Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems

Abstract

Neural networks are increasingly used for intrusion detection on industrial control systems (ICS). With neural networks being vulnerable to adversarial examples, attackers who wish to cause damage to an ICS can attempt to hide their attacks from detection by using adversarial example techniques. In this work we address the domain specific challenges of constructing such attacks against autoregressive based intrusion detection systems (IDS) in an ICS setting. We model an attacker that can compromise a subset of sensors in a ICS which has a LSTM based IDS. The attacker manipulates the data sent to the IDS, and seeks to hide the presence of real cyber-physical attacks occurring in the ICS. We evaluate our adversarial attack methodology on the Secure Water Treatment system when examining solely continuous data, and on data containing a mixture of discrete and continuous variables. In the continuous data domain our attack successfully hides the cyber-physical attacks requiring 2.87 out of 12 monitored sensors to be compromised on average. With both discrete and continuous data our attack required, on average, 3.74 out of 26 monitored sensors to be compromised.

Accepted at the 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

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Cited by in corpus (6)

• Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
• Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems
• Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks
• Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systems
• SAGE: Stealthy Attack GEneration for Cyber-Physical Systems