EAGER

This project aims to tackle some of the most significant challenges facing the design and adoption of risk-informed cyber insurance policies; these challenges include cyber risk interdependence, correlated risk and value-at-risk, and a fast-changing threat landscape. The research has the potential to bring about a paradigm shift in the design of cyber insurance policies so that they are used as effective economic and incentive mechanisms consistent with cyber risk realities; in doing so it also introduces new ways of thinking about cybersecurity in a holistic, risk management context.

The project will investigate human factors in network security. The security of network systems relies on proper protection from not only known vulnerabilities, but also new vulnerabilities resulting from unexpected human behavior. The project will directly address a user's situational behavior and its consequence on network security. It engages in the challenges of modeling decision-making process and integrating it in the human-network interaction.

Malware, a broad term for any type of malicious software, is a piece of code designed by cyber attackers to infect computing systems without the user consent, typically for harmful purposes such as stealing sensitive information. The ubiquity of information technology has made malware a serious threat. Detecting malware in a system is a difficult task, particularly when the malware is stealthy. Hardware-assisted malware detection (HMD) mechanisms seek runtime detection of malware.

Digital Encryption is typically performed by specialized circuits to ensure confidentiality and integrity of data. While encryption is mathematically robust, the circuits encrypting data may leak information via the amount of the power drawn from the supply, and the amount of electromagnetic (EM) radiation that emanates from the circuit. This is known as side-channel leakage. An attacker may be able to unravel the secret cryptographic information by analyzing the side-channel leakage, thereby compromising security. Newer analysis techniques based on machine-learning make the attack easier.

This project is designed to advance research on problematic eating behavior. The project investigates wearable sensors to measure eating behavior and developing models of behavior that comprise multiple observable behaviors such as eating alone or with friends, or chewing speed. These data can help scientists improve upon current traditional methods such as self-reported eating diaries, which tend to be inconsistent, sparse, and rarely timely. We capture human behavior using a custom wearable augmented camera. Wearable cameras provide rich data, but raise privacy concerns.