Deep Learning At The Shallow End: Malware Classification For Non-Domain Experts

Oisin Boydell discusses his research at DFRWS US 2018.

Oisin: Good morning, everyone. Can you hear me? My name is Oisin Boydell. I’m from CeADAR, which is the Centre for Applied Data Analytics Research, and that’s at University [00:15]. I’m going to talk about our paper, which we call ‘Deep Learning at the Shallow End: Malware Classification for Non-Domain Experts’.

To give it a context – malware analysis and detection and classification face a number of challenges, and these are related to the huge volume and variation of malware and data that is present. This is very dynamic. Malware is constantly changing, it’s constantly adapting. There’s definitely a cat-and-mouse between malware creators and developers, and tools and approaches to prevent that and analyze that.


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