Malware Analysis

Abstract

Malware is a major threat to computer systems and networks. Traditional malware detection methods, such as signature-based detection, are becoming –increasingly ineffective due to the ever-increasing sophistication of malware. Deep learning methods, such as convolutional neural networks (CNNs) and convolutional neural networks with long short-term memory (LSTM) units, have shown promise in malware detection. In this project, we propose a novel malware detection system that uses a CNN with LSTM units. The CNN is used to extract features from the malware code, while the LSTM is used to model the temporal relationships between these features. The system is trained on a dataset of malware and benign code. We evaluate the system on a test dataset of malware and benign code and show that it can achieve high accuracy in detecting malware. Our results show that deep learning methods can be used to effectively detect malware. The proposed system is a promising new approach to malware detection and can be used to protect computer systems and networks from malware attacks.