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A new design of intrusion detection in IoT sector using optimal feature selection and high ranking-based ensemble learning model

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Abstract

Internet of Things (IoT) is a significant area in the digital era for the purpose of data collection and transferring them on the network without the help of a human, which shows that the whole world is linked over a single system. It can also be seen as the powerful force that operates modern health systems, home automation, improved manufacturing, and smart cities. IoT increases the chances of cyber threats due to the extensive usage of IoT devices and services. Thus, there is a required to design a robust Intrusion Detection System (IDS) to obtain better network security. The conventional machine learning methods are not optimum for processing complex network data by various intrusion methods. Subsequently, the conventional deep learning approaches in intrusion detection show their efficiency in only one-dimensional feature data, and also they are insufficient for predicting the unknown intrusions. This paper focuses on proposing a novel High Ranking-based Optimized Ensemble Learning Model (HR-OELM) using three different classifiers for developing an intelligent IDS. The first phase is data collection, in which the benchmark datasets are gathered. As the features or attributes associated with the IoT devices from benchmark source datasets are more, it is required to extract the most relevant data that could be highly efficient for attaining the high detection rate. Thus, the accurate feature selection is developed to construct a powerful classification methgods and to decreases the data dimensionality. The major highlight of the optimal feature selection is to decreases the correlation between the features giving unique information. These features are subjected to the proposed HR-OELM, in which the Deep Neural Network (DNN), Random Forest, and Adaboost classifiers are used. The detection performance is finalized based on the high ranking of output from three classifiers. One of the main contributions of the proposed IDS is the development of Adaptive Frequency-based Electric Fish Optimization (AF-EFO) for the optimal feature selection and variable optimization of HR-OELM, thus ensuring superior performance. Finally, the suggested ensemble learning model holds a minimum false-positive range and a maximum detection range than the other conventional traditional methods.

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References

  1. Aloqaily M, Otoum S, Al Ridhawi I, Jararweh Y (2019) An intrusion detection system for connected vehicles in smart cities. Ad Hoc Netw

    Google Scholar 

  2. Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M (2015) Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor 2347–2376

    Article  Google Scholar 

  3. Mosenia A, Jha NK (2017) A comprehensive study of security of internet-of-things. IEEE Trans Emerg Top Comput 5(4):586–602

    Article  Google Scholar 

  4. Adat V, Gupta BB (2018) Security in Internet of Things: issues, challenges, taxonomy, and architecture. Telecommun Syst 423–441

    Article  Google Scholar 

  5. Diro AA, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for Internet of Things. Futur Gener Comput Syst 82:761–768

    Article  Google Scholar 

  6. HaddadPajouh H, Dehghantanha A, Khayami R, Choo K (2018) A deep Recurrent Neural Network based approach for Internet of Things malware threat hunting. Future Gener Comput Syst 85:88–96

    Article  Google Scholar 

  7. RandhirKumar PK, Tripathi R, Gupta GP, Garg S, Hassan MM (2022) A distributed intrusion detection system to detect DDoS attacks in blockchain-enabled IoT network. J Parallel Distrib Comput 164:55–68

    Article  Google Scholar 

  8. Raghuvanshi A et al (2022) Intrusion detection using machine learning for risk mitigation in IoT-enabled smart irrigation in smart farming. J Food Qual

    Google Scholar 

  9. Geetha K, Brahmananda SH (2022) Network traffic analysis through deep learning for detection of an army of bots in health IoT network. Int J Pervasive Comput Commun

    Google Scholar 

  10. Balamurugan E, Mehbodniya A, Kariri E, Yadav K, Kumar A, AnulHaq M (2022) Network optimization using defender system in cloud computing security based intrusion detection system withgame theory deep neural network (IDSGT-DNN). Pattern Recognit Lett

    Google Scholar 

  11. Ziegeldorf JH, Morchon OG, Wehrle K (2014) Privacy in the Internet of Things: Threats and challenges. Secur Commun Netw 7(12):2728–2742

    Article  Google Scholar 

  12. Eskandari M, Janjua ZH, Vecchio M, Antonelli F (2020) Passban IDS: An intelligent anomaly-based intrusion detection system for IoT edge devices. IEEE Internet Things J 7(8):6882–6897

    Article  Google Scholar 

  13. Atzori L, Iera A, Morabito G (2010) The Internet of Things: A survey. Comput Netw 54(1):27872805

    MATH  Google Scholar 

  14. Rahman MA, Asyhari AT, Wen OW et al (2021) Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection. Multimed Tools Appl

    Google Scholar 

  15. Mavromatis A, Colman-Meixner C, Silva AP, Vasilakos X, Nejabati R, Simeonidou D (2020) A software-defined IoT device management framework for edge and cloud computing. IEEE Internet Things J 7(3):1718–1735

    Article  Google Scholar 

  16. Elrawy M, Awad A, Hamed H (2018) Intrusion detection systems for IoT-based smart environments: a survey. J Cloud Comput. 7(21)

    Google Scholar 

  17. Zarpelao BB, Miani RS, Kawakani CT, Alvarenga SCD (2017) A survey of intrusion detection in internet of things. J Netw Comput Appl 84:25–37

    Article  Google Scholar 

  18. Davahli A, Shamsi M, Abaei G (2020) Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks. J Ambient Intell Humaniz Comput 11:5581–5609

    Article  Google Scholar 

  19. Hosseinpour F, Amoli PV, Plosila J, Hamalainen T (2016) An intrusion detection system for fog computing and IoT based logistic systems using a smart data approach. Int J Digit Content Technol Appl 10(5):34–46

    Google Scholar 

  20. Keserwani PK, Govil MC, Pilli ES et al (2021) A smart anomaly-based intrusion detection system for the Internet of Things (IoT) network using GWO–PSO–RF model. J Reliab Intell Environ 7:3–21

    Article  Google Scholar 

  21. Gothawal DB, Nagaraj SV (2020) Anomaly-based intrusion detection system in RPL by applying stochastic and evolutionary game models over IoT environment. Wirel Pers Commun 110:1323–1344

    Article  Google Scholar 

  22. Li D, Cai Z, Deng L et al (2019) Information security model of block chain based on intrusion sensing in the IoT environment. Clust Comput 22:451–468

    Article  Google Scholar 

  23. Verma A, Ranga V (2019) Machine learning based intrusion detection systems for IoT applications. Wirel Pers Commun

    Google Scholar 

  24. Mandal K, Rajkumar M, Ezhumalai P, Jayakumar D, Yuvarani R (2020) Improved security using machine learning for IoT intrusion detection system. Mater Today Proc

    Google Scholar 

  25. Otoum Y, Liu D, Nayak A (2019) DL-IDS: a deep learning–based intrusion detection framework for securing IoT. Trans Emerg Telecommun Technol

    Google Scholar 

  26. Li W, Meng W, Au MH (2020) Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments. J Netw Comput Appl 161

    Google Scholar 

  27. Almiani M, AbuGhazleh A, Al-Rahayfeh A, Atiewi A, Razaque A (2020) Deep recurrent neural network for IoT intrusion detection system. Simul Model Pract Theor 101

    Google Scholar 

  28. Li Y, Xu Y, Liu Z, Hou H, Zheng Y, Xin Y, Zhao Y, Cui L (2020) Robust detection for network intrusion of industrial IoT based on multi-CNN fusion. Measurement 154

    Google Scholar 

  29. Yahyaoui A, Abdellatif T, Yangui S, Attia R (2021) READ-IoT: Reliable event and anomaly detection framework for the Internet of Things. IEEE Access 9:24168–24186

    Article  Google Scholar 

  30. Mishra N, Pandya S (2021) Internet of Things applications, security challenges, attacks, intrusion detection, and future visions: A systematic review. IEEE Access 9:59353–59377

    Article  Google Scholar 

  31. Moustafa N, Turnbull B, Choo KR (2019) An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of Things. IEEE Internet Things J 6(3):4815–4830

    Article  Google Scholar 

  32. Attota DC, Mothukuri V, Parizi RM, Pouriyeh S (2021) An ensemble multi-view federated learning intrusion detection for IoT. IEEE Access 9:117734–117745

    Article  Google Scholar 

  33. Siddiqui AJ, Boukerche A (2021) TempoCode-IoT: temporal codebook-based encoding of flow features for intrusion detection in Internet of Things. Cluster Comput 24:17–35

    Article  Google Scholar 

  34. Lee JD, Cha HS, Rathore S, Park JH (2021) M-IDM: A multi-classication based intrusion detection model in healthcare IoT. Comput Mater Continua 67(2)

    Google Scholar 

  35. Yilmaz S, Sen S (2020) Electric fish optimization: a new heuristic algorithm inspired by electrolocation. Neural Comput Appl 32:11543–11578

    Article  Google Scholar 

  36. Ali MH et al (2022) Threat analysis and Distributed Denial of Service (DDoS) attack recognition in the Internet of Things (IoT). Electroinics 11(3):494

    Google Scholar 

  37. Naseer S et al (2018) Enhanced network anomaly detection based on deep neural networks. IEEE Access 6:48231–48246

    Article  Google Scholar 

  38. Lee S, Chen T, Yu L, Lai C (2018) Image classification based on the boost convolutional neural network. IEEE Access 6:12755–12768

    Article  Google Scholar 

  39. Tesfahun A, Bhaskari DL (2013) Intrusion detection using random forests classifier with SMOTE and feature reduction. Int Conf Cloud Ubiquitous Comput Emerg Technol

    Google Scholar 

  40. Liu J, Yang D, Lian M, Li M (2021) Research on intrusion detection based on particle swarm optimization in IoT. IEEE Access 9:38254–38268

    Article  Google Scholar 

  41. Guo MW, Wang JS, Zhu LF, Guo SS, Xie W (2020) An improved grey wolf optimizer based on tracking and seeking modes to solve function optimization problems. IEEE Access 8:69861–69893

    Article  Google Scholar 

  42. Vijayanand R, Devaraj D (2020) A novel feature selection method using whale optimization algorithm and genetic operators for intrusion detection system in wireless mesh network. IEEE Access 8:56847–56854

    Article  Google Scholar 

  43. Gao X, Shan C, Hu C, Niu Z, Liu Z (2019) An adaptive ensemble machine learning model for intrusion detection. IEEE Access 7:82512–82521

    Article  Google Scholar 

  44. Tao P, Sun Z, Sun Z (2018) An improved intrusion detection algorithm based on GA and SVM. IEEE Access 6:13624–13631

    Article  Google Scholar 

  45. Haghighat MH, Li J (2021) Intrusion detection system using voting-based neural network. Tsinghua Sci Technol 26(4):484–495

    Article  Google Scholar 

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Authors and Affiliations

  1. Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, India

    B. Gopalakrishnan & P. Purusothaman

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  1. B. Gopalakrishnan
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  2. P. Purusothaman
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Correspondence to B. Gopalakrishnan.

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Gopalakrishnan, B., Purusothaman, P. A new design of intrusion detection in IoT sector using optimal feature selection and high ranking-based ensemble learning model. Peer-to-Peer Netw. Appl. 15, 2199–2226 (2022). https://doi.org/10.1007/s12083-022-01336-1

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