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

TL;DR

This paper presents a real-time method for mitigating TCP SYN Flood DDoS attacks in fog computing environments using an Adaptive Neuro-Fuzzy Inference System combined with SDN assistance, improving detection accuracy and response time.

Contribution

It introduces a novel FASA system that integrates ANFIS and SDN to effectively detect and mitigate SYN Flood DDoS attacks in fog computing, outperforming existing algorithms.

Findings

FASA system achieves higher accuracy, precision, recall, and F1-score.

Simulation results demonstrate superior performance over other algorithms.

Effective real-time detection and mitigation of TCP SYN Flood attacks.

Abstract

The growth of the Internet of Things (IoT) has recently impacted our daily lives in many ways. As a result, a massive volume of data is generated and needs to be processed in a short period of time. Therefore, the combination of computing models such as cloud computing is necessary. The main disadvantage of the cloud platform is its high latency due to the centralized mainframe. Fortunately, a distributed paradigm known as fog computing has emerged to overcome this problem, offering cloud services with low latency and high-access bandwidth to support many IoT application scenarios. However, Attacks against fog 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 SYN Flood DDoS attacks using an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Software Defined Networking (SDN) Assistance (FASA). The simulation results show that FASA system outperforms other algorithms in terms of accuracy, precision, recall, and F1-score. This shows how crucial our system is for detecting and mitigating TCP SYN floods DDoS attacks.