Maryam Raiyat Aliabadi

Maryam Raiyat Aliabadi was a participant in the 2017 UBC Three Minute Thesis competition, with her presentation, "ARTINALI: Dynamic invariant detection for Cyber-Physical System Security”.

Attack Detection and Diagnosis in Cyber-Physical Systems

Why did you decide to pursue a graduate degree?

Having eagerness to learn new aspects of technology and science.

Why did you decide to study at UBC?

UBC is a top university in Canada.


Learn more about Maryam's research

Cyber-Physical Systems (CPS) are being widely deployed in security-critical scenarios such as smart homes and medical devices. Unfortunately, the connectedness of these systems and their relative lack of security measures makes them ripe targets for attacks. Speci€cation-based Intrusion Detection Systems (IDS) have been shown to be effective for securing CPSs. Unfortunately, deriving invariants for capturing the speci€cations of CPS systems is a tedious and error-prone process. Therefore, it is important to dynamically monitor the CPS system to learn its common behaviors and formulate invariants for detecting security attacks. Existing techniques for invariant mining only incorporate data and events, but not time. However, time is central to most CPS systems, and hence incorporating time is essential for achieving low false-positives and false-negatives. This paper proposes ARTINALI, which mines dynamic system properties by incorporating time into other important properties of the system. We demonstrate ARTINALI-based Intrusion Detection Systems (IDS) for two case studies, namely smart meters and smart medical devices, and measure their overheads.