Automated Malware Similarity Analysis

Architecture and Background

Automated Malware Similarity Analysis Daniel Raygoza DC3 / General Dynamics While it is fairly straightforward for a malware analyst to compare two pieces of malware for code reuse, it is not a simple task to scale to thousands of pieces of code.

Vulnerability Analysis

Many existing automated approaches focus on runtime analysis and critical trait extraction through signatures, but they don't focus on code reuse.

Exploitation and Demonstrations

Automated code reuse detection can help malware analysts quickly identify previously analyzed code, develop links between malware and its authors, and triage large volumes of incoming data.

Remediation and Defensive Strategies

Daniel Raygoza is employed with General Dynamics at the Department of Defense Cyber Crime Center (DC3) Computer Forensics Laboratory (DCFL) as a Forensic Examiner, where he has worked for six years. He performs system forensics, incident response, malware analysis and reverse engineering, and R&D in support of his fellow analysts. He has presented several times at the DC3 Cyber Crime Conference, and has released several small tools for use by the forensics community. For copies of the slides and additional materials please see the DEF CON 17 Archive here: