Abstract: 

The amounts of data in digital investigations are ever increasing and new approaches are needed for finding the relevant items amongst the noise. For too long, the focus on digital investigation software has been on parsing and extracting any possible piece of data and displaying it to the user. But, with the increasing amount of data, the focus needs to be on showing only the most relevant items.

Machine learning techniques can help identify which items the user should see first and therefore save them time. This talk will outline how these techniques can be used to rank documents, executables, and other files found during a digital investigation.

Bio: 

Mario Vuksan is the Co-Founder and Chief Executive Officer at ReversingLabs Corporation. Mr. Vuksan served as a Director of Research and Knowledgebase Services at Bit9 Inc. He also served as Program Manager and Consulting Engineer at Groove Networks (acquired by Microsoft), working on Web based solutions, P2P management, and integration servers. Before Groove Networks, Mr. Vuksan developed one of the first Web 2.0 applications at 1414c, a spin-off from PictureTel. He is a regular presenter at RSA, Black Hat, Defcon, Caro Workshop, Virus Bulletin, CEIC, FSISAC, and AVAR Conferences, and has also authored numerous texts on security. He supports AMTSO, IEEE Malware Working Group and CTA, and holds a BA from Swarthmore College and an MA from Boston University.