Fisher exact Boschloo and polynomial vector learning for malware detection

Abstract

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.

An intelligent obfuscated mobile malware detection using deep supervised learning algorithms

“…After the data pre-processing phase, the raw CICMalMem_ 2022 dataset is transformed into a refined format by neglecting the extraneous values and replicas with 58,062 records with 57 features as labelled numerical data. Further, the data is split into 80% (46,449) for training and 20% (11,613) for testing.…”

“…The critical factor is determining a pertinent ML technique for obtaining an optimal result for OMM detection. Currently, ML models are prone to misclassifying the results by considering the minimal malicious behaviour as an outlier and omitting them during classification [11]. Therefore, exploring ML algorithms to attain higher efficacy for OMM is a challenging one.…”