Deep Defender: An Adaptive Edge Cloud Framework for Intelligent DDoS Detection, Deception, and Tracking
Authors
-
Sandeep Bhattacharjee
Assistant Professor, Amity University Kolkata, Major Arterial Road, Rajarhat, Kolkata, West Bengal - 700 135
https://orcid.org/0000-0002-6686-3947
DOI:
https://doi.org/10.17010/ijcs/2026/v11/i4/176099Keywords:
Cloud security, CNN-LSTM, cyberattack detection, deep learning, distributed denial-of-service (DDoS), intrusion detection systems
Publishing Chronology Paper Submission Date : June 6, 2026 ; Paper sent back for Revision : June 12, 2026 ; Paper Acceptance Date : June 15, 2026 ; Paper Published Online : August 5, 2026.
Abstract
Cloud computing environments are increasingly vulnerable to Distributed Denial-of-Service (DDoS) attacks and sophisticated cyber intrusions that compromise service availability, scalability, and operational security. In this research, we have developed a resilient cloud-based intrusion detection framework using a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) deep-learning architecture for intelligent DDoS detection and cyberattack tracking. The proposed framework integrates spatial feature extraction and temporal traffic-learning mechanisms to effectively capture complex network-behavior patterns within heterogeneous cloud environments. We used the NSL-KDD dataset for experimental validation, which included preprocessing, feature normalization, correlation analysis, feature-importance evaluation, PCA visualization, and t-SNE-based nonlinear traffic analysis. The results reflected high detection performance (accuracy-99.14%, precision-99.06%, recall-99.14%, and F1 score-99.04%) besides low false-positive behavior and strong attack-prediction. The developed framework exhibited robust adaptive intrusion-learning capability, reliable multi-class attack classification, and scalable operational suitability for intelligent cloud-security monitoring and resilient cyber-threat management applications.
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