Systems

Computer and network security is currently challenged by the need to secure diverse network environments including clouds and data-centers, PCs and enterprise infrastructures. This diversity of environments is coupled to increased attack sophistication. Today's tools for securing network and computing infrastructures can be painstakingly composed and configured using available components, but fail to automatically learn from their environment and actively protect it.

The World Wide Web and computer "clouds" have become widely used, and are interwoven into many activities of daily life, from shopping to socializing to education. But the data center servers that are the backbone of this richly connected world remain vulnerable to malicious software ("malware"). Over the past decade, attacks have increased in number and sophistication, motivated by both financial and political goals. The results include consumer concerns about identify theft and fraudulent charges, corporate concerns about millions of dollars in losses, and potential defense concerns.

Outsourcing storage to the cloud has become more widespread in recent years; however, cloud storage services are constantly exposed to a number of non-trivial adversarial threats. This work addresses security risks arising from the leakage of access patterns, which is the ability of an adversary to detect when the same item is accessed repeatedly on a storage server, which has been shown to substantially impact data privacy.

As society becomes more dependent on cyber infrastructure, the security of networks and information technologies has become a growing concern. Individuals, businesses, and governmental organizations are now common victims of cyber-attacks that seek to steal private data, gain remote control over remote systems, and cause harm to networks and systems through other malicious means. Additionally, critical infrastructures such as smart power grids and communication networks are facing an increasing number of cyber-based threats.

Common smartphone authentication mechanisms such as PINs, graphical passwords, and fingerprint scans offer limited security. They are relatively easy to guess or spoof, and are ineffective when the smartphone is captured after the user has logged in. Multi-modal active authentication addresses these challenges by frequently and unobtrusively authenticating the user via behavioral biometric signals, such as touchscreen interaction, hand movements, gait, voice, and phone location.