Adaptive Fuzzy-Neural Architectures for Explainable Intrusion Detection in Big Data Environments

Modern connected systems face unexpected challenges because of accelerating digital data growth when identifying and stopping security threats. Traditional intrusion detection systems operate poorly in dynamic environments because they provide insufficient adaptability and transparency if scrutinized. An Adaptive Fuzzy-Neural Architecture presents itself as a new solution which unifies neural network learning with fuzzy logic interpretability to implement secure intrusion detection systems in big data environments. The model utilizes learning mechanisms that adapt dynamically to evolving threats patterns and its fuzzy inference system provides transparent decision capabilities. Numerous experiments using benchmark intrusion datasets show how the proposed model excels in detection accuracy together with being scalable and interpretable over existing conventional approaches. The system delivers actionable threat action information for real-time security intelligence through its explainable features. Such an architecture represents a promising approach for building intelligent security frameworks that demonstrated scalability and interpretability within the context of big data environments.