Fisher exact Boschloo and polynomial vector learning for malware detection

Sheelavathy Veerabhadrappa Kudrekar, Udaya Rani Vinayaka Murthy


Computer technology shows swift progress that has infiltrated people’s lives with the candidness and pliability of computers to work ease shows security breaches. Thus, malware detection methods perform modifications in running the malware based on behavioral and content factors. The factors are taken into consideration compromises of convergence rate and speed. This research paper proposed a method called fisher exact Boschloo and polynomial vector learning (FEB-PVL) to perform both content and behavioral-based malware detection with early convergence to speed up the process. First, the input dataset is provided as input then fisher exact Boschloo’s test Bernoulli feature extraction model is applied to obtain independent observations of two binary variables. Next, the extracted network features form input to polynomial regression support vector learning to different malware classes from benign classes. The proposed method validates the results with respect to the malware and the benign files. The present research aimed to develop the behaviors to detect the accuracy process of the features that have minimum time speeds the overall performances. The proposed FEB-PVL increases the true positive rate and reduces the false positive rate and hence increasing the precision rate using FEB-PVL by 7% compared to existing approaches.


behavior-based network feature; content-based network feature; malware detection; polynomial regression; support vector learning;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578