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