision-making, narrowing the futures they consider.
10. Intrusion Detection in Internet of Things using Convolutional Neural Networks
Authors: Martin Kodys, Zhi Lu, Kar Wai Fok, Vrizlynn L. L. Thing
Categories: cs.CR, cs.AI, cs.LG
Published: 2022-11-18
arXiv: 2211.10062v1
Abstract:
Internet of Things (IoT) has become a popular paradigm to fulfil needs of the industry such as asset tracking, resource monitoring and automation. As security mechanisms are often neglected during the deployment of IoT devices, they are more easily attacked by complicated and large volume intrusion attacks using advanced techniques. Artificial Intelligence (AI) has been used by the cyber security community in the past decade to automatically identify such attacks. However, deep learning methods have yet to be extensively explored for Intrusion Detection Systems (IDS) specifically for IoT. Most recent works are based on time sequential models like LSTM and there is short of research in CNNs as they are not naturally suited for this problem. In this article, we propose a novel solution to the intrusion attacks against IoT devices using CNNs. The data is encoded as the convolutional operations to capture the patterns from the sensors data along time that are useful for attacks detection by CNNs. The proposed method is integrated with two classical CNNs: ResNet and EfficientNet, where the detection performance is evaluated. The experimental results show significant improvement in both true positive rate and false positive rate compared to the baseline using LSTM.
11. Proceedings of The second international workshop on eXplainable AI for the Arts (XAIxArts)
Authors: Nick Bryan-Kinns, Corey Ford, Shuoyang Zheng, Helen Kennedy, Alan Chamberlain, Makayla Lewis, Drew Hemment, Zijin Li, Qiong Wu, Lanxi Xiao, Gus Xia, Jeba Rezwana, Michael Clemens, Gabriel Vigliensoni
Categories: cs.AI, cs.HC, cs.MM, cs.SD, eess.AS
Published: 2024-06-20
arXiv: 2406.14485v8
Abstract:
This second international workshop on explainable AI for the Arts (XAIxArts) brought together a community of researchers in HCI, Interaction Design, AI, explainable AI (XAI), and digital arts to explore the role of XAI for the Arts. Workshop held at the 16th ACM Conference on Creativity and Cognition (C&C 2024), Chicago, USA.
12. A New Clustering Approach for Anomaly Intrusion Detection
Authors: Ravi Ranjan, G. Sahoo
Categories: cs.DC, cs.CR, cs.LG
Published: 2014-04-10
arXiv: 1404.2772v1
Abstract:
Recent advances in technology have made our work easier compare to earlier times. Computer network is growing day by day but while discussing about the security of computers and networks it has always been a major concerns for organizations varying from smaller to larger enterprises. It is true that organizations are aware of the possible threats and attacks so they always prepare for the safer side but due to some loopholes attackers are able to make attacks. Intrusion detection is one of the major fields of research and researchers are trying to find new algorithms for detecting intrusions. Clustering techniques of data mining is an interested area of research for detecting possible intrusions and attacks. This paper presents a new clustering approach for anomaly intrusion detection by using the approach of K-medoids method of clustering and its certain modifications. The proposed algorithm is able to achieve high detection rate and overcomes the disadvantages of K-means algorithm.
13. Proceedings of The third international workshop on eXplainable AI for the Arts (XAIxArts)
Authors: Corey Ford, Elizabeth Wilson, Shuoyang Zheng, Gabriel Vigliensoni, Jeba Rezwana, Lanxi Xiao, Michael Clemens, Makayla Lewis, Drew Hemment, Alan Chamberlain, Helen Kennedy, Nick Bryan-Kinns
Categories: cs.AI, cs.HC, cs.MM, cs.SD
Published: 2025-11-13
arXiv: 2511.10482v1
Abstract:
This third international workshop on explainable AI for the Arts (XAIxArts) brought together a community of researchers in HCI, Interaction Design, AI, explainable AI (XAI), and digital arts to explore the role of XAI for the Arts. Workshop held at the 17th ACM Conference on Creativity and Cognition (C&C 2025), online.
14. Embodied Exploration of Latent Spaces and Explainable AI
Authors: Elizabeth Wilson, Mika Satomi, Alex McLean, Deva Schubert, Juan Felipe Amaya Gonzalez
Categories: cs.SD, cs.CY, cs.HC, eess.AS
Published: 2024-10-18
arXiv: 2410.14590v1
Abstract:
In this paper, we explore how performers’ embodied interactions with a Neural Audio Synthesis model allow the exploration of the latent space of such a model, mediated through movements sensed by e-textiles. We provide background and context for the performance, highlighting the potential of embodied practices to contribute to developing explainable AI systems. By integrating various artistic domains with explainable AI principles, our interdisciplinary exploration contributes to the discourse on art, embodiment, and AI, offering insights into intuitive approaches found through bodily expression.
15. Burstiness of Intrusion Detection Process: Empirical Evidence and a Modeling Approach
Authors: Richard Harang, Alexander Kott
Categories: cs.CR
Published: 2017-07-12
arXiv: 1707.03927v1
Abstract:
We analyze sets of intrusion detection records observed on the networks of several large, nonresidential organizations protected by a form of intrusion detection and prevention service. Our analyses reveal that the process of intrusion detection in these networks exhibits a significant degree of burstiness as well as strong memory, with burstiness and memory properties that are comparable to those of natural processes driven by threshold effects, but different from bursty human activities. We explore time-series models of these observable network security incidents based on partially observed data using a hidden Markov model with restricted hidden states, which we fit using Markov Chain Monte Carlo techniques. We examine the output of the fitted model with respect to its statistical properties and demonstrate that the model adequately accounts for intrinsic “bursting” within observed network incidents as a result of alternation between two or more stochastic processes. While our analysis does not lead directly to new detection capabilities, the practical implications of gaining better understanding of the observed burstiness are significant, and include opportunities for quantifying a network’s risks and defensive efforts.
16. Explaining Network Intrusion Detection System Using Explainable AI Framework
Authors: Shraddha Mane, Dattaraj Rao
Categories: cs.CR, cs.AI
Published: 2021-03-12
arXiv: 2103.07110v1
Abstract:
Cybersecurity is a domain where the data distribution is constantly changing with attackers exploring newer patterns to attack cyber infrastructure. Intrusion detection system is one of the important layers in cyber safety in today’s world. Machine learning based network intrusion detection systems started showing effective results in recent years. With deep learning models, detection rates of network intrusion detection system are improved. More accurate the model, more the complexity and hence less the interpretability. Deep neural networks are complex and hard to interpret which makes difficult to use them in production as reasons behind their decisions are unknown. In this paper, we have used deep neural network for network intrusion detection and also proposed explainable AI framework to add transparency at every stage of machine learning pipeline. This is done by leveraging Explainable AI algorithms which focus on making ML models less of black boxes by providing explanations as to why a prediction is made. Explanations give us measurable factors as to what features influence the prediction of a cyberattack and to what degree. These explanations are generated from SHAP, LIME, Contrastive Explanations Method, ProtoDash and Boolean Decision Rules via Column Generation. We apply these approaches to NSL KDD dataset for intrusion detection system and demonstrate results.
17. Foundations of GenIR
Authors: Qingyao Ai, Jingtao Zhan, Yiqun Liu
Categories: cs.IR, cs.LG
Published: 2025-01-06
arXiv: 2501.02842v1
Abstract:
The chapter discusses the foundational impact of modern generative AI models on information access (IA) systems. In contrast to traditional AI, the large-scale training and superior data modeling of generative AI models enable them to produce high-quality, human-like responses, which brings brand new opportunities for the development of IA paradigms. In this chapter, we identify and introduce two of them in details, i.e., information generation and information synthesis. Information generation allows AI to create tailored content addressing user needs directly, enhancing user experience with immediate, relevant outputs. Information synthesis leverages the ability of generative AI to integrate and reorganize existing information, providing grounded responses and mitigating issues like model hallucination, which is particularly valuable in scenarios requiring precision and external knowledge. This chapter delves into the foundational aspects of generative models, including architecture, scaling, and training, and discusses their applications in multi-modal scenarios. Additionally, it examines the retrieval-augmented generation paradigm and other methods for corpus modeling and understanding, demonstrating how generative AI can enhance information access systems. It also summarizes potential challenges and fruitful directions for future studies.
18. Interaction Design for Explainable AI: Workshop Proceedings
Authors: Prashan Madumal, Ronal Singh, Joshua Newn, Frank Vetere
Categories: cs.AI
Published: 2018-12-13
arXiv: 1812.08597v1
Abstract:
As artificial intelligence (AI) systems become increasingly complex and ubiquitous, these systems will be responsible for making decisions that directly affect individuals and society as a whole. Such decisions will need to be justified due to ethical concerns as well as trust, but achieving this has become difficult due to the
black-box' nature many AI models have adopted. Explainable AI (XAI) can potentially address this problem by explaining its actions, decisions and behaviours of the system to users. However, much research in XAI is done in a vacuum using only the researchers' intuition of what constitutes agood’ explanation while ignoring the interaction and the human aspect. This workshop invites researchers in the HCI community and related fields to have a discourse about human-centred approaches to XAI rooted in interaction and to shed light and spark discussion on interaction design challenges in XAI.
19. A review of Federated Learning in Intrusion Detection Systems for IoT
Authors: Aitor Belenguer, Javier Navaridas, Jose A. Pascual
Categories: cs.CR, cs.LG
Published: 2022-04-26
arXiv: 2204.12443v2
Abstract:
Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. The development of deep learning technologies opened the door to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Internet of Things devices. Current approaches rely on powerful centralized servers that receive data from all their parties – violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach where different agents collaboratively train a shared model, neither exposing training data to others nor requiring a compute-intensive centralized infrastructure. This paper focuses on the application of Federated Learning approaches in the field of Intrusion Detection. Both technologies are described in detail and current scientific progress is reviewed and categorized. Finally, the paper highlights the limitations present in recent works and presents some future directions for this technology.
20. Need of AI in Modern Education: in the Eyes of Explainable AI (xAI)
Authors: Supriya Manna, Niladri Sett
Categories: cs.AI
Published: 2024-07-31
arXiv: 2408.00025v3
Abstract:
Modern Education is not \textit{Modern} without AI. However, AI’s complex nature makes understanding and fixing problems challenging. Research worldwide shows that a parent’s income greatly influences a child’s education. This led us to explore how AI, especially complex models, makes important decisions using Explainable AI tools. Our research uncovered many complexities linked to parental income and offered reasonable explanations for these decisions. However, we also found biases in AI that go against what we want from AI in education: clear transparency and equal access for everyone. These biases can impact families and children’s schooling, highlighting the need for better AI solutions that offer fair opportunities to all. This chapter tries to shed light on the complex ways AI operates, especially concerning biases. These are the foundational steps towards better educational policies, which include using AI in ways that are more reliable, accountable, and beneficial for everyone involved.
21. Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI
Authors: Qi Huang, Emanuele Mezzi, Osman Mutlu, Miltiadis Kofinas, Vidya Prasad, Shadnan Azwad Khan, Elena Ranguelova, Niki van Stein
Categories: cs.AI, cs.CV, cs.LG
Published: 2024-07-17
arXiv: 2407.12950v2
Abstract:
We introduce a novel metric for measuring semantic continuity in Explainable AI methods and machine learning models. We posit that for models to be truly interpretable and trustworthy, similar inputs should yield similar explanations, reflecting a consistent semantic understanding. By leveraging XAI techniques, we assess semantic continuity in the task of image recognition. We conduct experiments to observe how incremental changes in input affect the explanations provided by different XAI methods. Through this approach, we aim to evaluate the models’ capability to generalize and abstract semantic concepts accurately and to evaluate different XAI methods in correctly capturing the model behaviour. This paper contributes to the broader discourse on AI interpretability by proposing a quantitative measure for semantic continuity for XAI methods, offering insights into the models’ and explainers’ internal reasoning processes, and promoting more reliable and transparent AI systems.
22. Competing Visions of Ethical AI: A Case Study of OpenAI
Authors: Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo
Categories: cs.CY
Published: 2026-01-23
arXiv: 2601.16513v1
Abstract:
Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI’s public discourse leveraged ’ethics’, ‘safety’, ‘alignment’ and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communication for a general audience and communication with an academic audience, was assembled from public documentation. Analysis. Qualitative content analysis of ethical themes combined inductively derived and deductively applied codes. Quantitative analysis leveraged computational content analysis methods via NLP to model topics and quantify changes in rhetoric over time. Visualizations report aggregate results. For reproducible results, we have released our code at https://github.com/famous-blue-raincoat/AI_Ethics_Discourse. Results. Results indicate that safety and risk discourse dominate OpenAI’s public communication and documentation, without applying academic and advocacy ethics frameworks or vocabularies. Conclusions. Implications for governance are presented, along with discussion of ethics-washing practices in industry.
23. Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning
Authors: Poulmanogo Illy, Georges Kaddoum, Christian Miranda Moreira, Kuljeet Kaur, Sahil Garg
Categories: cs.CR, cs.LG, cs.NI
Published: 2019-01-30
arXiv: 1901.10933v1
Abstract:
The growing interest in the Internet of Things (IoT) applications is associated with an augmented volume of security threats. In this vein, the Intrusion detection systems (IDS) have emerged as a viable solution for the detection and prevention of malicious activities. Unlike the signature-based detection approaches, machine learning-based solutions are a promising means for detecting unknown attacks. However, the machine learning models need to be accurate enough to reduce the number of false alarms. More importantly, they need to be trained and evaluated on realistic datasets such that their efficacy can be validated on real-time deployments. Many solutions proposed in the literature are reported to have high accuracy but are ineffective in real applications due to the non-representativity of the dataset used for training and evaluation of the underlying models. On the other hand, some of the existing solutions overcome these challenges but yield low accuracy which hampers their implementation for commercial tools. These solutions are majorly based on single learners and are therefore directly affected by the intrinsic limitations of each learning algorithm. The novelty of this paper is to use the most realistic dataset available for intrusion detection called NSL-KDD, and combine multiple learners to build ensemble learners that increase the accuracy of the detection. Furthermore, a deployment architecture in a fog-to-things environment that employs two levels of classifications is proposed. In such architecture, the first level performs an anomaly detection which reduces the latency of the classification substantially, while the second level, executes attack classifications, enabling precise prevention measures. Finally, the experimental results demonstrate the effectiveness of the proposed IDS in comparison with the other state-of-the-arts on the NSL-KDD dataset.
24. TextSleuth: Towards Explainable Tampered Text Detection
Authors: Chenfan Qu, Jian Liu, Haoxing Chen, Baihan Yu, Jingjing Liu, Weiqiang Wang, Lianwen Jin
Categories: cs.CV
Published: 2024-12-19
arXiv: 2412.14816v3
Abstract:
Recently, tampered text detection has attracted increasing attention due to its essential role in information security. Although existing methods can detect the tampered text region, the interpretation of such detection remains unclear, making the prediction unreliable. To address this problem, we propose to explain the basis of tampered text detection with natural language via large multimodal models. To fill the data gap for this task, we propose a large-scale, comprehensive dataset, ETTD, which contains both pixel-level annotations for tampered text region and natural language annotations describing the anomaly of the tampered text. Multiple methods are employed to improve the quality of the proposed data. For example, elaborate queries are introduced to generate high-quality anomaly descriptions with GPT4o. A fused mask prompt is proposed to reduce confusion when querying GPT4o to generate anomaly descriptions. To automatically filter out low-quality annotations, we also propose to prompt GPT4o to recognize tampered texts before describing the anomaly, and to filter out the responses with low OCR accuracy. To further improve explainable tampered text detection, we propose a simple yet effective model called TextSleuth, which achieves improved fine-grained perception and cross-domain generalization by focusing on the suspected region, with a two-stage analysis paradigm and an auxiliary grounding prompt. Extensive experiments on both the ETTD dataset and the public dataset have verified the effectiveness of the proposed methods. In-depth analysis is also provided to inspire further research. Our dataset and code will be open-source.
25. More Questions than Answers? Lessons from Integrating Explainable AI into a Cyber-AI Tool
Authors: Ashley Suh, Harry Li, Caitlin Kenney, Kenneth Alperin, Steven R. Gomez
Categories: cs.HC, cs.AI
Published: 2024-08-08
arXiv: 2408.04746v1
Abstract:
We share observations and challenges from an ongoing effort to implement Explainable AI (XAI) in a domain-specific workflow for cybersecurity analysts. Specifically, we briefly describe a preliminary case study on the use of XAI for source code classification, where accurate assessment and timeliness are paramount. We find that the outputs of state-of-the-art saliency explanation techniques (e.g., SHAP or LIME) are lost in translation when interpreted by people with little AI expertise, despite these techniques being marketed for non-technical users. Moreover, we find that popular XAI techniques offer fewer insights for real-time human-AI workflows when they are post hoc and too localized in their explanations. Instead, we observe that cyber analysts need higher-level, easy-to-digest explanations that can offer as little disruption as possible to their workflows. We outline unaddressed gaps in practical and effective XAI, then touch on how emerging technologies like Large Language Models (LLMs) could mitigate these existing obstacles.
26. Explainable AI in Usable Privacy and Security: Challenges and Opportunities
Authors: Vincent Freiberger, Arthur Fleig, Erik Buchmann
Categories: cs.HC
Published: 2025-04-17
arXiv: 2504.12931v1
Abstract:
Large Language Models (LLMs) are increasingly being used for automated evaluations and explaining them. However, concerns about explanation quality, consistency, and hallucinations remain open research challenges, particularly in high-stakes contexts like privacy and security, where user trust and decision-making are at stake. In this paper, we investigate these issues in the context of PRISMe, an interactive privacy policy assessment tool that leverages LLMs to evaluate and explain website privacy policies. Based on a prior user study with 22 participants, we identify key concerns regarding LLM judgment transparency, consistency, and faithfulness, as well as variations in user preferences for explanation detail and engagement. We discuss potential strategies to mitigate these concerns, including structured evaluation criteria, uncertainty estimation, and retrieval-augmented generation (RAG). We identify a need for adaptive explanation strategies tailored to different user profiles for LLM-as-a-judge. Our goal is to showcase the application area of usable privacy and security to be promising for Human-Centered Explainable AI (HCXAI) to make an impact.
27. Explainability Paths for Sustained Artistic Practice with AI
Authors: Austin Tecks, Thomas Peschlow, Gabriel Vigliensoni
Categories: cs.SD, cs.AI, eess.AS
Published: 2024-07-21
arXiv: 2407.15216v1
Abstract:
The development of AI-driven generative audio mirrors broader AI trends, often prioritizing immediate accessibility at the expense of explainability. Consequently, integrating such tools into sustained artistic practice remains a significant challenge. In this paper, we explore several paths to improve explainability, drawing primarily from our research-creation practice in training and implementing generative audio models. As practical provisions for improved explainability, we highlight human agency over training materials, the viability of small-scale datasets, the facilitation of the iterative creative process, and the integration of interactive machine learning as a mapping tool. Importantly, these steps aim to enhance human agency over generative AI systems not only during model inference, but also when curating and preprocessing training data as well as during the training phase of models.
28. Tree-based Intelligent Intrusion Detection System in Internet of Vehicles
Authors: Li Yang, Abdallah Moubayed, Ismail Hamieh, Abdallah Shami
Categories: cs.LG, cs.CR, stat.ML
Published: 2019-10-18
arXiv: 1910.08635v2
Abstract:
The use of autonomous vehicles (AVs) is a promising technology in Intelligent Transportation Systems (ITSs) to improve safety and driving efficiency. Vehicle-to-everything (V2X) technology enables communication among vehicles and other infrastructures. However, AVs and Internet of Vehicles (IoV) are vulnerable to different types of cyber-attacks such as denial of service, spoofing, and sniffing attacks. In this paper, an intelligent intrusion detection system (IDS) is proposed based on tree-structure machine learning models. The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks. Furthermore, the proposed ensemble learning and feature selection approaches enable the proposed system to achieve high detection rate and low computational cost simultaneously.
29. Play Me Something Icy: Practical Challenges, Explainability and the Semantic Gap in Generative AI Music
Authors: Jesse Allison, Drew Farrar, Treya Nash, Carlos Román, Morgan Weeks, Fiona Xue Ju
Categories: cs.SD, cs.AI, eess.AS
Published: 2024-08-13
arXiv: 2408.07224v1
Abstract:
This pictorial aims to critically consider the nature of text-to-audio and text-to-music generative tools in the context of explainable AI. As a group of experimental musicians and researchers, we are enthusiastic about the creative potential of these tools and have sought to understand and evaluate them from perspectives of prompt creation, control, usability, understandability, explainability of the AI process, and overall aesthetic effectiveness of the results. One of the challenges we have identified that is not explicitly addressed by these tools is the inherent semantic gap in using text-based tools to describe something as abstract as music. Other gaps include explainability vs. useability, and user control and input vs. the human creative process. The aim of this pictorial is to raise questions for discussion and make a few general suggestions on the kinds of improvements we would like to see in generative AI music tools.
30. LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehicles
Authors: Li Yang, Abdallah Shami, Gary Stevens, Stephen De Rusett
Categories: cs.CR, cs.AI, cs.LG, cs.NI
Published: 2022-08-05
arXiv: 2208.03399v2
Abstract:
Modern vehicles, including autonomous vehicles and connected vehicles, have adopted an increasing variety of functionalities through connections and communications with other vehicles, smart devices, and infrastructures. However, the growing connectivity of the Internet of Vehicles (IoV) also increases the vulnerabilities to network attacks. To protect IoV systems against cyber threats, Intrusion Detection Systems (IDSs) that can identify malicious cyber-attacks have been developed using Machine Learning (ML) approaches. To accurately detect various types of attacks in IoV networks, we propose a novel ensemble IDS framework named Leader Class and Confidence Decision Ensemble (LCCDE). It is constructed by determining the best-performing ML model among three advanced ML algorithms (XGBoost, LightGBM, and CatBoost) for every class or type of attack. The class leader models with their prediction confidence values are then utilized to make accurate decisions regarding the detection of various types of cyber-attacks. Experiments on two public IoV security datasets (Car-Hacking and CICIDS2017 datasets) demonstrate the effectiveness of the proposed LCCDE for intrusion detection on both intra-vehicle and external networks.
31. Natural Language Interaction with Explainable AI Models
Authors: Arjun R Akula, Sinisa Todorovic, Joyce Y Chai, Song-Chun Zhu
Categories: cs.AI
Published: 2019-03-13
arXiv: 1903.05720v2
Abstract:
This paper presents an explainable AI (XAI) system that provides explanations for its predictions. The system consists of two key components – namely, the prediction And-Or graph (AOG) model for recognizing and localizing concepts of interest in input data, and the XAI model for providing explanations to the user about the AOG’s predictions. In this work, we focus on the XAI model specified to interact with the user in natural language, whereas the AOG’s predictions are considered given and represented by the corresponding parse graphs (pg’s) of the AOG. Our XAI model takes pg’s as input and provides answers to the user’s questions using the following types of reasoning: direct evidence (e.g., detection scores), part-based inference (e.g., detected parts provide evidence for the concept asked), and other evidences from spatio-temporal context (e.g., constraints from the spatio-temporal surround). We identify several correlations between user’s questions and the XAI answers using Youtube Action dataset.
32. Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework
Authors: B. M. Taslimul Haque, Md. Arifur Rahman, Md. Serajul Kabir Chowdhury Rubel, Md. Iqbal Hossan
Categories: cs.CR, cs.AI
Published: 2026-06-04
arXiv: 2606.05710v1
Abstract:
The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities. AI-powered governance and automated decision-making systems are becoming a key part of the operation of critical infrastructure systems, including energy, healthcare, transportation, financial services, and communication infrastructure, in order to improve efficiency and strategic management. The growing cyber threat environment, such as Distributed Denial of Service (DDos) attacks, botnets, ransomware, and Advanced Persistent Threats (APTs) pose significant challenges to infrastructure resilience, cyber security reliability, and governance trustworthiness. In a changing attack landscape and dynamic network environment, traditional cybersecurity mechanisms can often fall short of meeting the evolving needs and protecting critical systems. This study will develop a resilient cyber risk analytics and model reliability assessment framework to support intelligent governance and decision support for cyber risk exposure in the U.S. critical infrastructure environment. This study is based on the CICIDS2017 dataset for the development and testing of intrusion detection system models and cyber risk prediction models based on machine learning. Various classifiers like XGBoost, Random Forest, and Decision Tree are used to detect malicious activities on the network and determine the level of cyber risk. Furthermore, the Explainable Artificial Intelligence (XAI) techniques are integrated to enhance transparency, interpretability, and trust in cybersecurity decision-making processes. The proposed framework presents the reliability and resilience of the model by having various performance measures such as accuracy, precision, recall, F1 score, ROC-AUC, and false positive rate.
33. Defining Explainable AI for Requirements Analysis
Authors: Raymond Sheh, Isaac Monteath
Categories: cs.AI
Published: 2026-02-22
arXiv: 2602.19071v1
Abstract:
Explainable Artificial Intelligence (XAI) has become popular in the last few years. The Artificial Intelligence (AI) community in general, and the Machine Learning (ML) community in particular, is coming to the realisation that in many applications, for AI to be trusted, it must not only demonstrate good performance in its decisionmaking, but it also must explain these decisions and convince us that it is making the decisions for the right reasons. However, different applications have different requirements on the information required of the underlying AI system in order to convince us that it is worthy of our trust. How do we define these requirements? In this paper, we present three dimensions for categorising the explanatory requirements of different applications. These are Source, Depth and Scope. We focus on the problem of matching up the explanatory requirements of different applications with the capabilities of underlying ML techniques to provide them. We deliberately avoid including aspects of explanation that are already well-covered by the existing literature and we focus our discussion on ML although the principles apply to AI more broadly.
34. Explaining how your AI system is fair
Authors: Boris Ruf, Marcin Detyniecki
Categories: cs.AI, cs.CY, cs.LG
Published: 2021-05-03
arXiv: 2105.00667v1
Abstract:
To implement fair machine learning in a sustainable way, choosing the right fairness objective is key. Since fairness is a concept of justice which comes in various, sometimes conflicting definitions, this is not a trivial task though. The most appropriate fairness definition for an artificial intelligence (AI) system is a matter of ethical standards and legal requirements, and the right choice depends on the particular use case and its context. In this position paper, we propose to use a decision tree as means to explain and justify the implemented kind of fairness to the end users. Such a structure would first of all support AI practitioners in mapping ethical principles to fairness definitions for a concrete application and therefore make the selection a straightforward and transparent process. However, this approach would also help document the reasoning behind the decision making. Due to the general complexity of the topic of fairness in AI, we argue that specifying “fairness” for a given use case is the best way forward to maintain confidence in AI systems. In this case, this could be achieved by sharing the reasons and principles expressed during the decision making process with the broader audience.
35. ACE, Action and Control via Explanations: A Proposal for LLMs to Provide Human-Centered Explainability for Multimodal AI Assistants
Authors: Elizabeth Anne Watkins, Emanuel Moss, Ramesh Manuvinakurike, Meng Shi, Richard Beckwith, Giuseppe Raffa
Categories: cs.HC, cs.AI
Published: 2025-02-27
arXiv: 2503.16466v1
Abstract:
In this short paper we address issues related to building multimodal AI systems for human performance support in manufacturing domains. We make two contributions: we first identify challenges of participatory design and training of such systems, and secondly, to address such challenges, we propose the ACE paradigm: “Action and Control via Explanations”. Specifically, we suggest that LLMs can be used to produce explanations in the form of human interpretable “semantic frames”, which in turn enable end users to provide data the AI system needs to align its multimodal models and representations, including computer vision, automatic speech recognition, and document inputs. ACE, by using LLMs to “explain” using semantic frames, will help the human and the AI system to collaborate, together building a more accurate model of humans activities and behaviors, and ultimately more accurate predictive outputs for better task support, and better outcomes for human users performing manual tasks.
36. Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System
Authors: Li Yang, Abdallah Shami
Categories: cs.CR, cs.LG, cs.NI
Published: 2025-11-11
arXiv: 2511.08491v1
Abstract:
With increasingly sophisticated cybersecurity threats and rising demand for network automation, autonomous cybersecurity mechanisms are becoming critical for securing modern networks. The rapid expansion of Internet of Things (IoT) systems amplifies these challenges, as resource-constrained IoT devices demand scalable and efficient security solutions. In this work, an innovative Intrusion Detection System (IDS) utilizing Automated Machine Learning (AutoML) and Multi-Objective Optimization (MOO) is proposed for autonomous and optimized cyber-attack detection in modern networking environments. The proposed IDS framework integrates two primary innovative techniques: Optimized Importance and Percentage-based Automated Feature Selection (OIP-AutoFS) and Optimized Performance, Confidence, and Efficiency-based Combined Algorithm Selection and Hyperparameter Optimization (OPCE-CASH). These components optimize feature selection and model learning processes to strike a balance between intrusion detection effectiveness and computational efficiency. This work presents the first IDS framework that integrates all four AutoML stages and employs multi-objective optimization to jointly optimize detection effectiveness, efficiency, and confidence for deployment in resource-constrained systems. Experimental evaluations over two benchmark cybersecurity datasets demonstrate that the proposed MOO-AutoML IDS outperforms state-of-the-art IDSs, establishing a new benchmark for autonomous, efficient, and optimized security for networks. Designed to support IoT and edge environments with resource constraints, the proposed framework is applicable to a variety of autonomous cybersecurity applications across diverse networked environments.
37. Legally-Informed Explainable AI
Authors: Gennie Mansi, Naveena Karusala, Mark Riedl
Categories: cs.HC
Published: 2025-04-14
arXiv: 2504.10708v1
Abstract:
Explanations for artificial intelligence (AI) systems are intended to support the people who are impacted by AI systems in high-stakes decision-making environments, such as doctors, patients, teachers, students, housing applicants, and many others. To protect people and support the responsible development of AI, explanations need to be actionable–helping people take pragmatic action in response to an AI system–and contestable–enabling people to push back against an AI system and its determinations. For many high-stakes domains, such as healthcare, education, and finance, the sociotechnical environment includes significant legal implications that impact how people use AI explanations. For example, physicians who use AI decision support systems may need information on how accepting or rejecting an AI determination will protect them from lawsuits or help them advocate for their patients. In this paper, we make the case for Legally-Informed Explainable AI, responding to the need to integrate and design for legal considerations when creating AI explanations. We describe three stakeholder groups with different informational and actionability needs, and provide practical recommendations to tackle design challenges around the design of explainable AI systems that incorporate legal considerations.
38. Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection
Authors: Li Yang, Abdallah Shami
Categories: cs.LG, cs.CR, cs.NI
Published: 2024-09-05
arXiv: 2409.03141v1
Abstract:
The rapid evolution of mobile networks from 5G to 6G has necessitated the development of autonomous network management systems, such as Zero-Touch Networks (ZTNs). However, the increased complexity and automation of these networks have also escalated cybersecurity risks. Existing Intrusion Detection Systems (IDSs) leveraging traditional Machine Learning (ML) techniques have shown effectiveness in mitigating these risks, but they often require extensive manual effort and expert knowledge. To address these challenges, this paper proposes an Automated Machine Learning (AutoML)-based autonomous IDS framework towards achieving autonomous cybersecurity for next-generation networks. To achieve autonomous intrusion detection, the proposed AutoML framework automates all critical procedures of the data analytics pipeline, including data pre-processing, feature engineering, model selection, hyperparameter tuning, and model ensemble. Specifically, it utilizes a Tabular Variational Auto-Encoder (TVAE) method for automated data balancing, tree-based ML models for automated feature selection and base model learning, Bayesian Optimization (BO) for hyperparameter optimization, and a novel Optimized Confidence-based Stacking Ensemble (OCSE) method for automated model ensemble. The proposed AutoML-based IDS was evaluated on two public benchmark network security datasets, CICIDS2017 and 5G-NIDD, and demonstrated improved performance compared to state-of-the-art cybersecurity methods. This research marks a significant step towards fully autonomous cybersecurity in next-generation networks, potentially revolutionizing network security applications.
39. Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments
Authors: Md Shajalal, Alexander Boden, Gunnar Stevens, Delong Du, Dean-Robin Kern
Categories: cs.HC, cs.AI
Published: 2024-04-23
arXiv: 2404.16074v1
Abstract:
Smart home systems are gaining popularity as homeowners strive to enhance their living and working environments while minimizing energy consumption. However, the adoption of artificial intelligence (AI)-enabled decision-making models in smart home systems faces challenges due to the complexity and black-box nature of these systems, leading to concerns about explainability, trust, transparency, accountability, and fairness. The emerging field of explainable artificial intelligence (XAI) addresses these issues by providing explanations for the models’ decisions and actions. While state-of-the-art XAI methods are beneficial for AI developers and practitioners, they may not be easily understood by general users, particularly household members. This paper advocates for human-centered XAI methods, emphasizing the importance of delivering readily comprehensible explanations to enhance user satisfaction and drive the adoption of smart home systems. We review state-of-the-art XAI methods and prior studies focusing on human-centered explanations for general users in the context of smart home applications. Through experiments on two smart home application scenarios, we demonstrate that explanations generated by prominent XAI techniques might not be effective in helping users understand and make decisions. We thus argue for the necessity of a human-centric approach in representing explanations in smart home systems and highlight relevant human-computer interaction (HCI) methodologies, including user studies, prototyping, technology probes analysis, and heuristic evaluation, that can be employed to generate and present human-centered explanations to users.
40. MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles
Authors: Li Yang, Abdallah Moubayed, Abdallah Shami
Categories: cs.CR, cs.AI, cs.LG, cs.NI
Published: 2021-05-26
arXiv: 2105.13289v1
Abstract:
Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intra-vehicle networks to implement various functionalities and perform actions. Modern vehicles are also connected to external networks through vehicle-to-everything technologies, enabling their communications with other vehicles, infrastructures, and smart devices. However, the improving functionality and connectivity of modern vehicles also increase their vulnerabilities to cyber-attacks targeting both intra-vehicle and external networks due to the large attack surfaces. To secure vehicular networks, many researchers have focused on developing intrusion detection systems (IDSs) that capitalize on machine learning methods to detect malicious cyber-attacks. In this paper, the vulnerabilities of intra-vehicle and external networks are discussed, and a multi-tiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks. Experimental results illustrate that the proposed system can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset representing the intra-vehicle network data and 99.88% accuracy on the CICIDS2017 dataset illustrating the external vehicular network data. For the zero-day attack detection, the proposed system achieves high F1-scores of 0.963 and 0.800 on the above two datasets, respectively. The average processing time of each data packet on a vehicle-level machine is less than 0.6 ms, which shows the feasibility of implementing the proposed system in real-time vehicle systems. This emphasizes the effectiveness and efficiency of the proposed IDS.
41. Online Self-Supervised Deep Learning for Intrusion Detection Systems
Authors: Mert Nakıp, Erol Gelenbe
Categories: cs.CR, cs.LG, cs.NI
Published: 2023-06-22
arXiv: 2306.13030v2
Abstract:
This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network, and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known {machine learning and deep learning} models, showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-based IDS for IoT systems.
42. Explainable AI, but explainable to whom?
Authors: Julie Gerlings, Millie Søndergaard Jensen, Arisa Shollo
Categories: cs.HC
Published: 2021-06-10
arXiv: 2106.05568v2
Abstract:
Advances in AI technologies have resulted in superior levels of AI-based model performance. However, this has also led to a greater degree of model complexity, resulting in ‘black box’ models. In response to the AI black box problem, the field of explainable AI (xAI) has emerged with the aim of providing explanations catered to human understanding, trust, and transparency. Yet, we still have a limited understanding of how xAI addresses the need for explainable AI in the context of healthcare. Our research explores the differing explanation needs amongst stakeholders during the development of an AI-system for classifying COVID-19 patients for the ICU. We demonstrate that there is a constellation of stakeholders who have different explanation needs, not just the ‘user’. Further, the findings demonstrate how the need for xAI emerges through concerns associated with specific stakeholder groups i.e., the development team, subject matter experts, decision makers, and the audience. Our findings contribute to the expansion of xAI by highlighting that different stakeholders have different explanation needs. From a practical perspective, the study provides insights on how AI systems can be adjusted to support different stakeholders needs, ensuring better implementation and operation in a healthcare context.
43. A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles
Authors: Li Yang, Abdallah Shami
Categories: cs.CR, cs.AI, cs.CV, cs.LG, cs.NI
Published: 2022-01-27
arXiv: 2201.11812v1
Abstract:
Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.
44. Can I trust my anomaly detection system? A case study based on explainable AI
Authors: Muhammad Rashid, Elvio Amparore, Enrico Ferrari, Damiano Verda
Categories: cs.LG, cs.AI
Published: 2024-07-29
arXiv: 2407.19951v1
Abstract:
Generative models based on variational autoencoders are a popular technique for detecting anomalies in images in a semi-supervised context. A common approach employs the anomaly score to detect the presence of anomalies, and it is known to reach high level of accuracy on benchmark datasets. However, since anomaly scores are computed from reconstruction disparities, they often obscure the detection of various spurious features, raising concerns regarding their actual efficacy. This case study explores the robustness of an anomaly detection system based on variational autoencoder generative models through the use of eXplainable AI methods. The goal is to get a different perspective on the real performances of anomaly detectors that use reconstruction differences. In our case study we discovered that, in many cases, samples are detected as anomalous for the wrong or misleading factors.
45. Towards Explainable AI Planning as a Service
Authors: Michael Cashmore, Anna Collins, Benjamin Krarup, Senka Krivic, Daniele Magazzeni, David Smith
Categories: cs.AI
Published: 2019-08-14
arXiv: 1908.05059v1
Abstract:
Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service – that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a prototype framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Planning as a service that identify several possible research directions.
46. A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks
Authors: Abhilash Singh, J. Amutha, Jaiprakash Nagar, Sandeep Sharma
Categories: cs.LG, cs.NI
Published: 2022-08-25
arXiv: 2208.11887v1
Abstract:
Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at the border areas and in the defense establishments. The border areas are stretched in hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that are able to identify and detect the enemy as soon as it comes in the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, the transmission range of sensors, and the number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.
47. Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Authors: Hongzhan Lin, Ziyang Luo, Wei Gao, Jing Ma, Bo Wang, Ruichao Yang
Categories: cs.CL, cs.AI
Published: 2024-01-24
arXiv: 2401.13298v1
Abstract:
The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to the implicit meaning embedded in memes, which is not explicitly conveyed through the surface text and image. However, existing harmful meme detection methods do not present readable explanations that unveil such implicit meaning to support their detection decisions. In this paper, we propose an explainable approach to detect harmful memes, achieved through reasoning over conflicting rationales from both harmless and harmful positions. Specifically, inspired by the powerful capacity of Large Language Models (LLMs) on text generation and reasoning, we first elicit multimodal debate between LLMs to generate the explanations derived from the contradictory arguments. Then we propose to fine-tune a small language model as the debate judge for harmfulness inference, to facilitate multimodal fusion between the harmfulness rationales and the intrinsic multimodal information within memes. In this way, our model is empowered to perform dialectical reasoning over intricate and implicit harm-indicative patterns, utilizing multimodal explanations originating from both harmless and harmful arguments. Extensive experiments on three public meme datasets demonstrate that our harmful meme detection approach achieves much better performance than state-of-the-art methods and exhibits a superior capacity for explaining the meme harmfulness of the model predictions.
48. Explainable AI And Visual Reasoning: Insights From Radiology
Authors: Robert Kaufman, David Kirsh
Categories: cs.HC, cs.AI
Published: 2023-04-06
arXiv: 2304.03318v1
Abstract:
Why do explainable AI (XAI) explanations in radiology, despite their promise of transparency, still fail to gain human trust? Current XAI approaches provide justification for predictions, however, these do not meet practitioners’ needs. These XAI explanations lack intuitive coverage of the evidentiary basis for a given classification, posing a significant barrier to adoption. We posit that XAI explanations that mirror human processes of reasoning and justification with evidence may be more useful and trustworthy than traditional visual explanations like heat maps. Using a radiology case study, we demonstrate how radiology practitioners get other practitioners to see a diagnostic conclusion’s validity. Machine-learned classifications lack this evidentiary grounding and consequently fail to elicit trust and adoption by potential users. Insights from this study may generalize to guiding principles for human-centered explanation design based on human reasoning and justification of evidence.
49. Towards The Ultimate Brain: Exploring Scientific Discovery with ChatGPT AI
Authors: Gerardo Adesso
Categories: cs.OH
Published: 2023-07-08
arXiv: 2308.12400v1
Abstract:
This paper presents a novel approach to scientific discovery using an artificial intelligence (AI) environment known as ChatGPT, developed by OpenAI. This is the first paper entirely generated with outputs from ChatGPT. We demonstrate how ChatGPT can be instructed through a gamification environment to define and benchmark hypothetical physical theories. Through this environment, ChatGPT successfully simulates the creation of a new improved model, called GPT$^4$, which combines the concepts of GPT in AI (generative pretrained transformer) and GPT in physics (generalized probabilistic theory). We show that GPT$^4$ can use its built-in mathematical and statistical capabilities to simulate and analyze physical laws and phenomena. As a demonstration of its language capabilities, GPT$^4$ also generates a limerick about itself. Overall, our results demonstrate the promising potential for human-AI collaboration in scientific discovery, as well as the importance of designing systems that effectively integrate AI’s capabilities with human intelligence.
50. Study on the Helpfulness of Explainable Artificial Intelligence
Authors: Tobias Labarta, Elizaveta Kulicheva, Ronja Froelian, Christian Geißler, Xenia Melman, Julian von Klitzing
Categories: cs.HC, cs.AI
Published: 2024-10-14
arXiv: 2410.11896v1
Abstract:
Explainable Artificial Intelligence (XAI) is essential for building advanced machine learning-powered applications, especially in critical domains such as medical diagnostics or autonomous driving. Legal, business, and ethical requirements motivate using effective XAI, but the increasing number of different methods makes it challenging to pick the right ones. Further, as explanations are highly context-dependent, measuring the effectiveness of XAI methods without users can only reveal a limited amount of information, excluding human factors such as the ability to understand it. We propose to evaluate XAI methods via the user’s ability to successfully perform a proxy task, designed such that a good performance is an indicator for the explanation to provide helpful information. In other words, we address the helpfulness of XAI for human decision-making. Further, a user study on state-of-the-art methods was conducted, showing differences in their ability to generate trust and skepticism and the ability to judge the rightfulness of an AI decision correctly. Based on the results, we highly recommend using and extending this approach for more objective-based human-centered user studies to measure XAI performance in an end-to-end fashion.