Towards Accurate Labeling of Android Apps for Reliable Malware Detection

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.

Malware Detection

Maat: Automatically Analyzing VirusTotal for Accurate Labeling and Effective Malware Detection

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.

Malware Analysis Malware Detection

Don't Pick the Cherry: An Evaluation Methodology for Android Malware Detection Methods

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

Stimulation and Detection of Android Repackaged Malware with Active Learning

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