Behavioral Residualization for Unsupervised Intrusion Detection in Automotive CAN Networks

• URL: http://arxiv.org/abs/2608.05548v1
• Date: Thu, 06 Aug 2026 03:03:28 GMT
• Title: Behavioral Residualization for Unsupervised Intrusion Detection in Automotive CAN Networks
• Abstract summary: We present per-ID behavioral residualization, a CAN-specific representation that extracts temporal, protocol, and payload features from sliding windows.<n>Our central claim is that this representation, rather than any individual detector, drives the performance gains.<n>On the more realistic ROAD dataset, where attacks reuse legitimate IDs, the representation achieves recall >= 0.99 with high ROC-AUC on targeted signal-manipulation attacks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern vehicles rely on the Controller Area Network (CAN) bus, whose design prioritizes low cost and real-time performance but provides no message authentication or encryption. An attacker with physical or remote access can therefore inject arbitrary frames, making intrusion detection an important defense-in-depth mechanism. Most published CAN intrusion detection systems rely on presence-based features, such as novel arbitration IDs, frozen payload bytes, or anomalous DLC values. These features perform well on public datasets containing easily separable attacks but fail when attackers reuse legitimate arbitration IDs. We present per-ID behavioral residualization, a CAN-specific representation that extracts fourteen temporal, protocol, and payload features from sliding windows and residualizes them against each arbitration ID's normal baseline. Our central claim is that this representation, rather than any individual detector, drives the performance gains. Across six unsupervised detectors and two datasets, residualization improves mean F1 in the majority of evaluations (21/24 on HCRL and 30/36 on ROAD across five seeds). On the more realistic ROAD dataset, where attacks reuse legitimate IDs, the representation achieves recall >= 0.99 with high ROC-AUC on targeted signal-manipulation attacks. Two limitations are explicitly quantified: novel-ID flooding (HCRL DoS, F1 = 0.02) and cross-ID fuzzing (ROAD, F1 = 0.27), defining the measured coverage boundary of the proposed representation.

Related papers

• Detecting Adversarial Evasion Attacks Against Autoencoder-Based Network Intrusion Detection Systems [0.0]
  Evasion attacks deliberately manipulate input to an ML-based system to produce an incorrect prediction while the manipulated input still appears benign.<n>In this paper, we propose two complementary detectors: the Residual Localisation Detector (RLD) and the Feature-Space Perturbation Consistency (FPC) Detector.<n>We evaluate both detectors on benign, malicious, and adversarial traffic from multiple IoT devices in the UQ-IoT dataset.
  arXiv  Detail & Related papers  (2026-07-01T17:26:24Z)
• QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection [60.105690843777666]
  We show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features.<n>Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion.
  arXiv  Detail & Related papers  (2026-04-17T08:53:24Z)
• 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)
• 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)
• Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs [13.581341206178525]
  This paper presents an unsupervised-learning-based convolutional autoencoder architecture for detecting zero-day attacks. We quantise the model using Vitis-AI tools from AMD/Xilinx targeting a resource-constrained Zynq Ultrascale platform. The proposed model successfully achieves equal or higher classification accuracy (> 99.5%) on unseen DoS, fuzzing, and spoofing attacks.
  arXiv  Detail & Related papers  (2024-01-19T14:36:01Z)
• Malicious Agent Detection for Robust Multi-Agent Collaborative Perception [52.261231738242266]
  Multi-agent collaborative (MAC) perception is more vulnerable to adversarial attacks than single-agent perception. We propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception. We conduct comprehensive evaluations on a benchmark 3D dataset V2X-sim and a real-road dataset DAIR-V2X.
  arXiv  Detail & Related papers  (2023-10-18T11:36:42Z)
• Spatial-Frequency Discriminability for Revealing Adversarial Perturbations [53.279716307171604]
  Vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. Current algorithms typically detect adversarial patterns through discriminative decomposition for natural and adversarial data. We propose a discriminative detector relying on a spatial-frequency Krawtchouk decomposition.
  arXiv  Detail & Related papers  (2023-05-18T10:18:59Z)
• Detecting CAN Masquerade Attacks with Signal Clustering Similarity [2.2881898195409884]
  Fabrication attacks are the easiest to administer and the easiest to detect because they disrupt frame frequency. masquerade attacks can be detected by computing time series clustering similarity using hierarchical clustering on the vehicle's CAN signals. We develop a forensic tool as a proof of concept to demonstrate the potential of the proposed approach for detecting CAN masquerade attacks.
  arXiv  Detail & Related papers  (2022-01-07T20:25:40Z)
• Discriminator-Free Generative Adversarial Attack [87.71852388383242]
  Agenerative-based adversarial attacks can get rid of this limitation. ASymmetric Saliency-based Auto-Encoder (SSAE) generates the perturbations. The adversarial examples generated by SSAE not only make thewidely-used models collapse, but also achieves good visual quality.
  arXiv  Detail & Related papers  (2021-07-20T01:55:21Z)
• Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking [83.48804199140758]
  We propose a learning-to-mis-rank formulation to perturb the ranking of the system output. We also perform a back-box attack by developing a novel multi-stage network architecture. Our method can control the number of malicious pixels by using differentiable multi-shot sampling.
  arXiv  Detail & Related papers  (2020-04-08T18:48:29Z)

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.