Benchmark evaluation demonstrates high threat detection accuracy in organizational networks, highlighting transparent architectures for proactive defense.
Organizations face increasingly sophisticated cyber threats that traditional reactive cybersecurity approaches cannot adequately address. This research proposes an integrated framework combining Explainable Artificial Intelligence (XAI) with Ordinary Differential Deep Recurrent Unit Neural Network (OD-DRUNN) for proactive organizational threat mitigation. The methodology employs a Minimum Parameterized Muller Spanning Tree algorithm for comprehensive network traffic and user behavior analysis. The OD-DRUNN architecture overcomes traditional deep learning limitations through ordinary differential equation-based parameter isolation, while XAI provides transparent decision-making interpretability for security analysts. Threat severity assessment utilizes potential level scoring, with high-risk scenarios triggering Cycloid Curved Optimized Cryptography enhanced by Bernoulli Distribution-based Tuna Swarm Optimization. Experimental evaluation using the HIKARI-2021 dataset “for review, see ref. 21” demonstrates superior performance: 99.2% vulnerability detection accuracy, 97.8% packet delivery ratio, and 98.8% security level. The framework significantly outperforms existing approaches, providing organizations with comprehensive, interpretable, and proactive cybersecurity defense capabilities against evolving cyber attack vectors.
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Aditya et al. (2025) studied this question.
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