International Journal of Computer Networks and Applications

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Robust Tristate Security Mechanism to Protect Against Selective Forwarding Attack and Black Hole Attack in Intra-Cluster Multi-Hop Communication


Affiliations
1 Department of Computer Science, Kongunadu Arts and Science College, Coimbatore, Tamil Nadu., India
2 Department of Information Technology, Kongunadu Arts and Science College, Coimbatore, Tamil Nadu., India
 

Security is the most vital issue to be addressed in Wireless Sensor Networks (WSNs). The WSN dominates since it has an effectiveness of applications in numerous fields. Though it has effectiveness towards its applications likewise it is susceptible to two different kinds of attacks (i.e.) external attacks and internal attacks existence of constrained reckoning resources, low memory, inadequate battery lifetime, handling control, and nonexistence of interfere resilient packet. Handle internal attacks such as selective forwarding attacks (SFAs) and black hole attacks (BHA) are considered to be the most common security extortions in wireless sensor networks. The attacker nodes will execute mischievous activities during data communication by creating traffic load, delaying packet delivery, dropping packets selectively or dropping all packets, energy consumption, and depleting all network resources. These attacks can be handled efficiently by implementing the proposed methodology for detecting, preventing, and recovering Cluster Heads (CHs), Cluster Members (CMs), and Transient Nodes (TNs) from SFAs and BHA in intra-cluster multi-hop. It is accomplished by proposing a robust strategy for overcoming internal attacks on cluster head, cluster member, and transient node. The Fuzzy C-Means clustering is used to discover the prominent cluster head. The uncertainty entropy model is used to detect internal attacks by removing the malicious node from the transition path. The intermediate node is been selected based on the degree and dimension. The experimental results of the proposed Robust Tristate Security Mechanism (RTSSM) against SFAs and BHA are evaluated with packet delivery ratio, throughput, and packet drop and the results prove the effectiveness of the proposed methodology and it also aids in the extension of the network lifetime.

Keywords

Cluster Head, Cluster Member, Intra-Cluster, Multi-Hop, Clustering, Wireless Sensor Networks, Uncertainty, Robust, Fuzzy Membership, Entropy.
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  • Jayachandran J, Vimala Devi K, "A Survey on Clustering Algorithms and Proposed Architectural Framework for Border Surveillance System in Wireless Sensor Networks", International Journal of Computer Networks and Applications (IJCNA), 9(6), PP: 785-805, 2022, DOI: 10.22247/ijcna/2022/217710.

  • Iqbal, U., & Mir, A. H. (2022). Secure and practical access control mechanism for WSN with node privacy. Journal of King Saud University-Computer and Information Sciences, 34(6), 3630-3646.

  • Feng H, Fu W. Study of recent development about privacy and security of the internet of things. In: 2010 international conference on web information systems and mining, Sanya, China, 23 October 2010. IEEE, pp. 91–95.

  • Anitha, S.Mythili, “An Approach for Detection and Prevention of Cluster Head and Cluster Member from Selective Forwarding Attacks and Black Hole Attack in Intra-Cluster Multi-Hop Communication,” Design Engineering, Vol. 2021, Issue.06, pp. 1032-1060.

  • Bouarourou, Soukaina & Zannou, Abderrahim & Boulaalam, Abdelhak & Nfaoui, El Habib. (2022). Sensors Deployment in IoT Environment. 10.1007/978-3-031-01942-5_27.

  • Munir, A.; Gordon-Ross, A.; Ranka, S. Multi-core Embedded Wireless Sensor Networks: Architecture and Applications. In IEEE Transactions on Parallel and Distributed Systems (TPDS); IEEE: Piscataway, NJ, USA, 2014; Volume 25, pp. 1553–1562

  • Faris M, Mahmud MN, Salleh MFM, Alnoor A. Wireless sensor network security: A recent review based on state-of-the-art works. International Journal of Engineering Business Management. 2023;15.

  • Tam, Nguyen & Dat, Vi & Lan, Phan & Binh, Huynh & Vinh, Le & Swami, Ananthram. (2021). Multifactorial evolutionary optimization to maximize lifetime of wireless sensor network. Information Sciences. 576. 10.1016/j.ins.2021.06.056.

  • Maivizhi, Radhakrishnan & Yogesh, Palanichamy. (2021). Q-learning based routing for in-network aggregation in wireless sensor networks. Wireless Networks. 27. 1-20. 10.1007/s11276-021-02564-8.

  • Lalitha, K., Thangaraja, R., Siba, K. U., Poongodi, C., & Prasad, S. A. (2017). GCCR: An efficient grid based clustering and combinational routing in wireless sensor networks. Wireless Personal Communications.

  • Turgut, “Analysing Multi-hop Intra-Cluster Communication in Cluster-Based Wireless Sensor Networks”, Natural and Engineering Sciences (2019).

  • Mezghani, M, “An Efficient Multi-Hops Clustering and Data Routing for WSNs based on Khalimsky IpekAbasıkeleş Shortest Paths,” Journal of Ambient and Intelligence and Humanized Computing, Vol. 10, 1275-1288, 2019.

  • D.Wu, S. Gene, X. Cai, G. Zhang and F.Xue, “ A Many-Objective Optimization WSN Energy Balance Model,” KSSII Transactions on Internet and Informatiom Systems, Vol.14, No. 2, pp. 514-537, 2020.

  • H. El Alami and A. Najid, “ECH: An Enhanced Clustering Hierarchy Approach To Maximize Lifetime of Wireless Sensor Networks,” IEEE Access, Vol. 7,pp. 107145-107153,2019.

  • Gohar Ali, Fernando Moreira, Omar Alfandi, Babar Shah and Mohammed Ilyas, “A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks,” Electronics 9, No.4, 2020.

  • Hasan A, Khan MA, Shabir B, Munir A, Malik AW, Anwar Z, Ahmad J. Forensic Analysis of Blackhole Attack in Wireless Sensor Networks/Internet of Things. Applied Sciences. 2022; 12(22):11442.

  • Malik A, Khan MZ, Faisal M, Khan F, Seo J-T. An Efficient Dynamic Solution for the Detection and Prevention of Black Hole Attack in VANETs. Sensors. 2022; 22(5):189

  • Ali, Haider & Tariq, Umair & Hussain, Mubashir & Lu, Liu & Panneerselvam, John & Zhai, Xiaojun. (2020). ARSH-FATI a Novel Metaheuristic for Cluster Head Selection in Wireless Sensor Networks. IEEE Systems Journal. PP. 1-12. 10.1109/JSYST.2020.2986811.

  • Amutha, J. & Sharma, Sandeep & Sharma, Sanjay. (2021). Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions. Computer Science Review. 40. 100376. 10.1016/j.cosrev.2021.100376.

  • Rawat, Piyush & Chauhan, Siddhartha. (2021). Clustering protocols in wireless sensor network: A survey, classification, issues, and future directions. Computer Science Review. 40. 100396. 10.1016/j.cosrev.2021.100396.

  • S. Ramesh, R. Rajalakshmi, Jaiprakash Narain Dwivedi , S. Selvakanmani, Bhaskar Pant, N. Bharath Kumar , Zelalem Fissiha Demssie, Optimization of Leach Protocol in Wireless Sensor Network Using Machine Learning, Computational Intelligence and Neuroscience Volume 2022, Article ID 53932, 8 pages.

  • M. Sangeetha, and A. Sabari, “Genetic optimization of hybrid clustering algorithm in mobile wireless sensor networks,” Sensor Review, vol. 38, no. 4, pp. 526-533, 2018.

  • Zannou, A., Boulaalam, A., & Nfaoui, E. H. (2022). Data Flow Optimization in the Internet of Things. Statistics, Optimization & Information Computing, 10(1), 93-106. https://doi.org/10.19139/soic-2310-5070-1166. How to cite this article:

  • Firas Ali Al-Juboori & Ismail, E. S. F. (2014). A modified fuzzy C-means cluster-based approach for wireless sensor network. The Mediterranean Journal of Electronics and Communications, 10(2).

  • D. Hongjun, J. Zhiping and D. Xiaona, "An Entropy-based Trust Modeling and Evaluation for Wireless Sensor Networks," 2008 International Conference on Embedded Software and Systems, Chengdu, China, 2008, pp. 27-34.


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  • Robust Tristate Security Mechanism to Protect Against Selective Forwarding Attack and Black Hole Attack in Intra-Cluster Multi-Hop Communication

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Authors

A. Anitha
Department of Computer Science, Kongunadu Arts and Science College, Coimbatore, Tamil Nadu., India
S. Mythili
Department of Information Technology, Kongunadu Arts and Science College, Coimbatore, Tamil Nadu., India

Abstract


Security is the most vital issue to be addressed in Wireless Sensor Networks (WSNs). The WSN dominates since it has an effectiveness of applications in numerous fields. Though it has effectiveness towards its applications likewise it is susceptible to two different kinds of attacks (i.e.) external attacks and internal attacks existence of constrained reckoning resources, low memory, inadequate battery lifetime, handling control, and nonexistence of interfere resilient packet. Handle internal attacks such as selective forwarding attacks (SFAs) and black hole attacks (BHA) are considered to be the most common security extortions in wireless sensor networks. The attacker nodes will execute mischievous activities during data communication by creating traffic load, delaying packet delivery, dropping packets selectively or dropping all packets, energy consumption, and depleting all network resources. These attacks can be handled efficiently by implementing the proposed methodology for detecting, preventing, and recovering Cluster Heads (CHs), Cluster Members (CMs), and Transient Nodes (TNs) from SFAs and BHA in intra-cluster multi-hop. It is accomplished by proposing a robust strategy for overcoming internal attacks on cluster head, cluster member, and transient node. The Fuzzy C-Means clustering is used to discover the prominent cluster head. The uncertainty entropy model is used to detect internal attacks by removing the malicious node from the transition path. The intermediate node is been selected based on the degree and dimension. The experimental results of the proposed Robust Tristate Security Mechanism (RTSSM) against SFAs and BHA are evaluated with packet delivery ratio, throughput, and packet drop and the results prove the effectiveness of the proposed methodology and it also aids in the extension of the network lifetime.

Keywords


Cluster Head, Cluster Member, Intra-Cluster, Multi-Hop, Clustering, Wireless Sensor Networks, Uncertainty, Robust, Fuzzy Membership, Entropy.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F221900