TAE-IDS: a trust-aware explainable intrusion detection framework using attention-based meta-ensemble learning with blockchain validation.

Recent intrusion detection systems (IDS) increasingly rely on machine learning (ML) and deep learning techniques to detect sophisticated cyberattacks. However, many existing frameworks still suffer from limited explainability, black-box decision-making, and the absence of secure trust verification mechanisms for intrusion records. To address these challenges, this paper proposes TAE-IDS, a Trust-Aware Explainable Intrusion Detection Framework that integrates attention-based meta-ensemble learning, SHapley Additive exPlanations (SHAP)-driven explainability, and blockchain-inspired tamper-evident validation within a unified cybersecurity architecture. The proposed framework employs heterogeneous base classifiers, namely Logistic Regression (LR), Extra Trees (ET), and XGBoost (XGB), to capture diverse network traffic characteristics. Uncertainty-aware meta-features, including logits, confidence scores, and entropy representations, are extracted from the base learners and processed by an adaptive Bidirectional Long Short-Term Memory (BiLSTM) attention-based meta-classifier for contextual intrusion reasoning and adaptive ensemble aggregation. To enhance transparency and analyst trust, SHAP-based explainability is incorporated to provide both global and local interpretations of intrusion predictions. Furthermore, a blockchain-inspired tamper-evident validation mechanism based on SHA-256 cryptographic hashing is integrated to enable tamper-proof intrusion logging, immutable auditing, and secure forensic verification of IDS outputs. The proposed framework was evaluated on the UNSW-NB15 and CICIDS2017 benchmark datasets under both binary and multiclass intrusion detection settings. Experimental results demonstrate that TAE-IDS achieves strong intrusion detection performance, interpretable intrusion reasoning, and effective blockchain-assisted tamper-evident validation on the evaluated benchmark datasets. The integration of explainable artificial intelligence (XAI) and blockchain-assisted validation enhances transparency, forensic traceability, and the integrity of intrusion records while providing a foundation for future validation in operational network environments.