A Model for Energy-Efficient Household Maintenance through Behavioral Analysis of Electrical Appliances

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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A Model for Energy-Efficient Household Maintenance through Behavioral Analysis of Electrical Appliances

Cited by 3 publications

References 8 publications

Efficient parallel automata construction for hybrid resource-impelled data-matching

Efficient parallel automata construction for hybrid resource-impelled data-matching

Improved file-Carving through data-parallel pattern matching for data forensics

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

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