Impact of Neural Network on Malware Detection

Attacks by malware have significantly increased during the last several years, endangering the security of computer systems and networks. The continually shifting landscape of malware assaults makes it challenging for traditional techniques like a rule- and signature-based detection to stay up. Consequently, researchers are now looking at more cutting-edge methods, including neural networks, to increase the precision of malware detection. In assessing new malware by comparable traits, neural networks may gather data from previously discovered malware. This method makes it possible to quickly and reliably identify malware, which is essential for stopping infestation spread. Finding patterns and connections among massive data sets is one of the main benefits of utilising neural networks to detect malware. However, there are some issues with employing neural networks to find viruses. One of the main difficulties is that the network needs a lot of labelled data training. Effective evasion attacks are also feasible, in which fraudsters try to control neural networks by feeding them harmful material designed to elude detection. Despite these difficulties, researchers are still exploring neural networks’ potential for virus detection. This book chapter provides a detailed analysis of how neural networks affect malware detection. Along with an outline of the current status of the research area, updates on neural network topologies and training methods are provided. We'll discuss possible advancements and future possibilities in the industry, including reinforcement learning and generative adversarial networks for malware detection. The first section of the chapter summarises the essential characteristics of neural networks, including their capacity to identify patterns and abnormalities in massive amounts of data. The benefits and drawbacks of using neural networks to detect malware are highlighted. Finally, the extensive usage of neural networks in this book chapter's malware detection analysis is highlighted. Researchers may greatly enhance computer and network security and better protect against the rising danger of assaults by malware by using neural networks’ complicated capabilities.