A Cyber Security Threat Intelligence Framework Using Artificial Intelligence And NLP For Advanced Malware Detection

Abstract

The fast development of advanced cyberattacks and malware types has posed significant problems to the traditional signature-based models of cybersecurity, which, in most cases, cannot detect the zero-day and emerging threats in real-time. Current malware detection methods are also incapable of effectively processing large amount of unstructured cyber threat-intelligence information in the form of security reports, phishing messages, threat feeds, and network logs. To overcome these shortcomings, the present paper suggests a Hybrid AI-NLP Threat Intelligence Framework on Advanced Malware Detection that incorporates both the Artificial Intelligence (AI) and Natural Language Processing (NLP) methods of intelligent cyber threat detection and the malware-classifying techniques. The suggested model utilizes NLP-based threat feature extraction through tokenization, semantics analysis, TF-IDF vectorization and threat entity recognition to process textual intelligence data in the field of cybersecurity. The threat features obtained are then identified with the help of a deep learning-based malware detection engine to identify malicious behavioral patterns as well as advanced cyber threats. Australian benchmark cybersecurity datasets and real-life samples of threat intelligence were used as benchmark test samples. The accuracy of the malware detection in the proposed framework reached 99.12, precision 98.94, recall 98.76, F1-score 98.85, and false positive rate of 0.18. The findings indicate that the suggested AI-based integrated model can greatly enhance the malware detection capacity in the advanced stage, threat intelligence automation, and the efficiency of cybersecurity responses in real-time.