<![CDATA[With the proliferation of networked systems, intrusion detection systems (IDS) have become vital in identifying and mitigating cyber threats and unauthorized access. Traditional IDS approaches, such as signature-based and anomaly-based methods, often struggle to detect novel attacks and tend to generate high false alarm rates. This study presents a robust, fuzzy logic-based IDS designed to detect network intrusions and assess their risk levels while minimizing false positives. The IDS classifies network intrusions by analyzing parameters such as source bytes, destination bytes, and packet rates, categorizing them into risk levels through defined fuzzy rules. Implemented in Python using libraries like scikit-fuzzy and pandas, the system utilizes the KDD Cup 99 dataset, a widely recognized IDS benchmark. Fuzzy membership functions and inference rules were defined for the primary input variables, enabling the system to infer intrusion likelihood. The IDS was tested using both two-variable and multi-variable input setups. It achieved a precision of 0.89, a recall of 0.85, and an F1-score of 0.87 in the multi-variable scenario. Results indicate that the fuzzy logic-based IDS achieves a balanced trade-off between detection accuracy and interpretability. It offers a transparent decision-making framework suitable for real-time applications due to its adaptability and potential for integration with live data streams. This research proposes future improvements by creating a foundation for hybrid intrusion detection systems (IDS) that integrate fuzzy logic and machine learning to enhance accuracy and interpretability. It recommends future research on adaptive fuzzy rules, real-time data processing, and explainable AI (XAI) to improve system flexibility, responsiveness, and transparency in cybersecurity applications.]]>
