survey-intrusion-2024
Query: intrusion detection survey 2024
Results: 50
Date: 2026-07-07T18:53:30.930Z
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. NTU-NPU System for Voice Privacy 2024 Challenge
Authors: Nikita Kuzmin, Hieu-Thi Luong, Jixun Yao, Lei Xie, Kong Aik Lee, Eng Siong Chng
Categories: eess.AS, cs.AI
Published: 2024-10-03
arXiv: 2410.02371v1
Abstract:
In this work, we describe our submissions for the Voice Privacy Challenge 2024. Rather than proposing a novel speech anonymization system, we enhance the provided baselines to meet all required conditions and improve evaluated metrics. Specifically, we implement emotion embedding and experiment with WavLM and ECAPA2 speaker embedders for the B3 baseline. Additionally, we compare different speaker and prosody anonymization techniques. Furthermore, we introduce Mean Reversion F0 for B5, which helps to enhance privacy without a loss in utility. Finally, we explore disentanglement models, namely $β$-VAE and NaturalSpeech3 FACodec.
10. Inclusion 2024 Global Multimedia Deepfake Detection Challenge: Towards Multi-dimensional Face Forgery Detection
Authors: Yi Zhang, Weize Gao, Changtao Miao, Man Luo, Jianshu Li, Wenzhong Deng, Zhe Li, Bingyu Hu, Weibin Yao, Yunfeng Diao, Wenbo Zhou, Tao Gong, Qi Chu
Categories: cs.CV, cs.MM
Published: 2024-12-30
arXiv: 2412.20833v2
Abstract:
In this paper, we present the Global Multimedia Deepfake Detection held concurrently with the Inclusion 2024. Our Multimedia Deepfake Detection aims to detect automatic image and audio-video manipulations including but not limited to editing, synthesis, generation, Photoshop,etc. Our challenge has attracted 1500 teams from all over the world, with about 5000 valid result submission counts. We invite the top 20 teams to present their solutions to the challenge, from which the top 3 teams are awarded prizes in the grand finale. In this paper, we present the solutions from the top 3 teams of the two tracks, to boost the research work in the field of image and audio-video forgery detection. The methodologies developed through the challenge will contribute to the development of next-generation deepfake detection systems and we encourage participants to open source their methods.
11. Overview of the 2024 ALTA Shared Task: Detect Automatic AI-Generated Sentences for Human-AI Hybrid Articles
Authors: Diego Mollá, Qiongkai Xu, Zijie Zeng, Zhuang Li
Categories: cs.CL
Published: 2024-12-19
arXiv: 2412.17848v1
Abstract:
The ALTA shared tasks have been running annually since 2010. In 2024, the purpose of the task is to detect machine-generated text in a hybrid setting where the text may contain portions of human text and portions machine-generated. In this paper, we present the task, the evaluation criteria, and the results of the systems participating in the shared task.
12. 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.
13. Atmospheric entry and fragmentation of small asteroid 2024 BX1: Bolide trajectory, orbit, dynamics, light curve, and spectrum
Authors: P. Spurny, J. Borovicka, L. Shrbeny, M. Hankey, R. Neubert
Categories: astro-ph.EP
Published: 2024-03-01
arXiv: 2403.00634v2
Abstract:
Asteroid 2024 BX1 was the eighth asteroid that was discovered shortly before colliding with the Earth. The associated bolide was recorded by dedicated instruments of the European Fireball Network and the AllSky7 network on 2024 January 21 at 0:32:38-44 UT. We report a comprehensive analysis of this instrumentally observed meteorite fall, which occurred as predicted west of Berlin, Germany. The atmospheric trajectory was quite steep, with an average slope to the Earth’s surface of 75.6 deg. The entry speed was 15.20 km/s. The heliocentric orbit calculated from the bolide data agrees very well with the asteroid data. However, the bolide was fainter than expected for a reportedly meter-sized asteroid. The absolute magnitude reached -14.4, and the entry mass was estimated to be 140 kg. The recorded bolide spectrum was low in iron, based on which, the meteorite was expected to be rich in enstatite. The recovered meteorites, called Ribbeck, were classified as aubrites. The high albedo of enstatite (E-type) asteroids can explain the size discrepancy. The asteroid was likely smaller than 0.5 meter and should rather be called a meteoroid. During the atmospheric entry, the meteoroid severely fragmented into much smaller pieces already at a height of 55 km under an aerodynamic pressure of 0.12 MPa. The primary fragments then broke up again, most frequently at heights 39-29 km (0.9-2.2 MPa). Numerous small meteorites and up to four stones larger than 100g were expected to land. Within a few days of publication of the location of the strewn field, dozens of meteorites were found in the area we had predicted.
14. 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.
15. Discovery Opportunities with Gravitational Waves – TASI 2024 Lecture Notes
Authors: Valerie Domcke
Categories: astro-ph.CO, gr-qc, hep-ph
Published: 2024-09-13
arXiv: 2409.08956v1
Abstract:
Recent advancements in gravitational wave astronomy hold the promise of a completely new way to explore our Universe. These lecture notes aim to provide a concise but self-contained introduction to key concepts of gravitational wave physics, with a focus on the opportunities to explore fundamental physics in transient gravitational wave signals and stochastic gravitational wave background searches.CERN-TH-2024-152
16. Enhanced Object Detection: A Study on Vast Vocabulary Object Detection Track for V3Det Challenge 2024
Authors: Peixi Wu, Bosong Chai, Xuan Nie, Longquan Yan, Zeyu Wang, Qifan Zhou, Boning Wang, Yansong Peng, Hebei Li
Categories: cs.CV
Published: 2024-06-13
arXiv: 2406.09201v3
Abstract:
In this technical report, we present our findings from the research conducted on the Vast Vocabulary Visual Detection (V3Det) dataset for Supervised Vast Vocabulary Visual Detection task. How to deal with complex categories and detection boxes has become a difficulty in this track. The original supervised detector is not suitable for this task. We have designed a series of improvements, including adjustments to the network structure, changes to the loss function, and design of training strategies. Our model has shown improvement over the baseline and achieved excellent rankings on the Leaderboard for both the Vast Vocabulary Object Detection (Supervised) track and the Open Vocabulary Object Detection (OVD) track of the V3Det Challenge 2024.
17. 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.
18. 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.
19. Uncovering Coordinated Cross-Platform Information Operations Threatening the Integrity of the 2024 U.S. Presidential Election Online Discussion
Authors: Marco Minici, Luca Luceri, Federico Cinus, Emilio Ferrara
Categories: cs.SI, cs.CY
Published: 2024-09-23
arXiv: 2409.15402v2
Abstract:
Information Operations (IOs) pose a significant threat to the integrity of democratic processes, with the potential to influence election-related online discourse. In anticipation of the 2024 U.S. presidential election, we present a study aimed at uncovering the digital traces of coordinated IOs on $\mathbb{X}$ (formerly Twitter). Using our machine learning framework for detecting online coordination, we analyze a dataset comprising election-related conversations on $\mathbb{X}$ from May 2024. This reveals a network of coordinated inauthentic actors, displaying notable similarities in their link-sharing behaviors. Our analysis shows concerted efforts by these accounts to disseminate misleading, redundant, and biased information across the Web through a coordinated cross-platform information operation: The links shared by this network frequently direct users to other social media platforms or suspicious websites featuring low-quality political content and, in turn, promoting the same $\mathbb{X}$ and YouTube accounts. Members of this network also shared deceptive images generated by AI, accompanied by language attacking political figures and symbolic imagery intended to convey power and dominance. While $\mathbb{X}$ has suspended a subset of these accounts, more than 75% of the coordinated network remains active. Our findings underscore the critical role of developing computational models to scale up the detection of threats on large social media platforms, and emphasize the broader implications of these techniques to detect IOs across the wider Web.
20. 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.
21. Double Multi-Head Attention Multimodal System for Odyssey 2024 Speech Emotion Recognition Challenge
Authors: Federico Costa, Miquel India, Javier Hernando
Categories: eess.AS, cs.SD
Published: 2024-06-15
arXiv: 2406.10598v1
Abstract:
As computer-based applications are becoming more integrated into our daily lives, the importance of Speech Emotion Recognition (SER) has increased significantly. Promoting research with innovative approaches in SER, the Odyssey 2024 Speech Emotion Recognition Challenge was organized as part of the Odyssey 2024 Speaker and Language Recognition Workshop. In this paper we describe the Double Multi-Head Attention Multimodal System developed for this challenge. Pre-trained self-supervised models were used to extract informative acoustic and text features. An early fusion strategy was adopted, where a Multi-Head Attention layer transforms these mixed features into complementary contextualized representations. A second attention mechanism is then applied to pool these representations into an utterance-level vector. Our proposed system achieved the third position in the categorical task ranking with a 34.41% Macro-F1 score, where 31 teams participated in total.
22. 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.
23. ICAGC 2024: Inspirational and Convincing Audio Generation Challenge 2024
Authors: Ruibo Fu, Rui Liu, Chunyu Qiang, Yingming Gao, Yi Lu, Shuchen Shi, Tao Wang, Ya Li, Zhengqi Wen, Chen Zhang, Hui Bu, Yukun Liu, Xin Qi, Guanjun Li
Categories: eess.AS, cs.AI
Published: 2024-07-01
arXiv: 2407.12038v2
Abstract:
The Inspirational and Convincing Audio Generation Challenge 2024 (ICAGC 2024) is part of the ISCSLP 2024 Competitions and Challenges track. While current text-to-speech (TTS) technology can generate high-quality audio, its ability to convey complex emotions and controlled detail content remains limited. This constraint leads to a discrepancy between the generated audio and human subjective perception in practical applications like companion robots for children and marketing bots. The core issue lies in the inconsistency between high-quality audio generation and the ultimate human subjective experience. Therefore, this challenge aims to enhance the persuasiveness and acceptability of synthesized audio, focusing on human alignment convincing and inspirational audio generation. A total of 19 teams have registered for the challenge, and the results of the competition and the competition are described in this paper.
24. 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.
25. 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.
26. A Survey on Cross-Domain Sequential Recommendation
Authors: Shu Chen, Zitao Xu, Weike Pan, Qiang Yang, Zhong Ming
Categories: cs.IR
Published: 2024-01-10
arXiv: 2401.04971v4
Abstract:
Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain). In this survey, we first define the CDSR problem using a four-dimensional tensor and then analyze its multi-type input representations under multidirectional dimensionality reductions. Following that, we provide a systematic overview from both macro and micro views. From a macro view, we abstract the multi-level fusion structures of various models across domains and discuss their bridges for fusion. From a micro view, focusing on the existing models, we first discuss the basic technologies and then explain the auxiliary learning technologies. Finally, we exhibit the available public datasets and the representative experimental results as well as provide some insights into future directions for research in CDSR.
27. NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results
Authors: Zheng Chen, Zongwei Wu, Eduard Zamfir, Kai Zhang, Yulun Zhang, Radu Timofte, Xiaokang Yang, Hongyuan Yu, Cheng Wan, Yuxin Hong, Zhijuan Huang, Yajun Zou, Yuan Huang, Jiamin Lin, Bingnan Han, Xianyu Guan, Yongsheng Yu, Daoan Zhang, Xuanwu Yin, Kunlong Zuo, Jinhua Hao, Kai Zhao, Kun Yuan, Ming Sun, Chao Zhou, Hongyu An, Xinfeng Zhang, Zhiyuan Song, Ziyue Dong, Qing Zhao, Xiaogang Xu, Pengxu Wei, Zhi-chao Dou, Gui-ling Wang, Chih-Chung Hsu, Chia-Ming Lee, Yi-Shiuan Chou, Cansu Korkmaz, A. Murat Tekalp, Yubin Wei, Xiaole Yan, Binren Li, Haonan Chen, Siqi Zhang, Sihan Chen, Amogh Joshi, Nikhil Akalwadi, Sampada Malagi, Palani Yashaswini, Chaitra Desai, Ramesh Ashok Tabib, Ujwala Patil, Uma Mudenagudi, Anjali Sarvaiya, Pooja Choksy, Jagrit Joshi, Shubh Kawa, Kishor Upla, Sushrut Patwardhan, Raghavendra Ramachandra, Sadat Hossain, Geongi Park, S. M. Nadim Uddin, Hao Xu, Yanhui Guo, Aman Urumbekov, Xingzhuo Yan, Wei Hao, Minghan Fu, Isaac Orais, Samuel Smith, Ying Liu, Wangwang Jia, Qisheng Xu, Kele Xu, Weijun Yuan, Zhan Li, Wenqin Kuang, Ruijin Guan, Ruting Deng, Zhao Zhang, Bo Wang, Suiyi Zhao, Yan Luo, Yanyan Wei, Asif Hussain Khan, Christian Micheloni, Niki Martinel
Categories: cs.CV
Published: 2024-04-15
arXiv: 2404.09790v2
Abstract:
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained. The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs using prior information. The LR images originate from bicubic downsampling degradation. The aim of the challenge is to obtain designs/solutions with the most advanced SR performance, with no constraints on computational resources (e.g., model size and FLOPs) or training data. The track of this challenge assesses performance with the PSNR metric on the DIV2K testing dataset. The competition attracted 199 registrants, with 20 teams submitting valid entries. This collective endeavour not only pushes the boundaries of performance in single-image SR but also offers a comprehensive overview of current trends in this field.
28. AIn’t Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation
Authors: Leah von der Heyde, Anna-Carolina Haensch, Bernd Weiß, Jessica Daikeler
Categories: cs.CL, cs.AI, cs.CY
Published: 2025-06-17
arXiv: 2506.14634v3
Abstract:
The recent development and wider accessibility of LLMs have spurred discussions about how they can be used in survey research, including classifying open-ended survey responses. Due to their linguistic capacities, it is possible that LLMs are an efficient alternative to time-consuming manual coding and the pre-training of supervised machine learning models. As most existing research on this topic has focused on English-language responses relating to non-complex topics or on single LLMs, it is unclear whether its findings generalize and how the quality of these classifications compares to established methods. In this study, we investigate to what extent different LLMs can be used to code open-ended survey responses in other contexts, using German data on reasons for survey participation as an example. We compare several state-of-the-art LLMs and several prompting approaches, and evaluate the LLMs’ performance by using human expert codings. Overall performance differs greatly between LLMs, and only a fine-tuned LLM achieves satisfactory levels of predictive performance. Performance differences between prompting approaches are conditional on the LLM used. Finally, LLMs’ unequal classification performance across different categories of reasons for survey participation results in different categorical distributions when not using fine-tuning. We discuss the implications of these findings, both for methodological research on coding open-ended responses and for their substantive analysis, and for practitioners processing or substantively analyzing such data. Finally, we highlight the many trade-offs researchers need to consider when choosing automated methods for open-ended response classification in the age of LLMs. In doing so, our study contributes to the growing body of research about the conditions under which LLMs can be efficiently, accurately, and reliably leveraged in survey research.
29. OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation
Authors: Włodzimierz Lewoniewski, Piotr Stolarski, Milena Stróżyna, Elzbieta Lewańska, Aleksandra Wojewoda, Ewelina Księżniak, Marcin Sawiński
Categories: cs.CL, cs.AI
Published: 2024-09-04
arXiv: 2409.02649v2
Abstract:
This paper presents the experiments and results for the CheckThat! Lab at CLEF 2024 Task 6: Robustness of Credibility Assessment with Adversarial Examples (InCrediblAE). The primary objective of this task was to generate adversarial examples in five problem domains in order to evaluate the robustness of widely used text classification methods (fine-tuned BERT, BiLSTM, and RoBERTa) when applied to credibility assessment issues. This study explores the application of ensemble learning to enhance adversarial attacks on natural language processing (NLP) models. We systematically tested and refined several adversarial attack methods, including BERT-Attack, Genetic algorithms, TextFooler, and CLARE, on five datasets across various misinformation tasks. By developing modified versions of BERT-Attack and hybrid methods, we achieved significant improvements in attack effectiveness. Our results demonstrate the potential of modification and combining multiple methods to create more sophisticated and effective adversarial attack strategies, contributing to the development of more robust and secure systems.
30. Proceedings of 6th International Conference AsiaHaptics 2024
Authors: Yasutoshi Makino, Hsin-Ni Ho, Seokhee Jeon
Categories: cs.HC
Published: 2024-11-13
arXiv: 2411.08318v1
Abstract:
The sixth international conference AsiaHaptics 2024 took place at Sunway University, Malaysia on 28-30 October 2024. AsiaHaptics is an exhibition type of international conference dedicated to the haptics domain, engaging presentations accompanied by hands-on demonstrations. It presents the state-of-the-art of the diverse haptics (touch)-related research, including perception and illusion, development of haptics devices, and applications to a wide variety of fields such as education, medicine, telecommunication, navigation and entertainment. This proceedings volume is a valuable resource not only for active haptics researchers, but also for general readers wishing to understand the status quo in this interdisciplinary area of science and technology.
31. SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge Evaluation Plan
Authors: You Zhang, Yongyi Zang, Jiatong Shi, Ryuichi Yamamoto, Jionghao Han, Yuxun Tang, Tomoki Toda, Zhiyao Duan
Categories: eess.AS, cs.AI, cs.MM, cs.SD
Published: 2024-05-08
arXiv: 2405.05244v1
Abstract:
The rapid advancement of AI-generated singing voices, which now closely mimic natural human singing and align seamlessly with musical scores, has led to heightened concerns for artists and the music industry. Unlike spoken voice, singing voice presents unique challenges due to its musical nature and the presence of strong background music, making singing voice deepfake detection (SVDD) a specialized field requiring focused attention. To promote SVDD research, we recently proposed the “SVDD Challenge,” the very first research challenge focusing on SVDD for lab-controlled and in-the-wild bonafide and deepfake singing voice recordings. The challenge will be held in conjunction with the 2024 IEEE Spoken Language Technology Workshop (SLT 2024).
32. BrainStorm @ iREL at #SMM4H 2024: Leveraging Translation and Topical Embeddings for Annotation Detection in Tweets
Authors: Manav Chaudhary, Harshit Gupta, Vasudeva Varma
Categories: cs.CL, cs.SI
Published: 2024-05-18
arXiv: 2405.11192v2
Abstract:
The proliferation of LLMs in various NLP tasks has sparked debates regarding their reliability, particularly in annotation tasks where biases and hallucinations may arise. In this shared task, we address the challenge of distinguishing annotations made by LLMs from those made by human domain experts in the context of COVID-19 symptom detection from tweets in Latin American Spanish. This paper presents BrainStorm @ iRELs approach to the SMM4H 2024 Shared Task, leveraging the inherent topical information in tweets, we propose a novel approach to identify and classify annotations, aiming to enhance the trustworthiness of annotated data.
33. Team HYU ASML ROBOVOX SP Cup 2024 System Description
Authors: Jeong-Hwan Choi, Gaeun Kim, Hee-Jae Lee, Seyun Ahn, Hyun-Soo Kim, Joon-Hyuk Chang
Categories: eess.AS
Published: 2024-07-16
arXiv: 2407.11365v1
Abstract:
This report describes the submission of HYU ASML team to the IEEE Signal Processing Cup 2024 (SP Cup 2024). This challenge, titled “ROBOVOX: Far-Field Speaker Recognition by a Mobile Robot,” focuses on speaker recognition using a mobile robot in noisy and reverberant conditions. Our solution combines the result of deep residual neural networks and time-delay neural network-based speaker embedding models. These models were trained on a diverse dataset that includes French speech. To account for the challenging evaluation environment characterized by high noise, reverberation, and short speech conditions, we focused on data augmentation and training speech duration for the speaker embedding model. Our submission achieved second place on the SP Cup 2024 public leaderboard, with a detection cost function of 0.5245 and an equal error rate of 6.46%.
34. On The Lunar Origin of Near-Earth Asteroid 2024 PT5
Authors: Theodore Kareta, Oscar Fuentes-Muñoz, Nicholas Moskovitz, Davide Farnocchia, Benjamin N. L. Sharkey
Categories: astro-ph.EP
Published: 2024-12-13
arXiv: 2412.10264v1
Abstract:
The Near-Earth Asteroid (NEA) 2024 PT5 is on an Earth-like orbit which remained in Earth’s immediate vicinity for several months at the end of 2024. PT5’s orbit is challenging to populate with asteroids originating from the Main Belt and is more commonly associated with rocket bodies mistakenly identified as natural objects or with debris ejected from impacts on the Moon. We obtained visible and near-infrared reflectance spectra of PT5 with the Lowell Discovery Telescope and NASA Infrared Telescope Facility on 2024 August 16. The combined reflectance spectrum matches lunar samples but does not match any known asteroid types – it is pyroxene-rich while asteroids of comparable spectral redness are olivine-rich. Moreover, the amount of solar radiation pressure observed on the PT5 trajectory is orders of magnitude lower than what would be expected for an artificial object. We therefore conclude that 2024 PT5 is ejecta from an impact on the Moon, thus making PT5 the second NEA suggested to be sourced from the surface of the Moon. While one object might be an outlier, two suggest that there is an underlying population to be characterized. Long-term predictions of the position of 2024 PT5 are challenging due to the slow Earth encounters characteristic of objects in these orbits. A population of near-Earth objects which are sourced by the Moon would be important to characterize for understanding how impacts work on our nearest neighbor and for identifying the source regions of asteroids and meteorites from this under-studied population of objects on very Earth-like orbits.
35. 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.
36. Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey
Authors: Nicolas Chahine, Marcos V. Conde, Daniela Carfora, Gabriel Pacianotto, Benoit Pochon, Sira Ferradans, Radu Timofte
Categories: cs.CV
Published: 2024-04-17
arXiv: 2404.11159v1
Abstract:
This paper reviews the NTIRE 2024 Portrait Quality Assessment Challenge, highlighting the proposed solutions and results. This challenge aims to obtain an efficient deep neural network capable of estimating the perceptual quality of real portrait photos. The methods must generalize to diverse scenes and diverse lighting conditions (indoor, outdoor, low-light), movement, blur, and other challenging conditions. In the challenge, 140 participants registered, and 35 submitted results during the challenge period. The performance of the top 5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in Portrait Quality Assessment.
37. NPU-NTU System for Voice Privacy 2024 Challenge
Authors: Jixun Yao, Nikita Kuzmin, Qing Wang, Pengcheng Guo, Ziqian Ning, Dake Guo, Kong Aik Lee, Eng-Siong Chng, Lei Xie
Categories: eess.AS
Published: 2024-09-06
arXiv: 2409.04173v2
Abstract:
Speaker anonymization is an effective privacy protection solution that conceals the speaker’s identity while preserving the linguistic content and paralinguistic information of the original speech. To establish a fair benchmark and facilitate comparison of speaker anonymization systems, the VoicePrivacy Challenge (VPC) was held in 2020 and 2022, with a new edition planned for 2024. In this paper, we describe our proposed speaker anonymization system for VPC 2024. Our system employs a disentangled neural codec architecture and a serial disentanglement strategy to gradually disentangle the global speaker identity and time-variant linguistic content and paralinguistic information. We introduce multiple distillation methods to disentangle linguistic content, speaker identity, and emotion. These methods include semantic distillation, supervised speaker distillation, and frame-level emotion distillation. Based on these distillations, we anonymize the original speaker identity using a weighted sum of a set of candidate speaker identities and a randomly generated speaker identity. Our system achieves the best trade-off of privacy protection and emotion preservation in VPC 2024.
38. The Methanol Multibeam Survey
Authors: J. A. Green, the Methanol Multibeam Survey Collaboration
Categories: astro-ph.IM, astro-ph.GA
Published: 2012-10-03
arXiv: 1210.0979v1
Abstract:
A purpose built 7-beam methanol receiver, installed on the Parkes Radio Telescope, was used to survey the Galactic plane for newly forming high mass stars, pinpointed by strong methanol maser emission at 6.7 GHz. The Methanol Multibeam (MMB) survey observed over 60% of the Galactic plane, detecting close to 1000 sources. The MMB survey provides a huge resource for studies of high-mass star formation, an important stage in the evolution of the interstellar medium. The MMB survey is also a valuable resource for investigations into the structure and dynamics of our Galaxy: with narrow velocity ranges of emission (typically only a few km/s) and velocities closely correlated with the systemic velocity of their surrounding molecular clouds, 6.7-GHz methanol masers provide estimates of the spiral arm velocities and their structure. I will discuss the techniques and properties of the MMB survey, before outlining recent results, which include the identification of regions of enhanced star formation believed to be indicative of the origins of the spiral arms and the interaction of the Galactic bar with the 3-kpc arms. I will also discuss the various follow-up programmes including a study of magnetic fields through associated hydroxyl masers.
39. 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.
40. 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.
41. Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text
Authors: Seyedeh Fatemeh Ebrahimi, Karim Akhavan Azari, Amirmasoud Iravani, Arian Qazvini, Pouya Sadeghi, Zeinab Sadat Taghavi, Hossein Sameti
Categories: cs.CL, cs.AI
Published: 2024-07-16
arXiv: 2407.11774v1
Abstract:
Detecting Machine-Generated Text (MGT) has emerged as a significant area of study within Natural Language Processing. While language models generate text, they often leave discernible traces, which can be scrutinized using either traditional feature-based methods or more advanced neural language models. In this research, we explore the effectiveness of fine-tuning a RoBERTa-base transformer, a powerful neural architecture, to address MGT detection as a binary classification task. Focusing specifically on Subtask A (Monolingual-English) within the SemEval-2024 competition framework, our proposed system achieves an accuracy of 78.9% on the test dataset, positioning us at 57th among participants. Our study addresses this challenge while considering the limited hardware resources, resulting in a system that excels at identifying human-written texts but encounters challenges in accurately discerning MGTs.
42. Speech Foundation Model Ensembles for the Controlled Singing Voice Deepfake Detection (CtrSVDD) Challenge 2024
Authors: Anmol Guragain, Tianchi Liu, Zihan Pan, Hardik B. Sailor, Qiongqiong Wang
Categories: eess.AS, cs.AI, cs.SD
Published: 2024-09-03
arXiv: 2409.02302v1
Abstract:
This work details our approach to achieving a leading system with a 1.79% pooled equal error rate (EER) on the evaluation set of the Controlled Singing Voice Deepfake Detection (CtrSVDD). The rapid advancement of generative AI models presents significant challenges for detecting AI-generated deepfake singing voices, attracting increased research attention. The Singing Voice Deepfake Detection (SVDD) Challenge 2024 aims to address this complex task. In this work, we explore the ensemble methods, utilizing speech foundation models to develop robust singing voice anti-spoofing systems. We also introduce a novel Squeeze-and-Excitation Aggregation (SEA) method, which efficiently and effectively integrates representation features from the speech foundation models, surpassing the performance of our other individual systems. Evaluation results confirm the efficacy of our approach in detecting deepfake singing voices. The codes can be accessed at https://github.com/Anmol2059/SVDD2024.
43. Roman Galactic Plane Survey Definition Committee Report
Authors: Roman Galactic Plane Survey Definition Committee
Categories: astro-ph.GA, astro-ph.SR
Published: 2025-11-10
arXiv: 2511.07494v1
Abstract:
The Roman Galactic Plane Survey (RGPS) is a 700-hour program approved for early definition as a community-designed General Astrophysics Survey. It was selected following a proposal call for science programs that would benefit from an early community-based definition (Sanderson et al 2024). The community was invited to submit white papers and science pitches with a deadline of May 20, 2024; the Roman Galactic Plane Survey Definition Committee (RGPS-DC) first met on Sep 11, 2024. Based on the input provided, the RGPS-DC recommends a survey consisting of three elements: (1) a wide-field science element (691 sq deg, 541 hrs) covering the Galactic plane, Galactic latitude |b|<2 deg and Galactic longitude l=+50.1 deg to -79 deg (281 deg), in four filters (F129, F159, F184, and F213) with higher latitude extensions for the bulge, the Serpens South/W40 star formation region, and Carina, (2) a time-domain science element (19 sq deg , 130 hrs) of six fields, including the full Nuclear Stellar Disk (NSD) and Central Molecular Zone (CMZ), with coverage in seven filters and repeat observations in one or more filters with cadences from 11 minutes to weeks, and (3) a deep-field/spectroscopic science element (4 sq deg , 30 hrs) consisting of fifteen Roman pointings (with a wide range of extinction, diffuse emission, stellar density and population) using longer exposure times in seven filters in addition to grism and prism observations. This document summarizes the science that can be done with this survey, the process of survey definition, and details on all of the program elements.
44. Grounding and Evaluation for Large Language Models: Practical Challenges and Lessons Learned (Survey)
Authors: Krishnaram Kenthapadi, Mehrnoosh Sameki, Ankur Taly
Categories: cs.CL, cs.AI, cs.CV, cs.LG
Published: 2024-07-10
arXiv: 2407.12858v1
Abstract:
With the ongoing rapid adoption of Artificial Intelligence (AI)-based systems in high-stakes domains, ensuring the trustworthiness, safety, and observability of these systems has become crucial. It is essential to evaluate and monitor AI systems not only for accuracy and quality-related metrics but also for robustness, bias, security, interpretability, and other responsible AI dimensions. We focus on large language models (LLMs) and other generative AI models, which present additional challenges such as hallucinations, harmful and manipulative content, and copyright infringement. In this survey article accompanying our KDD 2024 tutorial, we highlight a wide range of harms associated with generative AI systems, and survey state of the art approaches (along with open challenges) to address these harms.
45. FMSG-JLESS Submission for DCASE 2024 Task4 on Sound Event Detection with Heterogeneous Training Dataset and Potentially Missing Labels
Authors: Yang Xiao, Han Yin, Jisheng Bai, Rohan Kumar Das
Categories: eess.AS, cs.SD
Published: 2024-06-29
arXiv: 2407.00291v1
Abstract:
This report presents the systems developed and submitted by Fortemedia Singapore (FMSG) and Joint Laboratory of Environmental Sound Sensing (JLESS) for DCASE 2024 Task 4. The task focuses on recognizing event classes and their time boundaries, given that multiple events can be present and may overlap in an audio recording. The novelty this year is a dataset with two sources, making it challenging to achieve good performance without knowing the source of the audio clips during evaluation. To address this, we propose a sound event detection method using domain generalization. Our approach integrates features from bidirectional encoder representations from audio transformers and a convolutional recurrent neural network. We focus on three main strategies to improve our method. First, we apply mixstyle to the frequency dimension to adapt the mel-spectrograms from different domains. Second, we consider training loss of our model specific to each datasets for their corresponding classes. This independent learning framework helps the model extract domain-specific features effectively. Lastly, we use the sound event bounding boxes method for post-processing. Our proposed method shows superior macro-average pAUC and polyphonic SED score performance on the DCASE 2024 Challenge Task 4 validation dataset and public evaluation dataset.
46. 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.
47. cantnlp@LT-EDI-2024: Automatic Detection of Anti-LGBTQ+ Hate Speech in Under-resourced Languages
Authors: Sidney G. -J. Wong, Matthew Durward
Categories: cs.CL
Published: 2024-01-28
arXiv: 2401.15777v1
Abstract:
This paper describes our homophobia/transphobia in social media comments detection system developed as part of the shared task at LT-EDI-2024. We took a transformer-based approach to develop our multiclass classification model for ten language conditions (English, Spanish, Gujarati, Hindi, Kannada, Malayalam, Marathi, Tamil, Tulu, and Telugu). We introduced synthetic and organic instances of script-switched language data during domain adaptation to mirror the linguistic realities of social media language as seen in the labelled training data. Our system ranked second for Gujarati and Telugu with varying levels of performance for other language conditions. The results suggest incorporating elements of paralinguistic behaviour such as script-switching may improve the performance of language detection systems especially in the cases of under-resourced languages conditions.
48. 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.
49. IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes
Authors: Shreenaga Chikoti, Shrey Mehta, Ashutosh Modi
Categories: cs.CL, cs.AI, cs.LG
Published: 2024-04-06
arXiv: 2404.04520v1
Abstract:
Memes are one of the most popular types of content used in an online disinformation campaign. They are primarily effective on social media platforms since they can easily reach many users. Memes in a disinformation campaign achieve their goal of influencing the users through several rhetorical and psychological techniques, such as causal oversimplification, name-calling, and smear. The SemEval 2024 Task 4 \textit{Multilingual Detection of Persuasion Technique in Memes} on identifying such techniques in the memes is divided across three sub-tasks: ($\mathbf{1}$) Hierarchical multi-label classification using only textual content of the meme, ($\mathbf{2}$) Hierarchical multi-label classification using both, textual and visual content of the meme and ($\mathbf{3}$) Binary classification of whether the meme contains a persuasion technique or not using it’s textual and visual content. This paper proposes an ensemble of Class Definition Prediction (CDP) and hyperbolic embeddings-based approaches for this task. We enhance meme classification accuracy and comprehensiveness by integrating HypEmo’s hierarchical label embeddings (Chen et al., 2023) and a multi-task learning framework for emotion prediction. We achieve a hierarchical F1-score of 0.60, 0.67, and 0.48 on the respective sub-tasks.
50. HYBRINFOX at CheckThat! 2024 – Task 2: Enriching BERT Models with the Expert System VAGO for Subjectivity Detection
Authors: Morgane Casanova, Julien Chanson, Benjamin Icard, Géraud Faye, Guillaume Gadek, Guillaume Gravier, Paul Égré
Categories: cs.CL, cs.AI
Published: 2024-07-04
arXiv: 2407.03770v1
Abstract:
This paper presents the HYBRINFOX method used to solve Task 2 of Subjectivity detection of the CLEF 2024 CheckThat! competition. The specificity of the method is to use a hybrid system, combining a RoBERTa model, fine-tuned for subjectivity detection, a frozen sentence-BERT (sBERT) model to capture semantics, and several scores calculated by the English version of the expert system VAGO, developed independently of this task to measure vagueness and subjectivity in texts based on the lexicon. In English, the HYBRINFOX method ranked 1st with a macro F1 score of 0.7442 on the evaluation data. For the other languages, the method used a translation step into English, producing more mixed results (ranking 1st in Multilingual and 2nd in Italian over the baseline, but under the baseline in Bulgarian, German, and Arabic). We explain the principles of our hybrid approach, and outline ways in which the method could be improved for other languages besides English.