International Journal of Computer Networks and Applications

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Performance Analysis of Malicious Node Detection in Wireless Multimedia Sensor Networks using Modified LeNET Architecture


Affiliations
1 Faculty of Electronics, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
2 Department of ECE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
 

The routing performance in Wireless Multimedia Sensor Network (WMSN), which transfers and receives multimedia content like a scalar, audio and video, is often affected by malicious nodes and residual nodes. An external attacker modifies the characteristics function of the node, and thus the node becomes malicious nodes in WMSN. These malicious nodes will affect the functionalities of its surrounding nodes and prevent routing through it and other nodes. Hence, the detection and mitigation of malicious nodes are essential to improve the routing efficiency in WMSN. The conventional methods mainly used machine learning algorithms to identify the malicious nodes in WMSN, which provided low accuracy and consumed more detection time as the main drawbacks. The proposed methodology resolves these drawbacks of the conventional algorithms in this paper. This paper presents an efficient method for detecting and mitigating the malicious nodes using feature index, which is optimized by a Genetic Algorithm (GA). The optimized feature set is classified by the modified LeNET deep learning classification approach. Even though conventional deep learning architectures provide a high classification rate for malicious node detection, it consumes a high detection time to identify malicious nodes. This drawback is overcome by modifying the internal layers of the existing LeNET architecture into parallel, and the dense layers in the existing LeNET architecture are replaced by Fuzzy C Means (FCM) algorithm. The performance of the proposed methodology is analyzed with respect to misclassification rate, precision, recall, accuracy and F1-score parameters.

Keywords

Wireless Multimedia Sensor Network, Malicious Node, Detection, Mitigation, Classification, Genetic Algorithm, FCM.
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  • Jin, S., Yuanzhi, W. & Yining, S., “Design and implementation of wireless multimedia sensor network node based on FPGA and binocular vision”, J Wireless Com Network (2018) 2018: 163.

  • Zou K., Ouyang Y., Niu C., Zou Y. (2012) Simulation of Malicious Nodes Detection Based on Machine Learing for WSN. In: Liu C., Wang L., Yang A. (eds) Information Computing and Applications. ICICA 2012. Communications in Computer and Information Science, vol 307. Springer, Berlin, Heidelberg.

  • Roman, R., Zhou, J., Lopez, J.: Applying intrusion detection systems to wireless sensor networks. In: CCNC 2006: Proceeding of the 3rd IEEE Consumer Communications and Networking Conference, pp. 640–644. IEEE, NJ (2006) [

  • Kaplantzis, S., Shilton, A., Mani, N., et al.: Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines. In: ISSNIP, vol. 10, pp. 335–340 (2007).

  • Liu, H., Cui, J., Dai, H.: Malicious nodes detecting in wireless sensor networks based on multivariate classification algorithm. Journal of Sensor Technology 24, 771–777 (2011).

  • Xiao, D., Chen, C., Chen, G.: Intrusion detection based security architecture for wireless sensor networks. In: ISCIT 2005: Proceeding of the International Symposium on Communications and Information Technologies, pp. 1365–1368. IEEE, WuHan (2005).

  • Shaikh RA, Jameel H, d’Auriol BJ, Lee H, Lee S (2009) Group-based trust management scheme for clustered wireless sensor networks. IEEE Trans Parallel Distrib Syst 20(11):1698–1712.

  • Huang X, Fang Y (2008) Multiconstrained qos multipath routing in wireless sensor networks. Wirel Netw 14(4):465–478.

  • Zhiming Zhang, Yu Yang, Wei Yang, Fuying Wu, Ping Li, XiaoyongXiong, "Detection and Location of Malicious Nodes Based on Homomorphic Fingerprinting in Wireless Sensor Networks", Security and Communication Networks, vol. 2021, Article ID 9082570, 12 pages, 2021.

  • L. K. Ramasamy, F. Khan K. P., A. L. Imoize, J. O. Ogbebor, S. Kadry and S. Rho, "Blockchain-Based Wireless Sensor Networks for Malicious Node Detection: A Survey," in IEEE Access, vol. 9, pp. 128765-128785, 2021.

  • Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53 (2021).

  • M. Koyuncu, A. Yazici, M. Civelek, A. Cosar and M. Sert, "Visual and Auditory Data Fusion for Energy-Efficient and Improved Object Recognition in Wireless Multimedia Sensor Networks," in IEEE Sensors Journal, vol. 19, no. 5, pp. 1839-1849, 1 March1, 2019.

  • Ahmed Salim, WalidOsamy and Ahmed M. Khedr , “Effective Scheduling Strategy in Wireless Multimedia Sensor Networks for Critical Surveillance Applications”, Appl. Math. Inf. Sci. 12, No. 1, 101-111 (2018).

  • Harsh Bhatt; MalaramKumhar, “QoE Aware Routing Protocols in Wireless Multimedia Sensor Networks: A Survey”, IJCSN - International Journal of Computer Science and Network, Volume 7, Issue 1, February 2018.

  • J. Pournazari, M. Alaei and F. Yazdanpanah, "A method for coverage optimization in wireless multimedia sensor networks," 2016 Eighth International Conference on Information and Knowledge Technology (IKT), Hamedan, 2016, pp. 128-133.

  • Li W. (2012) PSO Based Wireless Sensor Networks Coverage Optimization on DEMs. In: Huang DS.,Gan Y., Gupta P., Gromiha M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science, vol 6839. Springer, Berlin, Heidelberg.

  • Domínguez-Medina C., Cruz-Cortés N. (2010) Routing Algorithms for Wireless Sensor Networks Using Ant Colony Optimization. In: Sidorov G., Hernández Aguirre A., Reyes García C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science, vol 6438. Springer, Berlin, Heidelberg.

  • Zhao Jing, Zeng Jian-Chao, "A virtual centripetal force-based coverage-enhancing algorithm for wireless multimedia sensor networks", Sensors Journal (IEEE), vol. 10, no. 8, pp. 1328-1334, 2010.

  • Haining Chen, Hongyi Wu, Nian-Feng Tzeng, "Grid-based Approach for Working Node Selection in Wireless Sensor Networks", Communications IEEE International Conference, vol. 6, pp. 3673-3678, 2009.

  • Muhammad Kamal Amjad, ShahidIkramullah Butt, RubeenaKousar, et al., “Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems,” Mathematical Problems in Engineering, vol. 2018, Article ID 9270802, 32 pages, 2018.

  • Li X, Zhang J, Yin M., “Animal migration optimization: an optimization algorithm inspired by animal migration behavior,” Neural Comput & Applic., vol. 24, no. (7–8), pp. 1867–1877, 2013.

  • Rinki Gupta, SreeramanRajan, “Comparative Analysis of Convolution Neural Network Models for Continuous Indian Sign Language Classification”, Procedia Computer Science 171 (2020) 1542–1550.

  • Jiashun Chen, Hao Zhang, Dechang Pi, Mehmed Kantardzic, Qi Yin, Xin Liu, "A Weight Possibilistic Fuzzy C-Means Clustering Algorithm", Scientific Programming, vol. 2021, Article ID 9965813, 10 pages, 2021.

  • M. S. Nidhya, Dr. M. Vanitha, Dr. L. Jayanthi, Mr. L. Vadivel Kannan, Mr. S. Ajay, Mr. S. Gowdham Kumar, Dr. S. Sangeetha. (2021). A Detection of Malicious Nodes in HCCT Model for Wireless Sensor Network. Annals of the Romanian Society for Cell Biology, 1570–1579.

  • Chen Jian, Zhang Lu, KuoYonghong, "Coverage-enhancing algorithm based on overlap-sense ratio in wireless multimedia sensor networks", Sensors Journal (IEEE), vol. 13, no. 6, pp. 2077-2083, 2013.


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  • Performance Analysis of Malicious Node Detection in Wireless Multimedia Sensor Networks using Modified LeNET Architecture

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Authors

S. Arockia Jayadhas
Faculty of Electronics, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
S. Emalda Roslin
Department of ECE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

Abstract


The routing performance in Wireless Multimedia Sensor Network (WMSN), which transfers and receives multimedia content like a scalar, audio and video, is often affected by malicious nodes and residual nodes. An external attacker modifies the characteristics function of the node, and thus the node becomes malicious nodes in WMSN. These malicious nodes will affect the functionalities of its surrounding nodes and prevent routing through it and other nodes. Hence, the detection and mitigation of malicious nodes are essential to improve the routing efficiency in WMSN. The conventional methods mainly used machine learning algorithms to identify the malicious nodes in WMSN, which provided low accuracy and consumed more detection time as the main drawbacks. The proposed methodology resolves these drawbacks of the conventional algorithms in this paper. This paper presents an efficient method for detecting and mitigating the malicious nodes using feature index, which is optimized by a Genetic Algorithm (GA). The optimized feature set is classified by the modified LeNET deep learning classification approach. Even though conventional deep learning architectures provide a high classification rate for malicious node detection, it consumes a high detection time to identify malicious nodes. This drawback is overcome by modifying the internal layers of the existing LeNET architecture into parallel, and the dense layers in the existing LeNET architecture are replaced by Fuzzy C Means (FCM) algorithm. The performance of the proposed methodology is analyzed with respect to misclassification rate, precision, recall, accuracy and F1-score parameters.

Keywords


Wireless Multimedia Sensor Network, Malicious Node, Detection, Mitigation, Classification, Genetic Algorithm, FCM.

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F212334