Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vijay, B.; Chandra, J.; Nagendra, N.; Shobana, G.; Karunya, S.; Saranya, K. Title A name given to the resource A Deep Learning-Enhanced Adaptive Intrusion Detection Framework for Real-Time Cyber Security Threat Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Proceedings of the 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing, ICAUC 2026;pp.959-966 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICAUC68182.2026.11441145" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAUC68182.2026.11441145</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037449954?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037449954?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vijay B., Christ University, Bangalore, India; Chandra J., Christ University, Department of Computer Science and Engineering, Bangalore, India; Nagendra N., Ernst and Young, Bangalore, India; Shobana G., Sri Eshwar College of Engineering, Department of Artificial Intelligence and Data Science, Coimbatore, India; Karunya S., Dwaraka Doss Goverdhan Doss Vaishnav College, Pg Department of Information Technology and Bca, Chennai, India; Saranya K., Bannari Amman Institute of Technology, Department of Computer Science and Engineering, Sathyamangalam, India Description An account of the resource The rapidly evolving nature of cyber-attacks significantly reduces the effectiveness of conventional intrusion detection systems (IDS) that rely on static rules and signatures. This work presents an adaptive deep learning- based intrusion detection framework designed to maintain reliable performance in real-time environments affected by concept drift. The proposed approach integrates one-dimensional convolutional neural networks (1D-CNN) for local feature interaction learning with a bidirectional long short-term memory (BiLSTM) network to model sequential network traffic behavior. To address evolving attack patterns, a sliding-window-based incremental learning mechanism is employed, enabling continuous model adaptation to recent traffic characteristics. The model is trained using cross-entropy loss optimized with the Adam optimizer, while dropout regularization is applied to reduce overfitting and ensure fast convergence. To enhance transparency and analyst trust, explainable artificial intelligence techniques are incorporated, including SHAP-based feature attribution and an attention mechanism for interpreting temporal dependencies. Experimental evaluation on labeled network traffic data demonstrates stable convergence, consistent detection accuracy under changing traffic conditions, and improved robustness compared to non-adaptive baseline models. These results confirm the effectiveness and practical applicability of the proposed framework for real-time and interpretable cybersecurity intrusion detection. 2026 IEEE. Subject The topic of the resource Concept Drift Adaptation; Cyber Security; Deep Learning; Explainable Artificial Intelligence (XAI); Intrusion Detection System (IDS); SHAP and Attention Mechanisms Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155851-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online