Strategic profiling for behaviour visualization of malicious node in MANETs using game theory

• Main Authors: Khan, Burhan ul Islam, Olanrewaju, Rashidah Funke, Mir, Roohie Naaz, Baba, Asifa, Adebayo, Balagon Wasiu
• Format: Article
• Language: English; English
• Published: Little Lion Scientific Islamabad Pakistan 2015
• Subjects: Q Science (General)
• Online Access: http://irep.iium.edu.my/42849/1/42849.pdf; http://irep.iium.edu.my/42849/4/42849_Strategic%20profiling%20for%20behaviour%20visualization%20of%20malicious%20node%20in%20MANETs_SCOPUS.pdf; http://irep.iium.edu.my/42849/; http://www.jatit.org/volumes/Vol77No1/4Vol77No1.pdf
• Institution: Universiti Islam Antarabangsa Malaysia

In Mobile Adhoc Network (MANET), one of the precarious problems is of identifying the malicious nodes. The identification and later mitigation of the same becomes an immensely difficult task especially when selfish / erroneous nodes exist along with normal collaborative nodes in the Regular camp. The presence of selfish nodes is potentially harmful as similar behaviour can be imitated by malicious nodes which are the point of concern of many security aspects. The paper accentuates the use of game theory and probability theory considering selfish nodes in the regular node camp while modelling the Regular versus Malicious node game and thereby enhancing the prior mathematical schema of strategical decision making to accommodate for the same. The framework effectively represent the various unpredictable actions of node cooperation, node declination, node attacks, as well as node reporting that can model the strategic profiling of various mobile nodes. A significant focus is given on Perfect Bayesian Equilibrium (PBE) strategy which forms as the basis of all the result analysis. The enhancement is shown in terms of 63% lesser false positives which favors higher overall network utility (modelled as utility of regular nodes in the game) with selfish / erroneous nodes existing in the network when collating the proposed schema with prior work.