Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems
Recently, neural network (NN)-based methods, including autoencoders, have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detectors are often retrained, using data collected during system operation, to cope with the natural evolution (i.e., concept drift) of the monitored signals. However, by exploiting this mechanism, an attacker can fake the signals provided by corrupted sensors at training time and poison the learning process of the detector such that cyber attacks go undetected at test time. With this research, we are the first to demonstrate such poisoning attacks on ICS cyber attack online NN detectors. We propose two distinct attack algorithms, namely, interpolation- and back-gradient based poisoning, and demonstrate their effectiveness on both synthetic and real-world ICS data. We also discuss and analyze some potential mitigation strategies.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks
In recent years, a variety of effective neural network-based methods for anomaly and cyber attack detection in industrial control systems (ICSs) have been demonstrated in the literature. Given their successful implementa…
Cyber Attack DetectionData PoisoningMalware ClassificationMalware DetectionAutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain
Poisoning attacks pose an increasing threat to the security and robustness of Artificial Intelligence systems in the military domain. The widespread use of open-source datasets and pretrained models exacerbates this risk…
Anomaly DetectionObject DetectionPoison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks
The performance of machine learning models depends on the quality of the underlying data. Malicious actors can attack the model by poisoning the training data. Current detectors are tied to either specific data types, mo…
Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems
Cyber-Physical Systems (CPS) integrate sensing, communication, computation, and control to support critical infrastructure, including smart grids, industrial automation, and control systems. In the electrical utility dom…
False Alarms, Real Damage: Adversarial Attacks Using LLM-based Models on Text-based Cyber Threat Intelligence Systems
Cyber Threat Intelligence (CTI) has emerged as a vital complementary approach that operates in the early phases of the cyber threat lifecycle. CTI involves collecting, processing, and analyzing threat data to provide a m…
Text Generation