On the Challenges of Holistic Intrusion Detection in ICS

• URL: http://arxiv.org/abs/2604.21626v1
• Date: Thu, 23 Apr 2026 12:45:21 GMT
• Title: On the Challenges of Holistic Intrusion Detection in ICS
• Abstract summary: Past attacks against industrial control systems (ICS) show that adversaries often target both the ICS network and the physical process to achieve potential catastrophic impact.<n>To secure ICS, intrusion detection systems promise timely uncovering of such adversaries.<n>We present challenges encountered during our research towards a holistic intrusion detection system aiming to cover all dimensions of an ICS.
• Score: 2.0421986354783437
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Past attacks against industrial control systems (ICS) show that adversaries often target both the ICS network and the physical process to achieve potential catastrophic impact. To secure ICS, intrusion detection systems promise timely uncovering of such adversaries. However, as these detection mechanisms typically focus on isolated characteristics of ICS (e.g., packet timings), multiple detection systems have to be deployed in parallel, complicating their operation in practice. In this work, to spur discussion and further research, we present challenges encountered during our research towards a holistic intrusion detection system aiming to cover all dimensions of an ICS.

Related papers

• System-aware contextual digital twin for ICS anomaly diagnosis [6.47954540420163]
  We propose a system-aware unsupervised framework for ICS anomaly diagnosis.<n>The proposed framework achieves real-time detection efficiency and provides consistent, interpretable anomaly diagnoses.
  arXiv  Detail & Related papers  (2026-04-27T05:17:44Z)
• Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses [168.50301366360344]
  Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in safety-critical environments.<n>Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human-robot interactions.<n>This survey provides a comprehensive review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline.
  arXiv  Detail & Related papers  (2026-03-28T13:21:44Z)
• 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)
• Modern DDoS Threats and Countermeasures: Insights into Emerging Attacks and Detection Strategies [49.57278643040602]
  Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure.<n>This paper offers a comprehensive survey of emerging DDoS attacks and detection strategies over the past decade.
  arXiv  Detail & Related papers  (2025-02-27T11:22:25Z)
• Time-to-Lie: Identifying Industrial Control System Honeypots Using the Internet Control Message Protocol [4.328586290529485]
  This paper presents a side-channel method of covertly identifying ICS honeypots using the time-to-live (TTL) values of target devices. We show that many ICS honeypots can be readily identified, via minimal interactions, using only basic networking tools.
  arXiv  Detail & Related papers  (2024-10-23T10:06:02Z)
• ICS-Sniper: A Targeted Blackhole Attack on Encrypted ICS Traffic [7.188557101906752]
  We show that an Internet adversary in the path of an ICS's communication can cause damage to the ICS without infiltrating it. We present ICS-Sniper, a targeted blackhole attack that analyzes the packet metadata to identify the packets carrying critical ICS commands or data, and drops the critical packets to disrupt the ICS's operations.
  arXiv  Detail & Related papers  (2023-12-11T06:02:56Z)
• Vulnerability Assessment of Industrial Control System with an Improved CVSS [3.9596068699962323]
  This study proposes a method to assess the risk of cyberattacks on ICS with an improved Common Vulnerability Scoring System (CVSS) Results show the physical system levels of ICS have the highest severity once cyberattacked.
  arXiv  Detail & Related papers  (2023-06-14T16:48:06Z)
• Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems [6.417955560857806]
  In this work, we present an adversarial attack that simultaneously evades the anomaly detectors and rule checkers of a CPS. Inspired by existing gradient-based approaches, our adversarial attack crafts noise over the sensor and actuator values, then uses a genetic algorithm to optimise the latter. We implement our approach for two real-world critical infrastructure testbeds, successfully reducing the classification accuracy of their detectors by over 50% on average.
  arXiv  Detail & Related papers  (2021-05-22T12:19:03Z)
• TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack [46.79557381882643]
  We present TANTRA, a novel end-to-end Timing-based Adversarial Network Traffic Reshaping Attack. Our evasion attack utilizes a long short-term memory (LSTM) deep neural network (DNN) which is trained to learn the time differences between the target network's benign packets. TANTRA achieves an average success rate of 99.99% in network intrusion detection system evasion.
  arXiv  Detail & Related papers  (2021-03-10T19:03:38Z)
• Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems [34.86059492072526]
  We are first to demonstrate such poisoning attacks on ICS online neural network detectors. We propose two distinct attack algorithms, namely, back-gradient based poisoning, and demonstrate their effectiveness on both synthetic and real-world data.
  arXiv  Detail & Related papers  (2020-12-23T14:11:26Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• 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.