SAH-IDA: A Unified Framework for Accurate, Explainable, and Cryptographically Secure Network Intrusion Detection in Cloud Environments

Pushpendra Sharma and S S Sarangdevot

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 3, 253-267

Abstract: Intrusion detection systems (IDSs) used in cloud computing settings have to address three simultaneous structural problems, namely: (a) low classification efficiency for class-imbalance datasets; (b) absence of explanation of decisions at the inference level according to AI regulation requirements; (c) weak protection of results using modern cryptography algorithms. To solve this problem, this article introduces a novel SAH-IDA approach based on a combination of (i) heterogeneous stacking model with four base estimators (Gaussian Naive Bayes, K-Nearest Neighbors, Logistic Regression, and LDA) in logistic regression meta-model, which was trained with the help of fivefold cross-validation algorithm; (ii) decision-level explainability based on KernelSHAP that complies with requirements stated in Article 13 of EU AI Act and Article 22 of GDPR; (iii) neural synchronization technique called Tree Parity Machine combined with AES-128-CBC encryption and SHA-256 integrity check protocol. Tested on the NSL-KDD dataset, which included 125,973 training and 22,544 test observations, SAH-IDA demonstrated 98.41% accuracy and macro-average of F1 = 0.9840 against 98.09% for the most accurate standalone classifier (KNN) as determined using McNemar test (chi-square statistic equals 235.6, p

Keywords: heterogeneous stacking ensemble; KernelSHAP; Tree Parity Machine; AES-128-CBC; SHA-256; network intrusion detection; NSL-KDD; explainable AI; cloud security; trustworthy AI (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT26123317
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v12:y2026:i3:id:2015

DOI: 10.32628/CSEIT26123317

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