Malware Detection Using Neural Network

Abstract

One of the great and major issues facing the Internet today is a large amount of data and files that need to be analyzed for possible malicious purposes. Malicious software also referred to as an attacker’s malware is polymorphic and metamorphic in design. It has the potential to modify their code as it spreads. Increased malware and sophisticated cyber attacks are becoming a serious issue. Unknown malware that has not been identified by security vendors is often used in these attacks, making it difficult to protect terminals from infection. As of now, there is a lot of research being performed to identify and monitor malware. After acknowledgment of the deep learning area, several researchers have tried to detect malware using neural networks and deep learning methods. This paper contrasts the performance of three different neural networking models: Convolutional Neural Networks (CNN), Long-Short Term Memory (LSTM) Network, and Gated Recurrent Unit (GRU) for malware detection. Besides, we used secondary data to gather information about malware activity.