Chaos Game Optimization with stacked LSTM sequence to sequence autoencoder for malware detection in IoT cloud environment

Abstract

Malware detection in Internet of Things (IoT) cloud platforms is a crucial security system for securing data and devices' integrity, secrecy, and availability. IoT devices are linked to cloud-based services offering storage, calculating, and analytics abilities. However, these devices are also exposed to malware attacks that could cause significant damage. Malware detection in IoT cloud platforms involves analyzing and identifying potential threats like Trojans, viruses, ransomware, and worms. It is done through several processes, including behavior-based detection, signature-based detection, and anomaly-based detection. The study proposes a Chaos Game Optimization with improved deep learning for Malware Detection (CGOIDL-MD) technique in the IoT cloud platform. The proposed CGOIDL-MD technique majorly concentrates on the automated detection and classification of malware in the IoT cloud framework. The CGOIDL-MD method applies the CGO-based feature subset selection (CGO-FSS) approach to select features. Besides, the stacked long short-term memory sequence-to-sequence autoencoder (SLSTM-SSAE) approach was exploited for malware classification and detection. Moreover, the arithmetic optimization algorithm (AOA) technique was exploited for the hyperparameter selection technique. The simulation outcomes of the CGOIDL-MD technique were tested on the malware dataset, and the outcome can be studied from different perspectives. The experimentation outcomes illustrate the betterment of the CGOIDL-MD technique under various measures.