A novel machine learning-based attacker detection system to secure location aided routing in MANETs. (7th February 2020) - Suma, R.; Premasudha, B.G.; Ram, V. Ravi
The proposed work deals with the improvisation of the performance of location-based routing in mobile ad hoc network (MANET). A machine learning-based attacker detection (MLAD) algorithm that uses multipath routing is proposed to facilitate efficient routing even in the presence of attackers. The proposed algorithm adopts the location aided routing (LAR) to optimise the search process and to reduce the search area for new routes in MANETs. Learning automata (LA) is implemented to optimise the path selection and to reduce overhead in the network. Extended identity-base cryptography (EIBC) is used for efficient key management in providing system security. The proposed system implements privacy preserving communication system (PPCS) for maintaining privacy in end-to-end communication. This method decouples the location information from the node's identifier and abstracts the communication happening among nodes. The simulation results of the proposed method reveal its reliability and strength in securing LAR in MANETs.