DeepDefender: A Hybrid Deep Learning Driven Model for Intelligent and Context-Aware Attack Detection in SDN Environments

As software-defined networking (SDN) continues to expand in data centers, cloud systems, and new types of networks, secure network protection and successful detection of attacks has become a central concern. Despite the proposed machine learning (ML) and deep learning (DL)–based intrusion detection systems (IDS), the currently available research still bears the challenge of a high false alarm rate, limited ability to adapt to dynamic attacks, a large amount of resources, and low detection accuracy in a real-time SDN setting. In order to overcome these shortcomings, the following paper introduces a new hybrid drive DL-based model, AetherNet-ID, of intelligent and context-relevant detection of attack in the SDN environment. This work makes three most important contributions. On the one hand, a Maximal Information Coefficient Graph–based Feature Ranking (MIC-Gra) approach is suggested to efficiently select the subset of discriminative as well as nonredundant features in the data of a complex traffic dataset. Second, an Attentive Temporal-Hierarchical Network (AetherNet-ID) is proposed, that is a combination of spatial feature encoders and a temporal sequence adapter in the attention-based manner that aimsat learning fine-grained behavioral details and large-scale patterns of intrusion at the same time. Third, a Dual Revolutionary Optimization for Fusion (DRO-Fuse) method is proposed to predict optimal fusion weights to multi-stream fusion to improve reliability and robustness in the decisions. The proposed model was thoroughly tested against gold-standard datasets based on InSDN, ToN-IoT, and CIC-DDoS2019. The test outcomes on the proposed model show high accuracy in detecting at almost 99% and high precision and recall of nearly 0.99 and detection time of only 0.15 s with smaller memory usage of only 60 MB, which are considerably better than the ML and the DL-based models that currently exist.