Machine Learning Integrated Framework for Malware Analysis Web Application Protection and Network Threat Detection

The issue of security remains one of the most significant today since various industries are forced to address it, particularly in light of the new technological advances in AI and other solutions to the identical issue. The current paper is the outcome of the cooperation of five current cybersecurity implementations that unified their capabilities to solve online security issues. The project will include malware and Python and machine learning malware analysis in a sandbox environment, a deep learning-powered web application firewall, packet sniffing, visualization of the traffic, and the detection of keyloggers, among other things. The sandbox analysis also offered detailed static and dynamic reports of the malicious files and their metadata, imported libraries, registry modifications, and network traffic that therefore offered more to the forensic explanation of malware behavior. The web application firewall that was created with the use of the multiclass deep learning classifier had a test accuracy of 99.6 percent and an F1 score greater than 0.99, thereby making it highly reliable in terms of its ability to predict whether the network traffic will be permitted or rejected. The visualization of network traffic provided the admins with the complete architectural view of the company devices and their interconnections; thereby, the anomalies and situational awareness were also highly enhanced. The presence of keyloggers and their respective traffic patterns are checked using the LightGBM model that would inform you about the keylogging activity in place. The findings suggest that Python and machine learning integration can help develop automated, accurate, and comprehensible solutions. The strategies would possibly reduce the effects of cyber threats even in advance before they occur.