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An investigation of a deep learning based malware detection system - 2018

An Investigation Of A Deep Learning Based Malware Detection System

Research Paper on An Investigation Of A Deep Learning Based Malware Detection System

Research Area:  Machine Learning

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 an 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.

Keywords:  
Malware Detection System
Machine Learning
Deep Learning

Author(s) Name:  Mohit Sewak , Sanjay K. Sahay , Hemant Rathore

Journal name:  

Conferrence name:  Proceedings of the 13th International Conference on Availability, Reliability and Security

Publisher name:  ACM

DOI:  10.1145/3230833.3230835

Volume Information:  Article No.: 26Pages 1–5