Malware Detection and Classification System based on Behavior

With the rapid advancement of network technology, network security issues have become increasingly prominent, particularly the frequent occurrence of malicious behaviors that pose significant threats to both individuals and organizations.However, attackers are now more easily bypassing traditional network intrusion detection systems.The detection of such malicious behavior is crucial, regarding data security and impacts the system's stability and credibility.This study explored the application of deep learning for detecting such actions and analyzed its advantages over traditional methods.For comparative analysis, we employed a variety of machine learning algorithms, including CNNs, random forests, and support vector machines,.Additionally, we proposed a novel model that combines a CNN with long short-term memory networks.We experimentally verified the algorithm's effectiveness in practical applications.