Towards Building Efficient Malware Detection Engines Using Hybrid CPU/GPU-Accelerated Approaches

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Author(s): Ciprian Pungila (West University of Timişoara, Romania)and Viorel Negru (West University of Timişoara, Romania)
Copyright: 2014
Pages: 28
Source title: Architectures and Protocols for Secure Information Technology Infrastructures
Source Author(s)/Editor(s): Antonio Ruiz-Martinez (University of Murcia, Spain), Rafael Marin-Lopez (University of Murcia, Spain)and Fernando Pereniguez-Garcia (University of Murcia, Spain)
DOI: 10.4018/978-1-4666-4514-1.ch009

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

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Abstract

This chapter presents an outline of the challenges involved in constructing efficient malware detection engines using hybrid CPU/GPU-accelerated architectures and discusses how one can overcome such challenges. Starting with a general problem description for malware detection and moving on to the algorithmic background involved for solving it, the authors present a review of the existing approaches for detecting malware and discuss how such approaches may be improved through GPU-accelerated processing. They describe and discuss several hybrid hardware architectures built for detecting malicious software and outline the particular characteristics of each, separately, followed by a debate on their performance and most suitable application in real-world environments. Finally, the authors tackle the problem of performing real-time malware detection and present the most important aspects that need to be taken into account in intrusion detection systems.

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Towards Building Efficient Malware Detection Engines Using Hybrid CPU/GPU-Accelerated Approaches

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