Adam Doupé

TL;DR: A novel android malware detection system that uses a deep convolutional neural network (CNN) to perform static analysis of the raw opcode sequence from a disassembled program, removing the need for hand-engineered malware features.

TL;DR: The results of the evaluation show that crawling is a task that is as critical and challenging to the overall ability to detect vulnerabilities as the vulnerability detection techniques themselves, and that many classes of vulnerabilities are completely overlooked by these tools, and thus research is required to improve the automated detection of these flaws.

TL;DR: It is shown that the state-aware black-box web vulnerability scanner is able to not only exercise more code of the web application, but also discover vulnerabilities that other vulnerability scanners miss.

TL;DR: This study performs a measurement analysis of CryptoLocker, a family of ransomware that encrypts a victim's files until a ransom is paid, within the Bitcoin ecosystem from September 5, 2013 through January 31, 2014 and finds evidence that suggests connections to popular Bitcoin services, and subtle links to other cybercrimes surrounding Bitcoin.

TL;DR: An extensive analysis of the native code usage in 1.2 million Android apps demonstrates that sandboxing native code with no permissions is not ideal, as apps’ native code components perform activities that require Android permissions, and provides very encouraging insights that make us believe that sandboxed native code can be feasible and useful in practice.