Princeton University

Tanujay Saha

Niraj K Jha

Jacob Brown

Najwa Aaraj

According to various embodiments, a system for detecting security vulnerabilities in at least one of cyber-physical systems (CPSs) and Internet of Things (IoT) devices is disclosed. The system includes one or more processors configured to construct an attack directed acyclic graph (DAG) unique to each CPS or IoT device of the devices. The processors are further configured to generate an aggregate attack DAG from a classification of each device and a location of each device in network topology specified by a system administrator. The processors are also configured to calculate a vulnerability score and exploit risk score for each node in the aggregate attack DAG. The processors are further configured to optimize placement of defenses to reduce an adversary score of the aggregate attack DAG.

System and method for graphical reticulated attack vectors for internet of things aggregate security (gravitas)

According to various embodiments, a method for detecting security vulnerabilities in at least one of cyber-physical systems (CPSs) and Internet of Things (IoT) devices is disclosed. The method includes constructing an attack directed acyclic graph (DAG) from a plurality of regular expressions, where each regular expression corresponds to control-data flow for a known CPS/IoT attack. The method further includes performing a linear search on the attack DAG to determine unexploited CPS/IoT attack vectors, where a path in the attack DAG that does not represent a known CPS/IoT attack vector represents an unexploited CPS/IoT attack vector. The method also includes applying a trained machine learning module to the attack DAG to predict new CPS/IoT vulnerability exploits. The method further includes constructing a defense DAG configured to protect against the known CPS/IoT attacks, the unexploited CPS/IoT attacks, and the new CPS/IoT vulnerability exploits.

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Princeton, NJ, United States of America

Tanujay Saha