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.

翻译：各行业的技术进步，如网络智能、车载网络、电子商务、物联网（IoT）、普适计算以及基于云的应用，导致通过关键系统流动的信息量呈指数级增长。因此，保护关键基础设施免受入侵和安全威胁已成为入侵检测系统（IDS）领域的首要关注点。为解决这一问题，本研究论文聚焦于防御关键基础设施免受入侵和安全威胁的重要性，并提出一种通过模糊化融入特征选择的计算框架。该框架的有效性和性能通过结合选定的机器学习（ML）模型，在NSL-KDD和UGRansome数据集上进行了评估。研究结果凸显了模糊逻辑以及集成学习在提升ML模型性能方面的有效性。研究识别出随机森林（RF）和极限梯度提升（XGB）作为检测零日攻击的最佳算法。该计算框架获得的性能结果优于以往IDS文献中记载的方法，进一步证实了保护关键基础设施免受入侵和安全威胁的重要性。