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Modern critical infrastructure relies on successful interdependent function among many different types of networks. For example, the Internet depends on access to the power grid, which in turn depends on the power-grid communication network and the energy production network. For this reason, network science researchers have begun examining the robustness of critical infrastructure as a network of networks, or a multilayer network. Research in network anomaly detection systems has focused on single network structures (specifically, the Internet as a single network).

There are many applications in business and end-user applications where user tracking is part of the core functionality or feature set. However, user tracking can intrude on user privacy and even may lead to online crimes. Recent research has shown that tracking companies have started using advanced web tracking techniques that are more subtle and less transparent than traditional online tracking.

The main objective of this two-year project is to develop, implement, and study an intervention that increases cybersecurity awareness and proficiency among high school teachers and exposes high school girls to cybersecurity activities and careers. This objective is spurred by the growing need for cybersecurity professionals coupled with the under-representation of women in this field. The Bureau of Labor Statistics reports that 18% of cybersecurity specialists are women.

The project explores building support for malware detection in hardware. Malware detection is challenging and resource intensive, as the number and sophistication of malware increases. The resource requirements for malware detection limit its use in practice, leaving malware unchecked on many systems. We use a low level hardware detector to identify malware as a computational anomaly using low level features such as hardware events, instruction mixes and memory address patterns.

Secure multi-party computation (SMC) allows several mutually untrusting parties to perform joint computations while keeping their inputs private. This project develops new techniques for constructing two-party secure computation protocols with low communication overhead. Building on the PIs prior work for constructing special-purpose secure computation protocols for greedy algorithms, this project aims to develop new techniques that exploit the algorithmic structure of a function in order to develop more efficient secure computation protocols.