Hybridized Least Absolute Shrinkage Selection Operator and Cross-Validation Algorithm for Classification of Malware

Abstract

In recent years, the growth of malicious software is becoming high and led to a huge number of security threats. The malware exploits the system information and uses the important information of the user without any intimation. Moreover, the malware furtively directs that information to the servers which are organized by the attackers. In recent years, researchers and scientists discovered anti-malware products to identify known malware. But, these methods are not robust to detect obfuscated and packed malware. The fore mentioned issues rely on the existing approaches can be rectified using the hybridized least absolute shrinkage selection operator and cross validation (LASSO-CV) algorithm are proposed. The LASSO is used in the process of selecting the stochastic features and CV visualize the feature using weighted co-efficient approach. The effective features are selected by the LASSO-CV algorithm which eases the process of classifying the malware. This research also utilized long-short term memory (LSTM)-softsign classifier to classify the malware. The malware samples are collected from the VXHeavens and android malware dataset (AMD) dataset which consists of malware samples from various software. The classification results obtained from the LSTM-softsign classifier have better classification accuracy of 97.85% for VX heavens dataset and 99.24% for AMD.