Data Spoofing

Exploiting contextual anomalies to detect perception attacks on cyber-physical systems

Zhongyuan Hau

Abstract

Perception is a key component in Cyber-Physical Systems (CPS) where information is collected by sensors and processed for decision-making. Perception attacks have been crafted specifically to subvert the CPS decision-making process. CPS are used in many safety-critical applications and perception attacks could lead to catastrophic consequences. Hence, there is a need to study how effective detection systems can be designed to detect such attacks. Designing detection systems for perception attacks in CPS is difficult as each CPS is domain-specific and existing detection systems for one CPS in one domain cannot be easily transferred to another. Current proposed detection systems are implemented to mitigate specific attacks and most offer only high-level insights on how the detection is performed. A systematic approach to designing detection for perception attacks that is generally applicable for CPS is needed. We propose a threat-modelling based methodology to design perception attack detection systems for CPS. An information model of the CPS, together with a threat model are used to determine how information correlations, defined as invariants, can be exploited as context for detecting anomalies. The proposed methodology was first applied to design perception attack detection Autonomous Driving, where we tackle the problem of attacks on LiDAR-based perception to spoof and hide objects. A novel specified physical invariant, the 3D shadow, was identified and shown that it is a robust verifier of genuine objects and was used to detect spoofed and hidden objects. Another learnt physical invariant of an object, where its motion needs to be temporally consistent, was shown to be effective in detecting object spoofing. Secondly, we apply the methodology to design the detection of false data injection in low-density sensor networks. We show that the use of learnt correlations across sensor measurements is effective even in a constrained setting with few sensors and heterogeneous data.

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Ensuring the resilience of wireless sensor networks to malicious data injections through measurements inspection

Vittorio Illiano

Malicious data injections pose a severe threat to the systems based on Wireless Sensor Networks (WSNs) since they give the attacker control over the measurements, and on the system’s status and response in turn. Malicious measurements are particularly threatening when used to spoof or mask events of interest, thus eliciting or preventing desirable responses. Spoofing and masking attacks are particularly difficult to detect since they depict plausible behaviours, especially if multiple sensors have been compromised and collude to inject a coherent set of malicious measurements. Previous work has tackled the problem through measurements inspection, which analyses the inter-measurements correlations induced by the physical phenomena. However, these techniques consider simplistic attacks and are not robust to collusion. Moreover, they assume highly predictable patterns in the measurements distribution, which are invalidated by the unpredictability of events. We design a set of techniques that effectively detect malicious data injections in the presence of sophisticated collusion strategies, when one or more events manifest. Moreover, we build a methodology to characterise the likely compromised sensors. We also design diagnosis criteria that allow us to distinguish anomalies arising from malicious interference and faults. In contrast with previous work, we test the robustness of our methodology with automated and sophisticated attacks, where the attacker aims to evade detection. We conclude that our approach outperforms state-of-the-art approaches. Moreover, we estimate quantitatively the WSN degree of resilience and provide a methodology to give a WSN owner an assured degree of resilience by automatically designing the WSN deployment. To deal also with the extreme scenario where the attacker has compromised most of the WSN, we propose a combination with software attestation techniques, which are more reliable when malicious data is originated by a compromised software, but also more expensive, and achieve an excellent trade-off between cost and resilience.

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