Activation Space Clustering for Transparent AI-Driven Cybersecurity Against Deferred Backdoor Attacks

Adversarial robustness remains a critical challenge in AI-driven cybersecurity systems, particularly against sophisticated threats like Deferred Backdoor Activation (DBA) that exhibit long-term stealth behavior. This paper presents a novel transparent AI framework that integrates activation space clustering with built-in interpretability to enhance cybersecurity defense mechanisms. The proposed methodology introduces the K-Goodall Overlap Prism Medoid (K-GOPM) clustering algorithm to identify and mitigate poisoned samples in neural network activation spaces, effectively preventing DBA before trigger activation. A Built-in Interpretable Deep Few-Shot-Adaptive Lame Recurrent Neural Network (BIDFS-ALRNN) is developed, incorporating Stable and Explainable Attention (SEAT) mechanisms to provide immediate explanations without post-hoc interpretability tools. The framework employs Concentrated Vieta’s Differential Privacy (CVDP) for model privacy protection, Cubic Edwards-377 Curve Cryptography (CEdCC) for data protection, and Leaky Half Angle Bucket Algorithm (LHABA) for query rate limiting to prevent adversarial probing. Experimental validation on the NSL-KDD dataset demonstrates superior performance with 99.32% accuracy and 0.89 adversarial robustness. The proposed approach significantly advances transparent AI methodologies by combining theoretical foundations in activation space analysis with practical cybersecurity applications, contributing to both AI interpretability research and robust defense strategies against evolving adversarial threats in networked systems.