Malware Detection with AI Systems: bridging the gap between industry and academia

With the abundance of programs developed everyday, it is possible to develop next-generation antivirus programs that leverage this vast accumulated knowledge. In practice, these technologies are developed with a mixture of established techniques like pattern matching, and machine learning algorithms, both tailored to achieve high detection rate and low false alarms. While companies state the application of both techniques, no rigorous investigation on the interconnection between detection strategies have been properly discussed and evaluated, thus keeping further advancements in the field locked up in secrecy. In this talk, we will venture forth into both pattern-matching and data-based decision-making processes to study how they can be integrated, and how their performances can be tuned to improve their efficacy. Also, we will peek into the world of adversaries that want to sneak through these next-generation antivirus programs, highlighting new challenges as well.