An Intelligent LSTM-Based Network Risk Prediction Framework for Time-Stamped Cybersecurity Logs
Abstract
The rapid increase in the time-stamped security logs that record various and different attack patterns is increasing the vulnerability of computer networks. These logs contain useful temporal information, whereas conventional intrusion detection systems typically do not model sequential dependencies and time-lagged relationships among events. The fact that time-stamped cybersecurity logs have grown at a rapid rate, has complicated the process of detecting the changing patterns of attacks within the present computer networks. Conventional intrusion detection schemes tend to not capture serial dependencies and time-based association between occurrences and therefore cannot act promptly and respond predictably. The paper is a proposal of a smart threat prediction system that combines Temporal Pattern Learning and Long Short-Term Memory networks in proactive network risk analysis. The suggested TPL-LSTM model would process and examine sequential security logs to learn latent time variants and long-term dependencies of computer attacks. The framework can detect potential threats early and monitor intrusion in real-time in enterprise Security Operations Centers by establishing lagged relationships among log events. The results of the experimental works prove that the suggested method is more accurate in predictions and quicker on reactions than the traditional method of detection. The research is also a contribution to the field of cybersecurity because it presents a predictive deep learning framework that can be used to improve situational awareness and proactive defense mechanisms against impending attacks in a network.