Enhanced Malware Detection Using Deep Learning with Image Processing Techniques

Cyber security is a major worry for anyone with an internet-connected gadget in today's ever-changing environment. Cyber security has become a nightmare due to numerous issues such as intrusion detection, virus categorization, spam analysis, and phishing prevention. Our paper proposes a feature image generation and augmentation method that is integrated with a static analysis of harmful code using convolutional neural networks to address these difficulties (CNN). With the use of this approach, we are able to not only reduce the risk of letting the malware executing on our host system, also have a better availability of features due to the image augmentation that is applied to the feature images. When compared to previous methods, this CNN technique uses less resources and gives a more accurate output.