ADVENT: Attack/Anomaly Detection in VANETs

• URL: http://arxiv.org/abs/2401.08564v1
• Date: Tue, 16 Jan 2024 18:49:08 GMT
• Title: ADVENT: Attack/Anomaly Detection in VANETs
• Authors: Hamideh Baharlouei, Adetokunbo Makanju, Nur Zincir-Heywood
• Abstract summary: This study introduces a system for real-time detection of malicious behavior. By seamlessly integrating statistical and machine learning techniques, the proposed system prioritizes simplicity and efficiency. It excels in swiftly detecting attack onsets with a remarkable F1-score of 99.66%, subsequently identifying malicious vehicles with an average F1-score of approximately 97.85%.
• Score: 0.8594140167290099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the domain of Vehicular Ad hoc Networks (VANETs), where the imperative of having a real-world malicious detector capable of detecting attacks in real-time and unveiling their perpetrators is crucial, our study introduces a system with this goal. This system is designed for real-time detection of malicious behavior, addressing the critical need to first identify the onset of attacks and subsequently the responsible actors. Prior work in this area have never addressed both requirements, which we believe are necessary for real world deployment, simultaneously. By seamlessly integrating statistical and machine learning techniques, the proposed system prioritizes simplicity and efficiency. It excels in swiftly detecting attack onsets with a remarkable F1-score of 99.66%, subsequently identifying malicious vehicles with an average F1-score of approximately 97.85%. Incorporating federated learning in both stages enhances privacy and improves the efficiency of malicious node detection, effectively reducing the false negative rate.

Related papers

• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• WeiDetect: Weibull Distribution-Based Defense against Poisoning Attacks in Federated Learning for Network Intrusion Detection Systems [23.03944479383518]
  We propose WeiDetect, a two-phase, server-side defense mechanism for FL-based NIDS that detects malicious participants. We conducted experiments to evaluate the effectiveness of our approach in diverse attack settings. Our findings highlight that WeiDetect outperforms state-of-the-art defense approaches.
  arXiv  Detail & Related papers  (2025-04-06T05:31:24Z)
• Lie Detector: Unified Backdoor Detection via Cross-Examination Framework [68.45399098884364]
  We propose a unified backdoor detection framework in the semi-honest setting. Our method achieves superior detection performance, improving accuracy by 5.4%, 1.6%, and 11.9% over SoTA baselines. Notably, it is the first to effectively detect backdoors in multimodal large language models.
  arXiv  Detail & Related papers  (2025-03-21T06:12:06Z)
• Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection [15.146203784334086]
  We propose a generative active adaptation framework that minimizes labeling effort while enhancing model robustness. We evaluate our end-to-end framework on both simulated IDS data and a real-world ISP dataset. Our framework effectively enhances rare attack detection while reducing labeling costs, making it a scalable and adaptive solution for real-world intrusion detection.
  arXiv  Detail & Related papers  (2025-03-04T21:49:42Z)
• A Robust Multi-Stage Intrusion Detection System for In-Vehicle Network Security using Hierarchical Federated Learning [0.0]
  In-vehicle intrusion detection systems (IDSs) must detect seen attacks and provide a robust defense against new, unseen attacks. Previous work has relied solely on the CAN ID feature or has used traditional machine learning (ML) approaches with manual feature extraction. This paper introduces a cutting-edge, novel, lightweight, in-vehicle, IDS-leveraging, deep learning (DL) algorithm to address these limitations.
  arXiv  Detail & Related papers  (2024-08-15T21:51:56Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigation [15.803901489811318]
  NodLink is the first online detection system that maintains high detection accuracy without sacrificing detection granularity. We propose a novel design of in-memory cache, an efficient attack screening method, and a new approximation algorithm that is more efficient than the conventional one in APT attack detection.
  arXiv  Detail & Related papers  (2023-11-04T05:36:59Z)
• When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
  We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures. We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
  arXiv  Detail & Related papers  (2023-06-09T14:33:26Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• A Certifiable Security Patch for Object Tracking in Self-Driving Systems via Historical Deviation Modeling [22.753164675538457]
  We present the first systematic research on the security of object tracking in self-driving cars. We prove the mainstream multi-object tracker (MOT) based on Kalman Filter (KF) is unsafe even with an enabled multi-sensor fusion mechanism. We propose a simple yet effective security patch for KF-based MOT, the core of which is an adaptive strategy to balance the focus of KF on observations and predictions.
  arXiv  Detail & Related papers  (2022-07-18T12:30:24Z)
• ARLIF-IDS -- Attention augmented Real-Time Isolation Forest Intrusion Detection System [0.0]
  Internet of Things and Software Defined Networking leverage lightweight strategies for the early detection of DDoS attacks. It is essential to have a fast and effective security identification model based on low number of features. In this work, a novel Attention-based Isolation Forest Intrusion Detection System is proposed.
  arXiv  Detail & Related papers  (2022-04-20T18:40:23Z)
• Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios [3.236217153362305]
  We present an effective attack strategy aiming the objectness aspect of visual detection in autonomous vehicles. Experiments show that the proposed attack targeting the objectness aspect is 45.17% and 43.50% more effective than those generated from classification and/or localization losses. The proposed adversarial defense approach can improve the detectors' robustness against objectness-oriented attacks by up to 21% and 12% mAP on KITTI and COCO_traffic, respectively.
  arXiv  Detail & Related papers  (2022-02-10T00:47:36Z)
• Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition [79.50450766097686]
  This paper proposes the targeted attention attack (TAA) method for real world road sign attack. Experimental results validate that the TAA method improves the attack successful rate (nearly 10%) and reduces the perturbation loss (about a quarter) compared with the popular RP2 method.
  arXiv  Detail & Related papers  (2020-10-09T02:31:34Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.