Category: Machine Learning

In cyber systems, the identities of devices can easily be spoofed and are frequent targets of cyber-attacks. Once an identity is fabricated, stolen or spoofed it may be used as a nexus to systems, thus forming a Sybil Attack. To address these and other problems associated with identity deception researchers at the Carnegie Mellon University Software Engineering Institute, New York University's Tandon School of Engineering and Courant Institute of Mathematical Sciences, and the University of Göttingen (Germany), collaborated to develop a deception-resistant identity management system inspired by biological systems; namely, ant colonies. This blog post highlights our research contributions.

Malware, which is short for "malicious software," consists of programming aimed at disrupting or denying operation, gathering private information without consent, gaining unauthorized access to system resources, and other inappropriate behavior. Malware infestation is of increasing concern to government and commercial organizations. For example, according to the Global Threat Report from Cisco Security Intelligence Operations, there were 287,298 "unique malware encounters" in June 2011, double the number of incidents that occurred in March. To help mitigate the threat of malware, researchers at the SEI are investigating the origin of executable software binaries that often take the form of malware. This posting augments a previous postingdescribing our research on using classification (a form of machine learning) to detect "provenance similarities" in binaries, which means that they have been compiled from similar source code (e.g., differing by only minor revisions) and with similar compilers (e.g., different versions of Microsoft Visual C++ or different levels of optimization).