An Efficient Malware Detection System using Hybrid Feature Selection Methods

Abstract

Malware is a serious threat to individuals and users. The security researchers present various solutions, striving to achieve efficient malware detection. Malware attackers devise detection avoidance techniques to escape from detection systems. The key challenge is that growth of malware increases every hour, leading to large damages to users’ privacy. The training process takes much longer time, mining the unnecessary features. Feature Selection is effective in achieving unique feature set in detecting malware. In this paper, we propose a malware detection system using hybrid feature selection approach to detect malware efficiently with a reduced feature set. Machine learning based classification is performed on eight classifiers with two malware datasets. The experiments were done without and with feature selection. The empirical results show that the classification using selected feature set and XGB classifier identifies malware efficiently with an accuracy of 98.9% and 99.26% for the two datasets.

Malware Classification Using Xgboost With Vote Based Backward Feature Elimination Technique

“…The modified features are now trained again once, and the procedure is repeated up to the highest P-value feature is less than the threshold limit. [8]. Approaches that use machine learning and similitude mining to visualize static and dynamic malware detection.…”