Enhanced Image-Based Malware Classification Using Snake Optimization Algorithm With Deep Convolutional Neural Network

Abstract

Malware is a malicious software intended to cause damage to computer systems. In recent times, significant proliferation of malware utilized for illegal and malicious goals has been recorded. Several machine and deep learning methods are widely used for the detection and classification of malwares. Image-based malware detection includes the usage of machine learning and computer vision models for analyzing the visual representation of malware, including binary images or screenshots, for the purpose of detecting malicious behaviors. This techniques provides the potential to identify previously hidden or polymorphic malware variants based on the visual features, which provide a further layer of defense against emerging cyber-attacks. This study introduces a new Snake Optimization Algorithm with Deep Convolutional Neural Network for Image-Based Malware Classification technique. The primary intention of the proposed technique is to apply a hyperparameter-tuned deep learning method for identifying and classifying malware images. Primarily, the ShuffleNet method is mainly used to derivate the feature vectors. Besides, the snake optimization algorithm can be deployed to boost the choice of hyperparameters for the ShuffleNet algorithm. For the recognition and classification of malware images, attention-based bi-directional long short-term memory model. The simulation evaluation of the proposed algorithm has been examined using the Malimg malware dataset. The experimental values inferred that the proposed methodology achieves promising performance with a maximum accuracy of 98.42% compared to existing models.