Visual Malware Recognition Using Artificial Neural Networks

The use of neural networks is becoming increasingly widespread in many areas of human activity. Thus, artificial intelligence technologies are being introduced into many mechanisms for ensuring information and computer security, since with the help of neural networks it is possible to analyze a huge amount of data about security events. However, some dependencies in the data are better visible in visualizations. At the same time, visual search still requires the use of a human operator. In order to automate the decision-making process in computer security systems, a study was conducted on the effectiveness of malware data visual analysis. This paper proposes an approach to visually searching for anomalies in software data using neural networks to detect the presence of malicious software. Experiments have also been conducted, and they show that with this approach, the neural network recognizes the presence of malicious software with very high accuracy.