no code implementations • 1 Jul 2020 • Aleieldin Salem
In training their newly-developed malware detection methods, researchers rely on threshold-based labeling strategies that interpret the scan reports provided by online platforms, such as VirusTotal.
no code implementations • 1 Jul 2020 • Aleieldin Salem, Sebastian Banescu, Alexander Pretschner
We found that such ML-based strategies (a) can accurately and consistently label apps based on their VirusTotal scan reports, and (b) contribute to training ML-based detection methods that are more effective at classifying out-of-sample apps than their threshold-based counterparts.
no code implementations • 25 Mar 2019 • Aleieldin Salem, Sebastian Banescu, Alexander Pretschner
In evaluating detection methods, the malware research community relies on scan results obtained from online platforms such as VirusTotal.
Cryptography and Security
no code implementations • 3 Aug 2018 • Aleieldin Salem
In this paper, we propose the usage of active learning to train classifiers able to cope with the ambiguous nature of repackaged malware.
Cryptography and Security