Georgia Institute of Technology Institute for Information Security & Privacy | Georgia Tech

For example, pieces of malicious code are often the most common pieces of forensic evidence available to the security research community. However, a standalone binary by itself is not overly useful in driving attribution statements. This is because a standalone binary lacks context: it does not give away who wrote it, by whom it was controlled, who the infected victims were or what the aim of the operation was that used the binary. A standalone binary does not even confirm in which operation it was used.  What is lacking is context. 

Georgia Tech researchers are working on large-scale collections of malware samples to posit relationships between binaries.  With a sufficient sample size, machine-learning techniques can be applied. Current research builds upon the community’s state-of-the-art approach to attribution, in which code stylometry looks at stylistic features (i.e. white spaces, operators, literals, etc) and author-created attributes (i.e. average number of characters per word, character count, use of special characters, punctuation, etc).  Our aim is produce credible links between a binary and a given set of binaries from the same cyber threat actor in a measurable way.  We focus on the following domains to derive attribution inferences, and require multiple positive correlations between domains to produce results:

• String constants;
• Implementation traits;
• Custom features, and
• Infrastructure.

Another emerging research area is the attribution -- not just of cyber espionage or cyber attack, in which information theft or damage to a network or physical environment is the goal -- of the growing number of cyber-based information operations. Trolls are undertaking sophisticated operations to sway popular opinion, curb dissent, stir unrest and instill fear.  Regardless of what this type of activity is called – information operations, influence campaigns, cyber manipulation, or irregular warfare – this asymmetric and increasingly dangerous activity is on the rise. Researchers at Georgia Tech are looking at ways to attribute this activity using Internet metadata and other fact-based log analysis techniques.