1
Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, India
(chandanan.amit@ggu.ac.in)
Abstract :
Among the many uses for WSN, which is an ad hoc wireless system, are conveyance, calamity administration, industrialized observing, health observing, and so on. Intrusion Detection System (IDS) is a top-tier network security measure. In order to prevent cross-layer attacks, IDS detection rates must be high. Using a technique known as the "Rule of Thumb" or ELM (Extreme Learning Machine) algorithm, WSN is able to predict the future with a great grade of accurateness. The projected RELM provides a comprehensive overview of both the attacks and the rules for detecting them. The rules can identify threats at the different layers. If the rule-founded IDS were deployed at the sensor nodes, less data would need to be transmitted over the network, saving power. Relative to the SVM (Support Vector Machine) and BPN (Back Propagation Neural Network) on the NSL-KDD dataset, RELM evaluates ELM's detection rate. Because of its superior detection rate, ELM has been used as the foundation of the IDS deployed at the BS to protect it against intrusion. If the criteria were combined with the ELM algorithm, the resulting system would have a higher detection rate than any currently available alternative.
Keywords :
Secure WSN; Extreme Learning Machine; Intrusion Detection System; RELM; Support Vector Machine.
References :
[1]. Xu, S & Wang, J 2016, „A fast incremental extreme learning machine algorithm for data streams classification‟, Expert Systems with Applications, vol. 65, pp. 332- 344.
[2]. Xun, Y, Athman, B, Dimitrios, G, Andy, S & Jan, W 2015, „Privacy Protection for Medical Sensor Data‟, IEEE Transaction on Dependable and Secure Computing, vol. 13, no. 3, pp. 369–380.
[3]. Yi, X, Bouguettaya, A, Georgakopoulos, D, Song, A & Willemson, J 2015, „Privacy Protection for Medical Sensor Data‟, IEEE Transaction on Dependable and Secure Computing, vol. 13, no. 3, pp. 369–380.
[4]. Wang, SS, Yan, KQ, Wang, SC & Liu, CW 2011, „An Integrated Intrusion Detection System for Cluster-based Wireless Sensor Networks‟, Expert Systems with Applications, vol. 38, no. 12, pp. 15234-15243.
[5]. Wang, C, Feng, T, Kim, J, Guiling, W & Wensheng, Z 2012, „Catching Packet Droppers and Modifiers in Wireless Sensor Networks‟, IEEE Transaction on Parallel and Distributed Systems, vol. 23, no. 5, pp. 835- 843.
[6]. Roshan, S, Miche, Y, Akusok, A & Lendasse, A 2018, „Adaptive and online network intrusion detection system using clustering and Extreme Learning Machines‟, Journal of the Franklin Institute, vol. 355, no. 4, pp. 1752-1779.
[7]. Roy, S, Conti, M, Setia, S & Jajodia, S 2014, „Secure Data Aggregation in Wireless Sensor Networks‟, IEEE Transactions on Information Forensics and Security, vol. 9, no. 4, pp. 681- 694.
[8]. Khalid A. Darabkh, Mohammad Z. El-Yabroudi, and Ali H. El-Mousa, "BPA-CRP: A Balanced Power-Aware Clustering and Routing Protocol for Wireless Sensor Networks," Ad Hoc Networks, vol. 82, pp. 155-171, 2019.
[9]. Taochun Wang, Xiaolin Qin, Youwei Ding, Liang Liu, and Yonglong Luo, "Privacy-Preserving and Energy-Efficient Continuous Data Aggregation Algorithm in Wireless Sensor Networks," Wireless Personal Communications, vol. 98, no. 1 pp. 665-684, 2018.
[10] B. Murugeshwari, K. Sarukesi and C. Jayakumar, “An Efficient Method for Knowledge Hiding Through Database Extension,” Test Conference International, pp. 342-344, 2010. Crossref, https://doi.org/10.1109/ITC.2010.93.
[11] A, Jenice and D, Hevin, “An Energy Efficient Secure Data Aggregation in Wireless Sensor Networks,” Research Square, 2021. Crossref, https://doi.org/10.21203/rs.3.rs-364741/v1.
[12] Shraddha Deshmukh, A. R. Bhagat Patil and Harshad Nakade, “Implementation of Effective Key Management Strategy with Secure Data Aggregation in Dynamic Wireless Sensor Network,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 4, no. 2, pp. 358-364, 2018.
[13] Dou H, Chen Y and Yang Y, “A Secure and Efficient Privacy-Preserving Data Aggregation Algorithm,” Journal of Ambient Intelligence and Humanized Computing, vol. 13, pp. 1495–1503, 2022. Crossref, https://doi.org/10.1007/s12652-020-02801-6.
[14] Xiaohan Qi, Xiaowu Liu, Jiguo Yu and Qiang Zhang, “A Privacy Data Aggregation Scheme for Wireless Sensor Networks,” Procedia Computer Science, vol. 174, pp. 578-583, 2020. Crossref, https://doi.org/10.1016/j.procs.2020.06.127.
[15]. Jeyalakshmi. C, Balasubramaniam, Murugeshwari and Karthick, M, “HMM and K-NN based Automatic Musical Instrument Recognition,” IEEE, pp. 350-355, 2018. Crossref, https://doi.org/10.1109/I-SMAC.2018.8653725.
[16] Khalid A. Darabkh, Saja M. Odetallah, Zouhair Al-qudah, Khalifeh Ala’F, and Mohammad M. Shurman, "Energy-Aware and Density Based Clustering and Relaying Protocol (EA-DB-CRP) for Gathering Data in Wireless Sensor Networks," Applied Soft Computing, vol. 80, pp. 154-166, 2019. Crossref, https://doi.org/10.1016/j.asoc.2019.03.025.
[17] Mohamed Elshrkawey, Samiha M. Elsherif, and M. Elsayed Wahed, "An Enhancement Approach for Reducing The Energy Consumption In Wireless Sensor Networks," Journal of King Saud University-Computer and Information Sciences, vol. 30, no. 2, pp. 259-267, 2018. Crossref, https://doi.org/10.1016/j.jksuci.2017.04.002.
[18] Bala subramaniam Murugeshwari, Daniel Raphael and Raghavan Singaravelu, “Metamaterial Inspired Structure with Offset-Fed Microstrip Line for Multi-Band Operations,” Progress in Electromagnetics Research, vol. 82, pp. 95-105, 2019.
[19] Vinod Kumar and Om Prakash Roy, "A Reliable and Secure Inter-and Intra-State Routing Protocol for VoIP communication," International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 479-490, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P250.
[20] Tianshu Wang, Gongxuan Zhang, Xichen Yang, and Ahmadreza Vajdi, "Genetic Algorithm for Energy- Efficient Clustering and Routing in Wireless Sensor Networks," Journal of Systems and Software, vol. 146, pp. 196-214, 2018. Crossref, https://doi.org/10.1016/j.jss.2018.09.067.
[21]. Piyush Kumar Shukla, Vandana Roy, Prashant Kumar Shukla, Anoop Kumar Chaturvedi, Aumreesh Kumar Saxena, Manish Maheshwari, Parashu Ram Pal, An Advanced EEG Motion Artifacts Eradication Algorithm, The Computer Journal, 2021;, bxab170, https://doi.org/10.1093/comjnl/bxab170.
[22] D. Dhinakaran, D. A. Kumar, S. Dinesh, D. Selvaraj and K. Srikanth, “Recommendation System for Research Studies Based on GCR,” 2022 International Mobile and Embedded Technology Conference (MECON), Noida, India, pp. 61-65, 2022. Crossref, https://doi.org/10.1109/MECON53876.2022.9751920.
[23]. K.Sudharson, Ahmed Mudassar Ali and N.Partheeban, "NUITECH – Natural user Interface Technique Foremulating Computer Hardware," International Journal of Pharmacy & Technology, vol. 8, no. 4, pp. 23598-23606, 2016.
[24] S. Arun and K. Sudharson. "DEFECT: discover and eradicate fool around node in Emergency Network using Combinatorial Techniques," Journal of Ambient Intelligence and Humanized Computing, pp. 1-12, 2020. Crossref, https://doi.org/10.1007/s12652- 020-02606-7.
[25] J. A. Shanny and K. Sudharson, "User Preferred Data Enquiry System using Mobile Communications," International Conference on Information Communication and Embedded Systems (ICICES2014), pp. 1-5, 2014. Crossref, https://doi.org/10.1109/ICICES.2014.7033943.
[26]. K.Priyadharshini, "A Review on Wireless Sensor Networks-Security Issues and Disputes," International Journal of P2P Network Trends and Technology, vol. 9, no. 2, pp. 6-9, 2019.
[27] Korada Kishore Kumar and Konni Srinivasa Rao, "An Efficient users Authentication and Secure Data Transmission of Cluster-based Wireless Sensor Network," SSRG International Journal of Computer Science and Engineering, vol. 5, no. 1, pp. 1-5, 2018. Crossref, https://doi.org/10.14445/23488387/IJCSE-V5I1P101.
[28] Khalid A. Darabkh, Noor J. Al-Maaitah, Iyad F. Jafar and Ala’F Khalifeh, "EA-CRP: A Novel Energy-Aware Clustering and Routing Protocol in Wireless Sensor Networks," Computers & Electrical Engineering, vol. 72, pp. 702-718, 2018. Crossref, https://doi.org/Crossref, https://doi.org/10.1016/j.compeleceng.2017.11.017.
| Style | # |
|---|---|
| MLA | Amit Kumar Chandanan. "An Encrypted Rules and Extreme Learning Machine Approach for Enhancement of Data Security." Journal of Cybersecurity and Information Management, Vol. 11, No. 2, 2023 ,PP. 47-56 (Doi : https://doi.org/10.54216/JCIM.110205) |
| APA | Amit Kumar Chandanan. (2023). An Encrypted Rules and Extreme Learning Machine Approach for Enhancement of Data Security. Journal of Journal of Cybersecurity and Information Management, 11 ( 2 ), 47-56 (Doi : https://doi.org/10.54216/JCIM.110205) |
| Chicago | Amit Kumar Chandanan. "An Encrypted Rules and Extreme Learning Machine Approach for Enhancement of Data Security." Journal of Journal of Cybersecurity and Information Management, 11 no. 2 (2023): 47-56 (Doi : https://doi.org/10.54216/JCIM.110205) |
| Harvard | Amit Kumar Chandanan. (2023). An Encrypted Rules and Extreme Learning Machine Approach for Enhancement of Data Security. Journal of Journal of Cybersecurity and Information Management, 11 ( 2 ), 47-56 (Doi : https://doi.org/10.54216/JCIM.110205) |
| Vancouver | Amit Kumar Chandanan. An Encrypted Rules and Extreme Learning Machine Approach for Enhancement of Data Security. Journal of Journal of Cybersecurity and Information Management, (2023); 11 ( 2 ): 47-56 (Doi : https://doi.org/10.54216/JCIM.110205) |
| IEEE | Amit Kumar Chandanan, An Encrypted Rules and Extreme Learning Machine Approach for Enhancement of Data Security, Journal of Journal of Cybersecurity and Information Management, Vol. 11 , No. 2 , (2023) : 47-56 (Doi : https://doi.org/10.54216/JCIM.110205) |