A novel Bayesian-inspired framework for proactive detection and mitigation of zero-day attacks in distributed network architectures

The rise of zero-day attacks in distributed networks make it essential to have sophisticated detection mechanisms that can recognize attacks even if they have never been seen before. In this study we propose a novel Bayesian Probabilistic Network (BPN) Framework for proactive zero-day attack detection based on modeling the dependencies between network events and real-time basis anomaly detection. A hybrid detection mechanism based on Bayesian inference for probabilistic threat prediction and a novel MCMC-based learning algorithm to dynamically update the threat model integrates into the framework. We implement the proposed framework in a simulated distributed network environment and evaluate it using the NSL-KDD and UNSW-NB15 datasets. The experimental results show a detection accuracy of 96.2% and a false-positive rate of 3.8% which is much lower than the existing intrusion detection systems. The proposed framework, which has been tested successfully on several well-known datasets, introduces a scalable and adaptive measure to minimize the impact of zero-day vulnerabilities, thereby paving the way for enhanced, next-generation intelligent cybersecurity systems for large-scale distributed systems.