A Novel Approach for detecting Flooding Attacks in Manet Using Machine Learning Methods

Over the years, ensuring security over Mobile Ad-hoc (MANET) Routing has been a challenging task for both the researchers and academicians due to its natural and unique characteristics of mobility, self organizing and free to join/leave features. Generally, an intruder can become part of MANET and can broadcast unnecessary or useless packets over the network to disrupt network activities by increasing network overhead, consuming network bandwidth and deploy the network with battery energy consumption. In the past, many research works were focused and dedicated to address this flooding attack issue. In this work, we propose machine learning based algorithm to detect flooding attacks that considers route discovery history information of each nodes and use it to identify the malicious node if the characteristics deviates from this class information. This work also introduces Enhanced-AODV for preventing flooding attacks (EFAP-AODV) to ensure the network security by effectively detecting and isolating malicious nodes from participating network functionalities. The obtained results were compared with AODV, B-AODV and our proposed approach outplays these two approaches in terms of packet delivery ratio, end to end delay, routing load and malicious node detection ratio.