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Analyzing Hardware Based Malware Detectors Using Machine Learning Techniques

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dc.contributor.advisor Homayoun, Houman
dc.contributor.author Randive, Onkar
dc.creator Randive, Onkar
dc.date 2018-08-03
dc.date.accessioned 2019-06-26T21:26:13Z
dc.date.available 2019-06-26T21:26:13Z
dc.identifier.uri http://hdl.handle.net/1920/11456
dc.description.abstract Growth of malware has been a serious problem in the technology community and would continue to grow with new advances in technology. Traditional software-based malware detection systems have proved to be inadequate. Behavioral malware detection systems have proved to be an improvement but are limited due to the fact that they are resource intensive and still prone to exploitation. Hardware based malware detection has proved to be an effective answer to reduce exploitability of computer systems due to less visibility and access for exploitation. This work shows the results of using different machine learning classifiers for Hardware based malware detection. Further, it analyzes the hardware implementation of these machine classifiers on an FPGA by discussing the latency and area requirements of the machine learning classifiers. It is proposed that classifiers with less or limited number of features for reduced system overhead especially in resource constrained environments like real time systems or embedded systems. Hence, the number of features fed to the classifier are reduced by using feature selection technique called Principal Component Analysis. Simple ML Classifiers like JRIP and OneR prove to be more efficient than complex ML classifiers like neural networks. The results for multiclass classification using Multinomial logistic regression, Multilayer Perceptron and Support Vector machines show that PCA assisted multiclass classifies prove to be 7% more efficient than regular Multiclass classifiers. Further the limitations of the above work are discussed by explaining solution leading to future works in this topic. en_US
dc.language.iso en en_US
dc.subject hardware performance counters en_US
dc.subject machine learning en_US
dc.subject malware detection en_US
dc.subject malware classification en_US
dc.title Analyzing Hardware Based Malware Detectors Using Machine Learning Techniques en_US
dc.type Thesis en_US
thesis.degree.name Master of Science in Computer Engineering en_US
thesis.degree.level Master's en_US
thesis.degree.discipline Computer Engineering en_US
thesis.degree.grantor George Mason University en_US


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