A hybrid learning-based intrusion detection framework for emerging network attacks with LIME-driven interpretability

Faisal, Md. MahirHossain, ZabiaIslam, Rahageer SaadmanSarkar, Lindsay PrachiKadir, Md. AbdulAhmed, Md. SabbirHossain, Muhammad Iqbal2026-10-052026-10-052025-01-01M. M. Faisal et al., "A Hybrid Learning-Based Intrusion Detection Framework for Emerging Network Attacks with LIME - Driven Interpretability," 2025 28th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2025, pp. 1606-1611, doi: 10.1109/ICCIT68739.2025.11491180.97983315786712-s2.0-105041656645https://hdl.handle.net/10361/30417The fast-developing artificial intelligence (AI) in cybersecurity has brought the most recent prospects and threats. This paper examines the weaknesses of AI-based Intrusion Detection Systems (IDS), especially in competitive and adversarial uses that strive to cause model misbehaviors. Starting with conducting a thorough literature review, we are discussing the current methodologies within AI-based IDS and indicating the issues of obscurity of models, scalability, and robustness. Then, a variety of models are applied, ensemble and ordinary machine learning, decision trees, random forests, gradient-based (XGBoost and LightGBM), etc. In order to further promote model reliability and transparency, the Explainable AI (XAI) technique is incorporated, paying particular attention to the LIME (Local Interpretable Model-Agnostic Explanations) method of AI decision-making interpretation. Moreover, we also build and test hybrid ensemble models in order to enhance the accuracy of detection and adversarial resilience. The paper concludes with a demonstration of how explainability and ensemble can be combined to have stronger and more trustworthy and effective intrusion detection frameworks.6 Pagesen-USTelemetryPayloadsAerospace and electronic systemsMilitary aircraftSpace technologyRadio broadcastingFrequency modulationDistributed denial-of-service attackIP networksIntrusion Detection System (IDS)Explainable AI (XAI)Competitive attacksAdversarial threatsXGBoostEnsemble learningCybersecurityIntrusion detection systems (Computer security).A hybrid learning-based intrusion detection framework for emerging network attacks with LIME-driven interpretabilityConference Proceeding10.1109/ICCIT68739.2025.11491180