International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 17, No. 2, 8 Apr. 2025
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An Efficient IoT Based Intrusion Detection System Using Optimization Kernel Extreme Learning Machine
PDF (1389KB), PP.72-87
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Author(s)
Laiby Thomas
1,*
Anoop B. K.
2
Index Terms
Internet of Things, Machine Learning, Intrusion Detection System, Chaotic Improved Black Widow Optimization, Kernel Extreme Learning Machine
Abstract
The Internet of Things (IoT) is an ever-expanding network that links all objects to the web so that they can communicate with one another using standardized protocols. Recently, IoT networks have been extensively used in advanced applications like smart factories, smart homes, smart grids, smart cities, etc. They can be used in conjunction with artificial intelligence (AI) and machine learning to facilitate a data collection procedure that is both simplified and more dynamic. Along with the services provided by IoT applications, various security issues are also raised. The accessing of IoT devices is mainly through an untrusted network like the Internet which makes them unprotected against a wide range of malicious attacks. The detection performance of current IDSs is hindered by issues including false alarms, low detection rate, an unbalanced dataset, and slow response time. This study proposes a new intrusion detection system (IDS) for the IoT that utilizes the chaotic improved Black Widow Optimization Kernel Extreme Learning Machine (CIBWO-KELM) algorithm to address these problems. Initially, the pre-processing of the dataset is carried out using min-max normalization, changing string values to numerical values and changing IP address to numerical values. The selection of the highest performing feature set is achieved through the information gain method (IGM), and finally, the intrusion detection is performed by the CIBWO-KELM algorithm. Python is the tool utilized for testing, while the BoT-IoT dataset is used for simulation analysis. The suggested model achieves an accuracy level of 99.7% when applied to the BoT-IoT dataset. In addition, the results of the studies demonstrate that the proposed model outperforms other current techniques.
Cite This Paper
Laiby Thomas, Anoop B. K., "An Efficient IoT Based Intrusion Detection System Using Optimization Kernel Extreme Learning Machine", International Journal of Computer Network and Information Security(IJCNIS), Vol.17, No.2, pp.72-87, 2025. DOI:10.5815/ijcnis.2025.02.05
Reference
[1]M.A. Umer, K.N. Junejo, M.T. Jilani and A.P. Mathur, "Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations." International Journal of Critical Infrastructure Protection vol. 38, pp. 100516, 2022.
[2]A. Chowdhury, G. Karmakar, J. Kamruzzaman, “The co-evolution of cloud and IoT applications: Recent and future trends,” In Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization. IGI Global pp. 213-234, 2019.
[3]A. Raghuvanshi, U.K. Singh, G.S. Sajja, H. Pallathadka, E. Asenso, M. Kamal, A. Singh and K. Phasinam, "Intrusion detection using machine learning for risk mitigation in IoT-enabled smart irrigation in smart farming." Journal of Food Quality vol. 2022, pp. 1-8, 2022.
[4]A. Khraisat, I. Gondal, P. Vamplew, J. Kamruzzaman, “Survey of intrusion detection systems: techniques, datasets and challenges,” Cybersecurity. vol. 2, no.1, pp. 1-22, 2019.
[5]K. Albulayhi, Q.A. Al-Haija, S.A. Alsuhibany, A.A. Jillepalli, M. Ashrafuzzaman, and F.T. Sheldon, "IoT intrusion detection using machine learning with a novel high performing feature selection method." Applied Sciences vol. 12, no. 10, pp. 5015, 2022.
[6]B.B. Zarpelão, R.S. Miani, C.T. Kawakani, S.C. de Alvarenga, “A survey of intrusion detection in Internet of Things,” Journal of Network and Computer Applications. vol. 84, pp. 25-37, 2017.
[7]I.D. Mienye, Y. Sun, Z. Wang, “Prediction performance of improved decision tree-based algorithms: a review,” Procedia Manufacturing. vol. 35, pp. 698-703, 2019.
[8]Y. Lu, L. Da Xu, “Internet of Things (IoT) cybersecurity research: A review of current research topics,” IEEE Internet of Things Journal. vol. 6, no. 2, pp. 2103-2115, 2018.
[9]N. Moustafa, B. Turnbull, K.K.R. Choo, “An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of Internet of things,” IEEE Internet of Things Journal. vol. 6, no. 3, pp. 4815-4830, 2018.
[10]M. Asif, S. Abbas, M.A. Khan, A. Fatima, M.A. Khan, and S.-W. Lee, "MapReduce based intelligent model for intrusion detection using machine learning technique." Journal of King Saud University-Computer and Information Sciences 2021.
[11]W.L. Al-Yaseen, Z.A. Othman, M.Z.A. Nazri, “Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system,” Expert Systems with Applications. vol. 67, pp. 296-303, 2017.
[12]K.-A. Tait, J.S. Khan, F. Alqahtani, A.A. Shah, F.A. Khan, M. Ur Rehman, W. Boulila, and J. Ahmad, "Intrusion detection using machine learning techniques: an experimental comparison." In 2021 International Congress of Advanced Technology and Engineering (ICOTEN), IEEE, pp. 1-10, 2021.
[13]G.S. Sajja, M. Mustafa, R. Ponnusamy and S. Abdufattokhov, "Machine learning algorithms in intrusion detection and classification." Annals of the Romanian Society for Cell Biology vol. 25, no. 6, pp. 12211-12219, 2021.
[14]H. Bostani, M. Sheikhan, “Hybrid of anomaly-based and specification-based IDS for Internet of Things using unsupervised OPF based on MapReduce approach,” Computer Communications. vol. 98, pp. 52-71, 2017.
[15]L. Santos, C. Rabadao, R. Gonçalves, “Intrusion detection systems in Internet of Things: A literature review,” In 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1-7, 2018. IEEE.
[16]N. Islam, F. Farhin, I. Sultana, M.S. Kaiser, M.S. Rahman, M. Mahmud, A.S.M. SanwarHosen, and G.H. Cho, "Towards Machine Learning Based Intrusion Detection in IoT Networks." Computers, Materials & Continua vol. 69, no. 2, 2021.
[17]C. Gomez, A. Arcia-Moret, J. Crowcroft, “TCP in the Internet of Things: from ostracism to prominence,” IEEE Internet Computing. vol. 22, no. 1, pp. 29-41, 2018.
[18]D. Midi, A. Rullo, A. Mudgerikar, E. Bertino, “Kalis—A system for knowledge-driven adaptable intrusion detection for the Internet of Things,” In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). pp. 656-666, 2017. IEEE.
[19]Q. Shafi, A. Basit, S. Qaisar, A. Koay, I. Welch, “Fog-assisted SDN controlled framework for enduring anomaly detection in an IoT network,” IEEE Access, vol. 6, pp. 73713-73723, 2018.
[20]S. Prabavathy, K. Sundarakantham, S.M. Shalinie, “Design of cognitive fog computing for intrusion detection in Internet of Things,” Journal of Communications and Network. vol. 20, no. 3, pp. 291-298, 2018.
[21]S. Deshmukh-Bhosale, S.S. Sonavane, “A real-time intrusion detection system for wormhole attack in the RPL based Internet of Things,” Procedia Manufacturing. vol. 32, pp. 840-847, 2019.
[22]R.K. Deka, D.K. Bhattacharyya, J.K. Kalita, “Active learning to detect DDoS attack using ranked features,” Computer Communications. vol. 145, pp. 203-222, 2019.
[23]S. Venkatraman, B. Surendiran, “Adaptive hybrid intrusion detection system for crowd sourced multimedia Internet of things systems,” Multimedia Tools and Applications. vol. 79, no. 5, pp. 3993-4010, 2020.
[24]R. Patil, C. Modi, “Designing a Virtual Environment Monitoring System to Prevent Intrusions in Future Internet of Things,” In Recent Findings in Intelligent Computing Techniques. pp. 345-351, 2019. Springer, Singapore.
[25]A.A. Diro, N. Chilamkurti, “Distributed attack detection scheme using deep learning approach for Internet of Things,” Future Generation Computer Systems, vol. 82, pp. 761-768, 2018.
[26]H.A.B. Salameh, S. Almajali, M. Ayyash, H. Elgala, “Spectrum assignment in cognitive radio networks for internet-of-things delay-sensitive applications under jamming attacks,” IEEE Internet of Things Journal. vol. 5, no. 3, pp. 1904-1913, 2018.
[27]N. Tariq, M. Asim, Z. Maamar, M.Z. Farooqi, N. Faci, T. Baker, “A Mobile Code-driven Trust Mechanism for detecting internal attacks in sensor node-powered IoT,” Journal of Parallel and Distributed Computing. vol. 134, pp. 198-206, 2019.
[28]M. Mahmudul Hasan, M. Islam, M. I. Islam Zarif, M. M. A. Hashem, “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches,” Internet of Things. vol. 7, pp. 1-16, 2019.
[29]C. Lyu, X. Zhang, Z. Liu, C.H. Chi, “Selective authentication based geographic opportunistic routing in wireless sensor networks for Internet of Things against DoS attacks,” IEEE Access, vol. 7, pp. 31068-31082, 2019.
[30]O. Brun, Y. Yin, E. Gelenbe, Y.M. Kadioglu, Augusto-Gonzalez, J. and Ramos, M., “Deep learning with dense random neural networks for detecting attacks against IoT-connected home environments,” In International ISCIS Security Workshop, pp. 79-89, 2018. Springer, Cham.
[31]M. Ahmad, Q. Riaz, M. Zeeshan, H. Tahir, S.A. Haider, and M.S. Khan, "Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using UNSW-NB15 data-set." EURASIP Journal on Wireless Communications and Networking vol. 2021, no. 1, pp. 1-23, 2021.
[32]G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, "Extreme learning machine for regression and multiclass classification." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) vol. 42, no. 2, pp. 513-529, 2011.