Hybridized grasshopper optimization and cuckoo search algorithm for the classification of malware

Abstract

The classification and analysis of malicious software (malware) has reached a huge development in the systems associated with the internet. The malware exploits the system information and takes off 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, many researchers and scientists discovered anti-malware products to identify known malware. But these methods are not robust to detect obfuscated and packed malware. To overcome these problems, the hybridized grasshopper optimization and cuckoo search (GOA-CSA) algorithm is proposed. The effective features are selected by the GOA-CSA 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 dataset which consists of malware samples from various software. The proposed model performance is estimated by using the performance metrics like accuracy, sensitivity, recall, and F1-score. The model attained better accuracy of 98.95% when the model is compared with other existing models.