Systems

The proliferation and increasing sophistication of censorship warrants continuing efforts to develop tools to evade it. Yet, designing effective mechanisms for censorship resistance ultimately depends on accurate models of the capabilities of censors, as well as how those capabilities will likely evolve. In contrast to more established disciplines within security, censorship resistance is relatively nascent, not yet having solid foundations for understanding censor capabilities or evaluating the effectiveness of evasion technologies.

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.

Living in an age when services are often rated, people are increasingly depending on reputation of sellers or products/apps when making purchases online. This puts pressure on people to gain and maintain a high reputation by offering reliable and high-quality services and/or products, which benefits the society at large. Unfortunately, due to extremely high competition in e-commerce or app stores, recently reputation manipulation related services have quickly developed into a sizable business, which is termed Reputation-Escalation-as-a-Service (REaaS).

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.

New security exploits emerge far faster than manual analysts can analyze them, driving growing interest in automated machine learning tools for computer security. Classifiers based on machine learning algorithms have shown promising results for many security tasks including malware classification and network intrusion detection, but classic machine learning algorithms are not designed to operate in the presence of adversaries.