Zero-Day Threats Detection for Critical Infrastructures

Technological advancements in various industries, such as network intelligence, vehicle networks, e-commerce, the Internet of Things (IoT), ubiquitous computing, and cloud-based applications, have led to an exponential increase volume information flowing through critical systems. As a result, protecting infrastructures from intrusions security threats has become paramount concern field intrusion detection systems (IDS). To address this concern, research paper focuses on importance defending against threats. It proposes computational framework that incorporates feature selection fuzzification. The effectiveness performance proposed are evaluated using NSL-KDD UGRansome datasets combination with selected machine learning (ML) models. findings study highlight fuzzy logic use ensemble enhance ML identifies Random Forest (RF) Extreme Gradient Boosting (XGB) top-performing algorithms detect zero-day attacks. results obtained implemented outperform previous methods documented IDS literature, reaffirming significance safeguarding