JMCER

Detection of Security Attacks in Wireless Sensor Networks

  • Received
    November 10, 2022
  • Revised
    December 13, 2022
  • Accepted
    December 16, 2022
  • Published
    December 16, 2022

Authors

  • Sura Alsharifi
  • Mafaz Alanezi

Abstract

One of the primary concerns in sensor networks is Security. Wireless Sensor Network involves a considerable number of nodes which can be known as” tiny sensor nodes”. These nodes swap the information with other nodes by special wireless links in a short period. The information is perhaps private for many people or business dealings. These networks are exposed to many attackers due to deployment in distant areas and distributed behavior. At the sensor node level, the networks are controlled by different constraints like less memory capacity, low transmission range, and less battery power. Where at the network level, they are controlled by irregular connectivity and Adhoc networking. This research may analyze the main security issues, security breaches, and challenges in the Wireless Sensor Networks world based on the analysis of Low Energy Adaptive Clustering Hierarchy (LEACH), Which is a cluster-based routing protocol by varying the number of clusters to observe the effect on Sensor Network performance in terms of network lifetime, energy dissipation, and amount of data reaching the base station. A scheme has been proposed for collecting and processing the data and then producing 12 features. This ordered dataset is called Wireless Sensor Networks- Data Set (WSN-DS). Support Vector Machine (SVM) was trained on the dataset for detecting and classifying the DoS attacks where 80% for training and 20% for testing. The results section shows how the WSN DS increased the capability of the Intrusion Detection System (IDS) to realize a better classification accuracy rate. The classification accuracies of attacks were 100%, and 98% for Normal, Black hole, and Sinkhole attacks.

Keywords

Support vector machine (SVM), DDos, Blackhole, Sinkhole, Low-Energy Adaptive Clustering Hierarchy (LEACH) Protocol, Wireless Sensor Networks (WSN)

References

Abid, S.H. 2018. OSCH-LEACH: Optimum Secondary Cluster Head Selection for LEACH Protocol. AL-MANSOUR JOURNALAL-MANSOUR JOURNAL, (30)

Abraham, R.a.M., S. 2018. A study of Low Energy Adaptive Clustering Hierarchy (LEACH) Protocol in Wireless Sensor Network. International Journal of Engineering Science Invention 7(8 Ver. V ) 99-102

Ahlawat, S.a.C., Amit 2020. Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Computer Science 167 2554–2560. doi:10.1016/j.procs.2020.03.309

Al-Maslamani, N.a.A., Mohamed 2020. Malicious node detection in wireless sensor network using swarm intelligence optimization. 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). pp. 219–224.

Alhawas, A.a.T., Nigel 2019. Abandonment Attack on the LEACH Protocol. European Workshop on Performance Engineering. Springer. pp. 1–15.

Almomani, I.a.A.-K., Bassam and Mousa, AL 2016. Akhras. WSNDS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks.Journal of Sensors 2016. doi:10.1155/2016/4731953

Alnawafa, E.a.M., Ion 2018. New energy efficient multi-hop routing techniques for wireless sensor networks: Static and dynamic techniques. Sensors18(6) 1863. doi:10.3390/s18061863

Alsulaiman, L.a.A.-A., Saad 2021. Performance evaluation of machine learning techniques for DOS detection in wireless sensor network. arXiv preprint arXiv:2104.01963arXiv preprint arXiv:2104.01963, 

Anarase, D.S.a.o. 2021. Study and Implementation of Routing Protocols in Wireless Sensor Network for IoT Applications. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12(10) 4223–4230

Behera, T.M.a.S., Umesh Chandra and Mohapatra, Sushanta Kumar and Khan, Mohammad S and Appasani, Bhargav and Bizon, Nicu and Thounthong, Phatiphat 2022. Energy-Efficient Routing Protocols for Wireless Sensor Networks: Architectures, Strategies, and Performance.Electronics 11(15) 2282. doi:10.3390/electronics11152282

Bilgin, B.E.a.B., Selccuk 2019. A light-weight solution for blackhole attacks in wireless sensor networks. Turkish Journal of Electrical Engineering and Computer Sciences 27(4) 2557–2570. doi:10.3906/elk-1809-23

Dener, M.a.A., Samed and Orman, Abdullah 2022. STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment. IEEE Access 10 92931–92945. doi:10.1109/ACCESS.2022.3202807

Djedouboum, A.C.a.A.A., Ado Adamou and Gueroui, Abdelhak Mourad and Mohamadou, Alidou and Aliouat, Zibouda 2018. Big data collection in large-scale wireless sensor networks. Sensors 18(12) 4474. doi:10.3390/s18124474

Hachimi, M.a.K., Georges and Gagnon, Ghyslain and Illy, Poulmanogo 2020. Multi-stage jamming attacks detection using deep learning combined with kernelized support vector machine in 5g cloud radio access networks. pp. 1–5.

Hidoussi, F.a.T.-C., Homero and Boubiche, Djallel Eddine and Lakhtaria, Kamaljit and Mihovska, Albena and Voznak, Miroslav 2015. Centralized IDS based on misuse detection for cluster-based wireless sensors networks. Wireless Personal Communications 85(1) 207–224. doi:10.1007/s11277-015-2734-2

Hussain, M.a.R., Jiadong and Akram, Awais 2020. Classification of DoS Attacks in Wireless Sensor Network with Artificial Neural Network.  22(3) 540–547

Kamal, Z.-E.a.S., Mohammad Ali and others 2015. Introduction to wireless sensor networks. Wireless sensor and mobile ad-hoc networks. Springer. pp. 3–32.

Kaur, G.a.S., Mandeep 2014. Detection of black hole in wireless sensor network based on data mining. 2014 5th International Conference-Confluence The Next Generation Information Technology Summit (Confluence). pp. 457–461.

Khediri, S.E.a.N., Nejah and Wei, Anne and Kachouri, Abdennaceur 2014. A new approach for clustering in wireless sensors networks based on LEACH. Procedia Computer Science 32 1180–1185. doi:10.1016/j.procs.2014.05.551

Khorgade, S.A.a.N., D Ghuse 2015. Attacks and Preventions in Wireless Sensor Network. International Journal of Engineering Research and General Science 3 2  Part 2

Kumar, S.R.a.T., M. and Umamakeswari, A. 2017. Analysis of Sinkhole Attack in Leach Based Wireless Sensor Network International Journal of Pure and Applied Mathematics 116(24) 185-197

Liu, Z.-z.a.L., Shi-ning 2018. Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing. International Journal of Distributed Sensor Networks 14(9). doi:10.1177/1550147718802259

Maxwell, A.E., Timothy A. Warner, and Luis Andrés Guillén. 2021. Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—part 1: Literature review. Remote Sensing 13(13) 2450. doi:10.3390/rs13132450

Meyer, D.a.W., FT 2015. Support vector machines. The Interface to libsvm in package e1071 28 20

Mittal, M., Rocío Pérez de Prado, Yukiko Kawai, Shinsuke Nakajima, and José E. Muñoz-Expósito. 2021. Machine learning techniques for energy efficiency and anomaly detection in hybrid wireless sensor networks. Energies 14(11) 3125. doi:10.3390/en14113125

Naeimi, S.a.G., Hamidreza and Chow, Chee-Onn and Ishii, Hiroshi 2012. A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors 12(6) 7350–7409. doi:10.3390/s120607350

Nguyen, M.T.a.N., Cuong V and Do, Hai T and Hua, Hoang T and Tran, Thang A and Nguyen, An D and Ala, Guido and Viola, Fabio 2021. Uav-assisted data collection in wireless sensor networks: A comprehensive survey. Electronics 10(21) 2603. doi:10.3390/electronics10212603

Ouni, R.a.S., Kashif 2022. Framework for Sustainable Wireless Sensor Network Based Environmental Monitoring. Sustainability 14(14) 8356. doi:10.3390/su14148356

Pal, M. 2006. Support vector machine-based feature selection for land cover classification: a case study with DAIS hyperspectral data. International Journal of Remote Sensing 27(14) 2877–2894. doi:10.1080/01431160512331314083

Rani, B.a.S., H. 2018. Blackhole attack detection and prevention in wireless sensor networks: a study. Journal of Emerging Technologies and Innovative Research 5(3) 461-465

Rathod, V.a.M., Mrudang 2011. Security in wireless sensor network: a survey. Ganpat university journal of engineering & technology 1(1) 35–44

Ren, D.a.A., Saleema and Lee, Bongshin and Suh, Jina and Williams, Jason D 2016. Squares: Supporting interactive performance analysis for multiclass classifiers. IEEE transactions on visualization and computer graphics 23(1) 61–70. doi:10.1109/TVCG.2016.2598828

Singh, R.a.R.K., C and Sharma, Rajnish and Vig, Renu 2021. Energy efficient fixed-cluster architecture for wireless sensor networks. Journal of Intelligent & Fuzzy Systems 40(5) 8727–8740. doi:10.3233/JIFS-192177

Singh, S.K.a.K., Prabhat and Singh, Jyoti Prakash 2017. A survey on successors of LEACH protocol. IEEE Access 5 4298–4328. doi:10.1109/ACCESS.2017.2666082

Tandel, R.I. 2016. Leach protocol in wireless sensor network: a survey. International Journal of Computer Science and Information Technologies 7(4) 1894–1896

Tang, T.a.C., Shengyong and Zhao, Meng and Huang, Wei and Luo, Jake 2019. Very large-scale data classification based on K-means clustering and multi-kernel SVM. Soft Computing 23(11) 3793–3801

Vinayakumar, R.a.A., Mamoun and Soman, KP and Poornachandran, Prabaharan and Al-Nemrat, Ameer and Venkatraman, Sitalakshmi 2019. Deep learning approach for intelligent intrusion detection system. IEEE Access 7 41525–41550. doi:10.1109/ACCESS.2019.2895334

Wang, H.a.X., Jinbo and Yao, Zhiqiang and Lin, Mingwei and Ren, Jun 2017. Research survey on support vector machine. 10th EAI International Conference on Mobile Multimedia Communications. pp. 95–103.

Widhalm, D.a.G., Karl M and Kastner, Wolfgang 2021. An Open-Source Wireless Sensor Node Platform with Active Node-Level Reliability for Monitoring Applications. Sensors 21(22) 7613. doi:10.3390/s21227613

Yang, M.a.H., Jingsha and Zhang, Yuqiang 2014. Calculating the number of cluster heads based on the rate-distortion function in wireless sensor networks. The Scientific World Journal 2014. doi:10.1155/2014/602875

Yousif, Y.K.a.M., Omar H and Rashed, Zainab Abdulateef 2021. An Overview of Wireless Sensor Network (WSN) and Its Applications.  8 7

About Authors

Sura Alsharifi is an M.Sc. student in Computer Science at the University of Mosul / Iraq. She can be contacted at email: sura.20csp80@student.uomosul.edu.iq

Mafaz Alanezi is a faculty member at the Department of Computer Science, University of Mosul, Iraq. She obtained her Ph.D. degree in Computer Science in the field of Computer and Network Security from University of Mosul / Iraq in 2012. Her M.Sc. degree was also in Computer Science in the field of Image Processing from the University of Mosul/ Iraq in 2003. Her current scientific degree Prof. Dr in Cybersecurity and Information Security. She can be contacted at email: mafazmhalanezi@uomosul.edu.iq

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