An investigation of a deep learning based malware detection system

TL;DR

This paper explores deep learning architectures for malware detection, achieving higher accuracy and lower false positive rates than previous methods, demonstrating the potential of deep learning for scalable and effective malware defense.

Contribution

It presents a deep learning-based malware detection system that outperforms prior approaches by reducing false positives and increasing accuracy without extensive feature engineering.

Findings

Achieved 99.21% accuracy in malware detection.

Reduced false positive rate to 0.19%.

Demonstrated deep learning's potential for automatic feature extraction.

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.