Adapt

Network attacks are increasingly complex and fast-evolving. A single attack may use multiple reconnaissance, exploit, and obfuscation techniques. This project investigates how to extract critical attack attributes, synthesize novel attack sequences, and reveal potential threats to critical assets in a timely manner. The project uses machine learning techniques to simultaneously identify new attack types and observed events that could identify those attacks.

The adoption of smartphones has steadily increased in the past few years, and smartphones have become the tool with which millions of users handle confidential information, such as financial and health-related data. As a result, these devices have become attractive targets for cybercriminals, who attempt to violate the trust assumptions underlying the smartphone platform in order to compromise the security and privacy of users.

Mobile cloud technologies have begun to rely heavily on services known as Mobile Back-end as a Service (MBaaS), including push messaging, data synchronization, and mobile identity management. Many of today's popular apps have already integrated push messaging services such as Google Cloud Messaging (GCM), Amazon Device Messaging (ADM), and third parties like Baidu, to enable the apps to receive notifications such as private messages, financial secrets or family members' locations.

This project addresses several key emerging security challenges that arise due to the wildly successful large-scale adoption of mobile devices with diverse network capabilities. The novel approach focuses on to understanding how various information that are legitimately and willingly provided by smartphone users due to the requested permissions of downloaded applications can be potentially abused. The second research focus is to identify improvements in the design of cellular network middlebox (e.g., firewall) policies by detailed exposure and explicitly defining the key requirements.

Touch screens on smart mobile devices such as cell phones or tablets allow both user input (touch events) and display output. For a touch screen to function, the mobile device stores input and display data in a graphics buffer internal to the device. The researchers have discovered that a malicious application running on the mobile device could silently monitor characteristics of the graphics buffer to identify the alphanumeric characters that the user types into the touch keyboard or information displayed on the screen.