Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security

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Author(s): S. Abijah Roseline (VIT Chennai, India)and S. Geetha (VIT, Chennai, India)
Copyright: 2019
Pages: 19
Source title: Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems
Source Author(s)/Editor(s): S. Geetha (VIT Chennai, India)and Asnath Victy Phamila (VIT Chennai, India)
DOI: 10.4018/978-1-5225-8241-0.ch011

Keywords: Cyber and Network Security / Digital Crime & Forensics / Information Science Reference / Security & Forensics

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Abstract

Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but are not optimal and show a low accuracy rate for unknown malwares. This chapter explores a novel deep learning model called deep dilated residual network model for malware image classification. The proposed model showed a higher accuracy of 98.50% and 99.14% on Kaggle Malimg and BIG 2015 datasets, respectively. The new malwares can be handled in real-time with minimal human interaction using the proposed deep residual model.

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Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security

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