ANFIS based Trust Management Model to Enhance Location Privacy in Underwater Wireless Sensor Networks. Arifeen, M. M., Islam, A. A., Rahman, M. M., Taher, K. A., Islam, M. M., & Kaiser, M. S. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pages 1–6, February, 2019.
doi  abstract   bibtex   
Trust management is a promising alternative solution to different complex security algorithms for Underwater Wireless Sensor Networks (UWSN) applications due to its several resource constraint behaviour. In this work, we have proposed a trust management model to improve location privacy of the UWSN. Adaptive Neuro Fuzzy Inference System (ANFIS) has been exploited to evaluate trustworthiness of a sensor node. Also Markov Decision Process (MDP) has been considered. At each state of the MDP, a sensor node evaluates trust behaviour of forwarding node utilizing the FIS learning rules and selects a trusted node. Simulation has been conducted in MATLAB and simulation results show that the detection accuracy of trustworthiness is 91.2% which is greater than Knowledge Discovery and Data Mining (KDD) 99 intrusion detection based dataset. So, in our model 91.2% trustworthiness is necessary to be a trusted node otherwise it will be treated as a malicious or compromised node. Our proposed model can successfully eliminate the possibility of occurring any compromised or malicious node in the network.
@inproceedings{arifeen_anfis_2019,
	title = {{ANFIS} based {Trust} {Management} {Model} to {Enhance} {Location} {Privacy} in {Underwater} {Wireless} {Sensor} {Networks}},
	doi = {10.1109/ECACE.2019.8679165},
	abstract = {Trust management is a promising alternative solution to different complex security algorithms for Underwater Wireless Sensor Networks (UWSN) applications due to its several resource constraint behaviour. In this work, we have proposed a trust management model to improve location privacy of the UWSN. Adaptive Neuro Fuzzy Inference System (ANFIS) has been exploited to evaluate trustworthiness of a sensor node. Also Markov Decision Process (MDP) has been considered. At each state of the MDP, a sensor node evaluates trust behaviour of forwarding node utilizing the FIS learning rules and selects a trusted node. Simulation has been conducted in MATLAB and simulation results show that the detection accuracy of trustworthiness is 91.2\% which is greater than Knowledge Discovery and Data Mining (KDD) 99 intrusion detection based dataset. So, in our model 91.2\% trustworthiness is necessary to be a trusted node otherwise it will be treated as a malicious or compromised node. Our proposed model can successfully eliminate the possibility of occurring any compromised or malicious node in the network.},
	booktitle = {2019 {International} {Conference} on {Electrical}, {Computer} and {Communication} {Engineering} ({ECCE})},
	author = {Arifeen, M. M. and Islam, A. A. and Rahman, M. M. and Taher, K. A. and Islam, M. M. and Kaiser, M. S.},
	month = feb,
	year = {2019},
	keywords = {ANFIS, Authentication, FIS learning rules, Information and communication technology, MDP, Markov decision process, Markov processes, Matlab, Privacy, Routing, Trust Management, Trust management, UWSN, Wireless sensor networks, adaptive neuro fuzzy inference system, complex security, data mining, data privacy, forwarding node, intrusion detection, location privacy, malicious node, marine communication, resource constraint behaviour, sensor node, telecommunication security, trust behaviour, trust management model, trusted node, underwater wireless sensor networks, wireless sensor networks},
	pages = {1--6},
}

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