TWC

Most of the world's internet access occurs through mobile devices such as smart phones and tablets. While these devices are convenient, they also enable crimes that intersect the physical world and cyberspace. For example, a thief who steals a smartphone can gain access to a person?s sensitive email, or someone using a banking app on the train may reveal account numbers to someone looking over her shoulder. This research will study how, when, and where people use smartphones and the relationship between these usage patterns and the likelihood of being a victim of cybercrime.

Today individuals and organizations leverage machine learning systems to adjust room temperature, provide recommendations, detect malware, predict earthquakes, forecast weather, maneuver vehicles, and turn Big Data into insights. Unfortunately, these systems are prone to a variety of malicious attacks with potentially disastrous consequences. For example, an attacker might trick an Intrusion Detection System into ignoring the warning signs of a future attack by injecting carefully crafted samples into the training set for the machine learning model (i.e., "polluting" the model).

In September of 2015, it was reported that hackers had stolen the fingerprint records of 5.6 million U.S. federal employees from the Office of Personnel Management (OPM). This was a severe security breach, and it is an even bigger problem because those fingerprints are now permanently compromised and the users cannot generate new fingerprints. This breach demonstrates two challenging facts about the current cybersecurity landscape. First, biometric credentials are vulnerable to compromise. And, second, biometrics that cannot be replaced if stolen are even more vulnerable to theft.

Human beings are diverse, and their online behavior is often unpredictable. In today's data-driven world, providers of online services are collecting detailed and comprehensive server-side traces of user activity. These records or logs include detailed, timestamped logs of actions taken by users, often called clickstreams. Given their scale and level of detail, clickstreams present an enormous opportunity for research into user behavioral analysis and modeling.

Society's dependence on mobile technologies rapidly increases as we entrust mobile applications with more and more private information and capabilities. Existing security research follows a common threat model that treats apps as monolithic entities and only captures attack surface between apps. However, recent research reveals that app internal attacks are emerging quickly as complex entities with conflicting interests are commonly included inside a single app to allow for rich features and fast development.