Accurate identification of node intrusion behavior in computer networks remains challenging due to the highly dynamic and complex nature of modern network environments, where benign activities are frequently misclassified as malicious events. To address the degradation in detection reliability resulting from such misjudgments, an intrusion identification framework based on an enhanced Recursive Residual Network (RRN) was developed. A statistical classification paradigm was incorporated to process the heterogeneous characteristics of network node intrusion data, enabling a more robust separation of normal and anomalous activity patterns. Features associated with abnormal nodes were extracted, and the range of intrusion-related behavioral deviations was optimized iteratively through an error-minimization function, allowing the model to adapt effectively to fluctuations in network states. A recursive structure derived from Recurrent Neural Network (RNN) principles was subsequently embedded within the residual regression architecture, through which node credit values were continuously iterated and updated to refine the distinction between legitimate behavior and genuine intrusion attempts, enhancing the stability of intrusion identification. In the final stage, a potential loss metric was computed to quantify the expected impact of detected anomalous behaviors on network assets, thereby enabling abnormal behaviors to be classified rigorously as intrusion events when their estimated loss exceeds a critical threshold. Experimental results demonstrate that the proposed method achieves high sensitivity, maintains a stable attack-type identification rate throughout the evaluation period, and reduces the trust value of compromised nodes below 0.15 within 75 seconds, indicating strong effectiveness in distinguishing authentic intrusion behaviors from normal variations. The overall findings suggest that the enhanced RRN offers a resilient and adaptive mechanism for intrusion behavior identification under conditions of complex network dynamics.
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