ItemConference Paper

A Deep Learning-Enhanced Adaptive Intrusion Detection Framework for Real-Time Cyber Security Threat Analysis

Title
A Deep Learning-Enhanced Adaptive Intrusion Detection Framework for Real-Time Cyber Security Threat Analysis
Creator
Vijay, B.; Chandra, J.; Nagendra, N.; Shobana, G.; Karunya, S.; Saranya, K.
Description
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.
Source
Proceedings of the 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing, ICAUC 2026;pp.959-966
Date
01-01-2026
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Concept Drift Adaptation; Cyber Security; Deep Learning; Explainable Artificial Intelligence (XAI); Intrusion Detection System (IDS); SHAP and Attention Mechanisms
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155851-2;
Format
online
Language
English
Type
Conference paper

Citation

Vijay, B.; Chandra, J.; Nagendra, N.; Shobana, G.; Karunya, S.; Saranya, K., “A Deep Learning-Enhanced Adaptive Intrusion Detection Framework for Real-Time Cyber Security Threat Analysis,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25909.

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