SYN Flood Attack Detection based on Bayes Estimator (SFADBE) For MANET
Khalid Hussaina, Syed Jawad Hussaina, Noor Zaman Jhanjhib c, M. Humayund
- aThe University of Lahore, Islamabad, Pakistan
- bSoCIT, Lakeside campus, Taylor’s University, Malaysia
- cSoCIT, Lakeside campus, Taylor's University, Malaysia
- dColege of computer and Information Sciences, Jouf University, Al-Jouf, Saudi Arabia
Papers open inside SciRah from the original publisher or repository; SciRah never hosts them.
Abstract
SYN flood attack is a very serious cause for disturbing the normal traffic in MANET. SYN flood attack takes advantage of the congestion caused by populating a specific route with unwanted traffic that results in the denial of services. In this paper, we proposed an Adaptive Detection Mechanism using Artificial Intelligence technique named as SYN Flood Attack Detection Based on Bayes Estimator (SFADBE) for Mobile ad hoc Network (MANET). In SFADBE, every node will gather the current information of the available channel and the secure and congested free (Best Path) channel for the traffic is selected. Due to constant congestion, the availability of the data path can be the cause of SYN Flood attack. By using this AI technique, we experienced the SYN Flood detection probability more than the others did. Simulation results show that our proposed SFADBE algorithm is low cost and robust as compared to the other existing approaches.