Abstract
A hybrid intrusion detection system (IDS) that depends on neuro-fuzzy system (NFS) strategy is proposed which identifies the WSN attacks. IDS which makes utilization of cluster-based engineering with upgraded the low-energy adaptive clustering hierarchy (LEACH) will be simulated for routing that expects to decrease energy utilization level by various sensor nodes. An ID utilizes anomaly detection and misuse detection dependent on NFS which will be changed by incorporating with meta-heuristic optimization strategies for ideally creating fuzzy structure. Fuzzy rule sets alongside the neural network are used to incorporate the location results and determine the attackers kinds of attacks, and the regular procedure of NFS is as per the following: Initially, fuzzy clustering strategy is used to produce distinctive training subsets; in light of unusual training subsets, divergent ANN models are prepared to devise unique base models and fuzzy aggregation module, which is being used to unite these result. The proposed WNFS is created by including the properties of the whale optimization algorithm (WOA) with the neuro-fuzzy architecture. The optimization algorithm selects the appropriate fuzzy rules for the detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Y. Maleh, A. Ezzatib, Y. Qasmaouic, M. Mbidac, A global hybrid intrusion detection system for wireless sensor networks. Procedia Comput. Sci. 52, 1047–1052 (2015)
P. Sarigiannidis, E. Karapistoli, A.A. Economides, Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information. Expert Syst. Appl. 42(21), 7560–7572 (2015)
O. Depren, M. Topallar, E. Anarim, M.K. Ciliz, An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert Syst. Appl. 29(4), 713–722 (2005)
Y. Shen, S. Liu, Z. Zhang, Detection of hello flood attack caused by malicious cluster heads on LEACH protocol. Int. J. Adv. Comput. Technol. 7(2), 40–47 (2015)
S.K. Saini, M. Gupta, Detection of malicious cluster head causing hello flood attack in LEACH protocol in wireless sensor networks. Int. J. Appl. Innov. Eng. Manag. 3(5), 384–391 (2014)
T.M. Rahayu, S.-G. Lee, H.-J. Lee, Security analysis of secure data aggregation protocols in wireless sensor networks, in Proceedings of the 16th International Conference on Advanced Communication Technology, 2014, pp. 471–474
S. Magotra, K. Kumar, Detection of HELLO flood attack on LEACH protocol, in Proceedings of the IEEE International Advance Computing Conference (IACC ’14), Gurgaon, India, 2014, pp. 193–198
S. Shamshirband, N.B. Anuar, M.L.M. Kiah et al., Co-FAIS: cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks. J. Netw. Comput. Appl. 42, 102–117 (2014)
S. Selvakennedy, S. Sinnappan, Y. Shang, A biologically-inspired clustering protocol for wireless sensor networks. Comput. Commun. 30(14–15), 2786–2801 (2007)
Y. Harold Robinson, E. Golden Julie, S. Balaji, A. Ayyasamy, Energy aware clustering scheme in wireless sensor network using neuro-fuzzy approach, in Wireless Personal Communication, vol. 95 (Springer, 2017), pp. 1–19
K. Kapitanova, S.H. Son, K.-D. Kang, Using fuzzy logic for robust event detection in wireless sensor networks, in Advances in Ad-hoc Network, vol. 10 (2012), pp. 709–722
S.A. Khan, B. Daachi, K. Djouani, Application of Fuzzy Inference Systems to Detection of Faults in Wireless Sensor Networks, vol. 94 (Elsevier, 2012), pp. 111–120
S. Jabbar, R. Iram, A.A. Minhas, I. Shafi, S. Khalid, M. Ahmad, Intelligent optimization of wireless sensor networks through bio-inspired computing: survey and future directions. Int. J. Distrib. Sensor Netw. 1–13 (2013)
L.B. Oliveira, H.C. Wong, M. Bern, R. Dahab, A.A.F. Loureiro, SecLeach—a random key distribution solution for securing clustered sensor networks, in Proceedings of the 5th IEEE International Symposium on Network Computing and Applications, Washington, DC, USA, 2006, pp. 145–154
R. Beghdad, Critical study of neural networks in detecting intrusions. Comput. Secur. 27, 168–175 (2008)
S. Mirjalili, A. Lewis, The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
W.R. Heinzelman, A. Chandrakasan, H. Balakrishnan, Energy-efficient communication protocol for wireless microsensor networks, in Proceeding of the 33rd Hawaii International Conference on System Sciences (IEEE, 2000), pp. 1–10
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, R., Athavale, V.A., Mittal, S. (2021). Whale Neuro-fuzzy System for Intrusion Detection in Wireless Sensor Network. In: Gao, XZ., Tiwari, S., Trivedi, M., Mishra, K. (eds) Advances in Computational Intelligence and Communication Technology. Advances in Intelligent Systems and Computing, vol 1086. Springer, Singapore. https://doi.org/10.1007/978-981-15-1275-9_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-1275-9_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1274-2
Online ISBN: 978-981-15-1275-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)