Enhanced Binary Waterwheel Plant Optimization with Fuzzy Decision Tree for Intrusion Detection in Cloud Environments

In the era of cloud computing, ensuring robust security measures against cyber threats is paramount. This paper introduces an advanced intrusion detection system (IDS) that uses an Enhanced binary Waterwheel Plant Optimization (Eb-WPO) algorithm combined with a Fuzzy Decision Tree (FDT) for improved detection accuracy and efficiency. The proposed Eb-WPO algorithm incorporates adaptive parameter tuning and mutation techniques to effectively balance exploration and exploitation, enhancing the optimization process. This algorithm optimizes the feature selection process, which is crucial for the Fuzzy Decision Tree's performance. The FDT is utilized for its ability to handle uncertainty and imprecision in data, making it an ideal choice for Intrusion Detection in dynamic cloud environments. The hybrid IDS is tested on various benchmark datasets, demonstrating significant improvements in detection rates and reduced false positives compared to traditional methods. The results highlight the system's potential for real-time application in securing cloud infrastructure against sophisticated cyber-attacks.