Developing an Integrated Framework Based on Neuro-Fuzzy Logic for an Enhanced Efficacy of Detecting Intrusion in Internet of Things (IOT)

Aarushi Chawla

Vol. 7, Issue 1, Jan-Dec 2021

Page Number: 66 - 76

Abstract:

The Internet of things (IoT) is essential for the most recent advancements having a combination of RFID, sensor hubs, correspondence innovations and conventions. IoT is probably the most recent innovation that has gathered huge exploration because of its capacity to screen real-world phenomena and its suitability to numerous applications. IoTs have a wide scope of utilization, including intelligent urban areas, smart homes, modern areas and so forth. The current situation is exceptionally requesting for sending intelligent sensors into existing applications to convey a completely mechanized framework. The significant issue looked at by IoT's current framework is a security issue. This paper focused on interruption identification in IoT using a neuro-fuzzy methodology. The proposed model examines how the abnormalities discovery combine further developed using the neuro-fuzzy approach system

References

• Denning, Dorothy, 'An Intrusion-Detection Model', IEEE Transactions on Software Engineering, vol.13, no.2, 1987
• Fox, Kevin L., Henning, Rhonda R., and Reed, Jonathan H. 'A Neural Network. Approach Towards Intrusion Detection', 13th National Computer Security Conference, 1990.
• Helman, P. and Liepins, G., 'Statistical foundations of audit trail analysis for the detection of computer misuse', IEEE Trans. on Software Engineering, 1993.
• Cannady J. and Mahaffey J, 'The application of Artificial Neural Networks to Misuse detection: initial results', Georgia Tech Research Institute, 1998.
• Girardin L. and Brodbeck D. 'A Visual Approach for Monitoring Logs', 12th System Administration Conference (LISA '98)', pages 299-308, 1998.
• Girardin L., 'An eye on network intruderadministrator shootouts - UBS UBILAB', 1st Workshop on Intrusion Detection and Network Monitoring (ID '99)', 1999.
• K. S. Anil Kumar and V. Nandamohan, 'Novel Anomaly Intrusion Detection using Neuro-Fuzzy Inference System', IJCSNS, pp.6-11, 2008.
• Elike Hodo, Xavier Bellekens, Andrew Hamilton, Pierre-Louis Dubouilh, 'Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System',International Symposium on Networks, Computers and Communications (ISNCC), 2016.
• Ala Al-Fuqaha, Mohsen Guizani, Mehdi Mohammadi, Mohammed Aledhari and Moussa Ayyash, 'Internet of Things: A Survey on Enabling Technologies, Protocols and Applications',IEEE Communications Surveys & Tutorials, 2015.
• Luigi Atzori, Antonio Iera and Giacomo Morabito, 'The Internet of Things: A survey', Computer Networks, vol. 54, pp. 2787-2805, 2010.
• V. Jyothsna and V. V. Rama Prasad, 'A Review of Anomly Based Intrusion Detection System', IJCA, Vol. 28, no. 7, pp. 26-35, 2011.
• Nilesh B. Nanda and Ajay Parikh, 'Classification and Technical Analysis of Network Intrusion Detection System', International Journal of Advanced Research in Computer Science, vol. 8, pp. 657-661, 2017.
• E. Kesavulu Reddy, 'Neural Networks for Intrusion Detection and Its Applications', World Congress in Engineering, vol. 2, 2013.
• Tariqahmad Sherasiya and Hardik Upadhyay, 'Intrusion Detection System for Internet of Things', IJARIIE, vol. 2, issue.3, pp. 2244-2248, 2016.
• Pavan Pongle and Gurunath Chavan, 'Real Time Intrusion and Wormhole Attack Detection in Internet of Things', IJCA, vol.121, no.9, pp. 1-9, 2015.
• Rupinder Singhm Jatinder Singh and Ravinder Singh, 'Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks', Wireless Communication and Mobile Computing, pp. 1- 14, 2017.
• Laurene Fausett, Fundamentals of Neural networks: Architecture, Algorithm and Applications, Pearson Education 1994.
• Mohit Mittal and Krishan Kumar, 'Data Clustering In Wireless Sensor Network Implemented On Self Organization Feature Map (SOFM) Neural Network' ICCCA, 2016.
• Mittal M., Kumar K., Network Lifetime Enhancement of Homogeneous Sensor Network Using ART1 Neural Network, Sixth International Conference on Computational Intelligence and Communication Networks, pp. 472-475 2014.
• Mittal M., Kumar K., Quality of Services Provisioning in Wireless Sensor Networks using Artificial Neural Network: A Survey, International Journal of Computer Application (IJCA), pp. 28-40 2015.
• Mittal M., Bhadoria R. S., Aspect of ESB with Wireless Sensor Network, Exploring Enterprise Service Bus in the Service-Oriented Architecture Paradigm', igi-global publications, pp. 319, 2017.

Back Download