An investigation of a deep learning based malware detection system

Abstract

We investigate a Deep Learning based system for malware detection. In the investigation, we experiment with different combination of Deep Learning architectures including Auto-Encoders, and Deep Neural Networks with varying layers over Malicia malware dataset on which earlier studies have obtained an accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these results were done using extensive man-made custom domain features and investing corresponding feature engineering and design efforts. In our proposed approach, besides improving the previous best results (99.21% accuracy and a False Positive Rate of 0.19%) indicates that Deep Learning based systems could deliver an effective defense against malware. Since it is good in automatically extracting higher conceptual features from the data, Deep Learning based systems could provide an effective, general and scalable mechanism for detection of existing and unknown malware.

13 Pages, 4 figures

References in corpus (3)

• DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
• A multi-task learning model for malware classification with useful file access pattern from API call sequence
• Group-wise classification approach to improve Android malicious apps detection accuracy

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• Malware Detection using Machine Learning and Deep Learning
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• DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture
• Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS
• LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach