Integrating NLP for Intelligent Cybersecurity Risk Detection from Bulletins

The rate of developed threats increases and poses challenges when it comes to the recognition of the risks and measures to be taken. Current cybersecurity risk assessment is mainly done using word-of-mouth through reviewed security announcements, vulnerable product or service reports, and threat intelligence data feeds, all of which are time-consuming and often contain human errors. The use of Natural Language Processing (NLP) in cybersecurity brings the efficiency of automation where text information is analyzed with the aim of identifying and categorizing threats in real time. In this paper, NLP techniques including Named Entity Recognition (NER), sentiment analysis and topic modelling are used to analyze the security bulletins and determine the security threats. Supervised, semi-supervised, and unsupervised learning methods of the NLP system can bene fit to mine adequate security-related text data, identify suitable patterns and or make forecasts of susceptible vulnerabilities. The proposed framework is primarily a framework to improve threat intelligence through vulnerability, attack vectors, and related systems connection. In addition, NLP-based risk identification also has several advantages: it increases the accuracy of risk identification and decreases the time for risk detection, as well as strengthens cybersecurity infrastructure. The paper demonstrates that by applying the technique of IBM security bulletins, organizations can reduce the risks and improve the controls effectively. The paper concludes with areas of future work in enhancing the application of NLP techniques in cybersecurity with an agreement on the idea of adaptive models that reflects the dynamic and ever-changing nature and trends of cyber threats.