Description
Machine learning based detection models can strengthen detection, but there remain some significant barriers to the widespread deployment of such techniques in operational detection systems. In this presentation, we identify the main challenges to overcome and we provide both methodological guidance and practical solutions to address them. The solutions we present are completely generic to be beneficial to any detection problem on any data type and are freely available in SecuML.The content of the presentation is mostly based on my PhD thesis “Expert-in-the-Loop Supervised Learning for Computer Security Detection Systems”.
Infos pratiques
Prochains exposés
-
Tackling obfuscated code through variant analysis and Graph Neural Networks
Orateur : Roxane Cohen and Robin David - Quarkslab
Existing deobfuscation techniques usually target specific obfuscation passes and assume a prior knowledge of obfuscated location within a program. Also, some approaches tend to be computationally costly. Conversely, few research consider bypassing obfuscation through correlation of various variants of the same obfuscated program or a clear program and a later obfuscated variant. Both scenarios are[…]-
Malware analysis
-
Binary analysis
-
Obfuscation
-