A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing

Network slicing is recognized as a key enabler for 5G and beyond (B5G) services. However, its dynamic nature and the growing sophistication of DDoS attacks put it at risk of Economical Denial of Sustainability (EDoS) attack, causing economic losses to service provider due to the increased elastic use of resources. Motivated by the limitations of existing solutions, we propose FortisEDoS, a novel framework that aims at enabling EDoS-aware elastic B5G services. FortisEDoS integrates a new deep learning-based DDoS anomaly detection model, called CG-GRU, that leverages the capabilities of emerging graph and recurrent neural networks in capturing spatio-temporal correlations to accurately identify malicious behavior, allowing proactive mitigation of EDoS attacks. Moreover, FortisEDoS uses transfer learning to effectively counteract EDoS attacks in newly deployed slices by leveraging the knowledge acquired in previously deployed slice. The experimental results show the superiority of transfer learning-powered CG-GRU in achieving higher detection performance with lower computation overhead, compared to other baseline methods.