A Rank Based Secure M-WSN Network using Machine Learning for Efficient Routing
Keywords:
DNN, Improvements, QoS, Secure RoutingAbstract
This paper presents an innovative and secure routing mechanism for wireless sensor networks, leveraging a Cluster Head (CH) to CH-based routing inspired by the Ad hoc On-Demand Distance Vector (AODV) protocol. The proposed method evaluates and optimizes key Quality of Service (QoS) parameters, including throughput, packet delivery ratio (PDR), delay, and energy consumption. By categorizing these parameters and analyzing their standard deviations, the method effectively identifies and labels route discoveries as malicious, efficient, or moderate. The labeled data is processed using a deep neural network (DNN) with a sigmoid activation function, trained over 100 epochs, to classify routes and distribute scores equally among nodes, resulting in a comprehensive ranking of nodes based on their performance in the route discovery process. The results demonstrate significant improvements over existing methods by Ismail et al. and Bashar et al., with the proposed mechanism achieving a throughput of 1.347652857, a PDR of 0.839435714, and a delay of 0.47388, outperforming the others in these key metrics. These enhancements ensure more reliable, efficient, and responsive communication within the network, making the proposed method highly suitable for a wide range of applications. The integration of QoS evaluation, standard deviation analysis, and deep learning-based classification, combined with secure routing principles, highlights the potential of this approach to establish new benchmarks in the performance and security of wireless sensor networks.
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T. Panigrahi, A. D. Hanumantharao, G. Panda, B. Majhi, and B. Mulgrew, "Maximum likelihood DOA estimation in distributed wireless sensor network using adaptive particle swarm optimization," in Proceedings of the 2011 International Conference on Communication, Computing & Security, 2011, pp. 134-137.
R. Damasevicius, A. Venckauskas, S. Grigaliunas, J. Toldinas, N. Morkevicius, T. Aleliunas, and P. Smuikys, "LITNET-2020: An annotated real-world network flow dataset for network intrusion detection," Electronics, vol. 9, no. 5, p. 800, 2020.
R. B. Pant, H. P. Halvorsen, F. Skulbru, and S. Mylvaganam, "Intermediate measurement node for extension of wsn coverage," Journal of Cyber Security and Mobility, pp. 29-61, 2012.
S. Gupta and C. Keshavamurthy, "A Cross Layered Network Condition Aware Mobile WSN Routing Protocol for Vehicular Communication Systems," International Journal of Computer Science and Information Security, vol. 14, no. 9, p. 1114, 2016.
R. Ashween, B. Ramakrishnan, and M. Milton Joe, "Energy efficient data gathering technique based on optimal mobile sink node selection for improved network life time in wireless sensor network (WSN)," Wireless Personal Communications, vol. 113, no. 4, pp. 2107-2126, 2020.
G. Liu, H. Zhao, F. Fan, G. Liu, Q. Xu, and S. Nazir, "An enhanced intrusion detection model based on improved kNN in WSNs," Sensors, vol. 22, no. 4, p. 1407, 2022.
B. M. Sahoo, R. K. Rout, S. Umer, and H. M. Pandey, "ANT colony optimization based optimal path selection and data gathering in WSN," in 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM), 2020, pp. 113-119.
J. Amutha, S. Sharma, and J. Nagar, "WSN strategies based on sensors, deployment, sensing models, coverage and energy efficiency: Review, approaches and open issues," Wireless Personal Communications, vol. 111, no. 2, pp. 1089-1115, 2020.
M. M. Nareshbabu, A. S. N. Chakravarthy, and C. Ravindranath, "A secure light weight authentication protocol for wireless sensor network in internet of things," International Journal of Scientific Research in Network Security and Communication, vol. 9, no. 1, pp. 17-19, 2021.
J. Chu, "Performance Modeling for Network Anomaly Detection and Sensor Networks," Doctoral dissertation, City University of New York, 2023.
H. Tabbaa and I. Hafidi, "An Online Model for Detecting Attacks in Wireless Sensor Networks," in International Conference of Machine Learning and Computer Science Applications, Cham: Springer Nature Switzerland, 2022, pp. 271-282.
W. Yao, L. Hu, Y. Hou, and X. Li, "A lightweight intelligent network intrusion detection system using one-class autoencoder and ensemble learning for IoT," Sensors, vol. 23, no. 8, p. 4141, 2023.
T. M. Nguyen, H. H. P. Vo, and M. Yoo, "Enhancing Intrusion Detection in Wireless Sensor Networks Using a GSWO-CatBoost Approach," Sensors, vol. 24, no. 11, p. 3339, 2024.
S. Ismail, Z. El Mrabet, and H. Reza, "An ensemble-based machine learning approach for cyber-attacks detection in wireless sensor networks," Applied Sciences, vol. 13, no. 1, p. 30, 2022.
S. Deshpande, J. Gujarathi, P. Chandre, and P. Nerkar, "A comparative analysis of machine deep learning algorithms for intrusion detection in wsn," Security Issues and Privacy Threats in Smart Ubiquitous Computing, pp. 173-193, 2021.
A. B. Abhale and S. S. Manivannan, "Supervised machine learning classification algorithmic approach for finding anomaly type of intrusion detection in wireless sensor network," Optical Memory and Neural Networks, vol. 29, no. 3, pp. 244-256, 2020.
S. Hemavathi and B. Latha, "HFLFO: Hybrid fuzzy levy flight optimization for improving QoS in wireless sensor network," Ad Hoc Networks, vol. 142, p. 103110, 2023.
R. Ahmad, R. Wazirali, Q. Bsoul, T. Abu-Ain, and W. Abu-Ain, "Feature-selection and mutual-clustering approaches to improve DoS detection and maintain WSNs’ lifetime," Sensors, vol. 21, no. 14, p. 4821, 2021.
K. P. R. Krishna and R. Thirumuru, "A balanced intrusion detection system for wireless sensor networks in a big data environment using CNN-SVM model," Informatics and Automation, vol. 22, no. 6, pp. 1296-1322, 2023.
R. Almesaeed and A. Jedidi, "Dynamic directional routing for mobile wireless sensor networks," Ad Hoc Networks, vol. 110, p. 102301, 2021.
A. Bashar, "Energy efficient multi-tier sustainable secure routing protocol for mobile wireless sensor networks," J. Sustain. Wireless System, vol. 1, no. 2, pp. 87-102, 2019.
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