Detection of SYN Flood Attack Based on Bays Estimator (DsFaBe) in MANET

SYN flood attacks pose a significant threat to the normal traffic flow in Mobile Ad Hoc Networks (MANETs). These attacks flood the network with unnecessary traffic, leading to congestion on specific routes. In this paper, we introduce an Adaptive Detection Mechanism that employs an Artificial Intelligence Technique. This method, known as SYN Flood Attack Detection Based on Bay Estimator (DsFaBe), is designed specifically for MANETs. Each node collects real-time information about the available channels. The DsFaBe then selects the most secure and congestion-free (Best Path) channel for traffic. Persistent congestion can lead to SYN Flood attacks. However, our use of AI techniques has resulted in a higher probability of detecting SYN Flood attacks compared to other methods. We also found that our proposed algorithm is cost-effective and more robust compared to existing approaches. The results of our simulations support our theory.