23rd EANN / EAAAI 2022, 17 - 20 June 2022, Greece

TrojanDroid: Android malware detection for Trojan discovery using convolutional neural networks

Saeed Seraj, Michalis Pavlidis, Nikolaos Polatidis


  Android platforms are widely used nowadays in different forms such as mobile phones and tablets, and this has made the Android platform an attractive target for hackers. While there are many solutions available for detecting malware on Android devices there aren’t that many that are concentrated on specific malware types. To this extent, this paper delivers a new dataset for Trojan detection for Android apps based on the permissions of the apps, while the second contribution is a neural network architecture that can classify with very high accuracy if an Android app is a genuine app or a Trojan pretending to be a normal app. We have run extensive evaluation tests to validate the performance of the proposed method and we have compared it to other well-known classifiers using well-known evaluation metrics to show its effectiveness.  

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