A comprehensive framework for cyber threat detection: leveraging AI, NLP, and malware analysis

A comprehensive framework for cyber threat detection: leveraging AI, NLP, and malware analysis

연구 분야: Safety

학회: International Journal of Information Technology

In today’s fast-paced cybersecurity landscape, vulnerabilities such as Follina (CVE-2022–30190) highlight the increasing risks software applications face, especially in terms of safeguarding user data and ensuring system integrity. Exploits like these, which manipulate document-based flaws to execute malicious code, demonstrate the critical need for enhanced protection strategies. This paper merges the technologies useful in real-time protection from malware by merging Natural Language Processing, Artificial Intelligence, and superior malware analysis. Our method starts with the use of Natural Language Processing to scan the logs of the system for indications of wrongdoing. These behaviors may many times act as an early alert to intrusion such as a change in the patterns of file access or running of scripts. Similarly, the use of other Artificial Intelligence-based procedures is utilized for real-time monitoring of network traffic in an attempt to detect any signs of an ongoing cyber attack. Due to its way of learning, the Artificial Intelligence model is capable of detecting even slight shifts in the flow of data and com, and s that may not be detected in rule-based systems. From these findings, the next step will entail a codebase analysis to determine vulnerabilities in their raw form. This is because while the framework focuses on review of code for vulnerabilities, and more especially those of the type that leads to remote code execution or privilege escalation, it gives a proactive layer of protection. Once vulnerabilities are recognized, they are promptly remedied by various controls inclusive of patching. The algorithm has an almost perfect efficiency of successful threat identification. Such high accuracy proves the efficiency of integrating Artificial Intelligence into one system with Natural Language Processing and malware investigation. This makes it possible to apply any software type and flexibility to counter all types of cyber threats hence is very effective. This paper’s proposed strategy can be useful as a resource for developers, IT specialists, and cybersecurity experts who wish to strengthen their defenses against ever-emerging threats like Follina.