Mitigating the Risks of Malware Attacks with Deep Learning Techniques

Abstract

Malware has become increasingly prevalent in recent years, endangering people, businesses, and digital assets worldwide. Despite the numerous techniques and methodologies proposed for detecting and neutralizing malicious agents, modern automated malware creation methods continue to produce malware that can evade modern detection techniques. This has increased the need for advanced and accurate malware classification and detection techniques. This paper offers a unique method for classifying malware, using images that use dual attention and convolutional neural networks. Our proposed model has demonstrated exceptional performance in malware classification, achieving the remarkable accuracy of 98.14% on the Malimg benchmark dataset. To further validate its effectiveness, we also evaluated the model’s performance on the big 2015 dataset, where it achieved an even higher accuracy rate of 98.95%, surpassing previous state-of-the-art solutions. Several metrics, including the precision, recall, specificity, and F1 score were used to evaluate accuracy, showing how well our model performed. Additionally, we used class-balancing strategies to increase the accuracy of our model. The results obtained from our experiments indicate that our suggested model is of great interest, and can be applied as a trustworthy method for image-based malware detection, even when compared to more complex solutions. Overall, our research highlights the potential of deep learning frameworks to enhance cyber security measures, and mitigate the risks associated with malware attacks.

Keywords

• attention module
• cyber security
• deep learning
• malware classification