Checking is Believing: Event-Aware Program Anomaly Detection in Cyber-Physical Systems

TL;DR

This paper introduces Orpheus, a novel security approach for cyber-physical systems that enforces execution semantics to detect data-oriented attacks, using event-aware automata to improve anomaly detection with low runtime overhead.

Contribution

It presents a new method for reasoning cyber-physical execution semantics and introduces eFSA, an event-aware automaton for detecting data-oriented exploits in CPS.

Findings

eFSA successfully detects runtime data-oriented attacks.

Prototype incurs minimal overhead, 0.0001s per state check.

Effective in case studies on Raspberry Pi.

Abstract

Securing cyber-physical systems (CPS) against malicious attacks is of paramount importance because these attacks may cause irreparable damages to physical systems. Recent studies have revealed that control programs running on CPS devices suffer from both control-oriented attacks (e.g., code-injection or code-reuse attacks) and data-oriented attacks (e.g., non-control data attacks). Unfortunately, existing detection mechanisms are insufficient to detect runtime data-oriented exploits, due to the lack of runtime execution semantics checking. In this work, we propose Orpheus, a new security methodology for defending against data-oriented attacks by enforcing cyber-physical execution semantics. We first present a general method for reasoning cyber-physical execution semantics of a control program (i.e., causal dependencies between the physical context and program control flows), including the event identification and dependence analysis. As an instantiation of Orpheus, we then present a new program behavior model, i.e., the event-aware finite-state automaton (eFSA). eFSA takes advantage of the event-driven nature of CPS control programs and incorporates event checking in anomaly detection. It detects data-oriented exploits if a specific physical event is missing along with the corresponding event dependent state transition. We evaluate our prototype's performance by conducting case studies under data-oriented attacks. Results show that eFSA can successfully detect different runtime attacks. Our prototype on Raspberry Pi incurs a low overhead, taking 0.0001s for each state transition integrity checking, and 0.063s~0.211s for the cyber-physical contextual consistency checking.