AN UNSUPERVISED MALWARE DETECTION SYSTEM FOR WINDOWS BASED SYSTEM CALL SEQUENCES

Abstract

Malware attacks have grown in prominence in recent years, posing severe security risks and resulting in significant financial losses. The ability to rapidly and reliably classify malware is vital to cybersecurity due to the exponential growth of malware variants. The role of artificial intelligence plays a significant role in cybersecurity industry. Recently, in the field of malware detection deep learning technique seeks more attention than the machine learning techniques due to the complexity of its behavior. Because the deep learning technique performs well than the machine learning techniques in terms of accuracy and it is well suited for large amount of data. The input attribute for the proposed model is windows-based system call sequence which is collected from NT mal detect project. In this work, the unsupervised deep learning technique used for text classification namely LSTM autoencoder and the performance of proposed model compares with existing DL methods such as CNN, RNN and LSTM with the performance parameters of accuracy, precision, recall and F1-measure.