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The growth of the Internet of Things (IoT) has recently impacted our dailylives in many ways. As a result, a massive volume of data is generated andneeds to be processed in a short period of time. Therefore, the combination ofcomputing models such as cloud computing is necessary. The main disadvantage ofthe cloud platform is its high latency due to the centralized mainframe.Fortunately, a distributed paradigm known as fog computing has emerged toovercome this problem, offering cloud services with low latency and high-accessbandwidth to support many IoT application scenarios. However, Attacks againstfog servers can take many forms, such as Distributed Denial of Service (DDoS)attacks that severely affect the reliability and availability of fog services.To address these challenges, we propose mitigation of Fog computing-based SYNFlood DDoS attacks using an Adaptive Neuro-Fuzzy Inference System (ANFIS) andSoftware Defined Networking (SDN) Assistance (FASA). The simulation resultsshow that FASA system outperforms other algorithms in terms of accuracy,precision, recall, and F1-score. This shows how crucial our system is fordetecting and mitigating TCP SYN floods DDoS attacks.

Toward a real-time TCP SYN Flood DDoS mitigation using Adaptive Neuro-Fuzzy classifier and SDN Assistance in Fog Computing