• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2021, Volume: 14, Issue: 1, Pages: 76-85

Original Article

An efficient algorithmic technique for feature selection in IoT based intrusion detection system

Received Date:13 November 2021, Accepted Date:26 December 2021, Published Date:13 January 2021


Background/Objectives Internet of Things (IoT) is an emerging technology that involves in monitoring the environment and the IoT networks are most vulnerable to attacks due to various number of devices connected in the network. The Intrusion detection technique has been applied to analyze the anomaly in the network. The Existing models have the limitation of inefficiency in the intrusion detection due to the overfit in the models. Methods/Statisticalanalysis: In this research, the Flower Pollination Algorithm (FPA) has been applied in the intrusion detection method to increase the efficiency of the IoT network. The FPA method has the advantage of long distance pollination and flower consistency to analyze the features effectively. The FPA selects the features in the IoT network and apply the features for the classifier to detect the attacks. The classifiers such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Artificial Neural Network (ANN) are used to detect the intrusions in the network. Findings: This experimental result shows that the proposed FPA method with ANN has the accuracy of 99.5 % in detection and existing ANN has 99.4 % accuracy in detection. Novelty/Applications: The FPA method has the advantages of long distance pollination and flower consistency which helps to analyze the network features effectively.

Keywords: Artificial neural network; flower pollination algorithm; internet of things; intrusion detection; long distance pollination


  1. Mukherjee A, Deb P, De D, Buyya R. IoT-F2N: An energy-efficient architectural model for IoT using Femtolet-based fog network. The Journal of Supercomputing. 2019;75(11):7125–7146. Available from: https://dx.doi.org/10.1007/s11227-019-02928-0
  2. Chowdhury A, Raut S. Scheduling Correlated IoT Application Requests Within IoT Eco-System: An Incremental Cloud Oriented Approach. Wireless Personal Communications. 2019;108:1275–1310. Available from: https://dx.doi.org/10.1007/s11277-019-06469-w
  3. Yu J, Bang HC, Lee H, Lee YS. Adaptive Internet of Things and Web of Things convergence platform for Internet of reality services. The Journal of Supercomputing. 2016;72(1):84–102. Available from: https://doi.org/10.1007/s11227-015-1489-6
  4. Mukherjee B, Wang S, Lu W, Neupane RL, Dunn D, Ren Y, et al. Flexible IoT security middleware for end-to-end cloud–fog communication. Future Generation Computer Systems. 2018;87:688–703. Available from: https://dx.doi.org/10.1016/j.future.2017.12.031
  5. Casola V, Benedictis AD, Riccio A, Rivera D, Mallouli W, Oca EMd. A security monitoring system for internet of things. Internet of Things. 2019;7. Available from: https://dx.doi.org/10.1016/j.iot.2019.100080
  6. Elrawy MF, Awad AI, Hamed HFA. Intrusion detection systems for IoT-based smart environments: a survey. Journal of Cloud Computing. 2018;7(1). Available from: https://dx.doi.org/10.1186/s13677-018-0123-6
  7. Dovom EM, Azmoodeh A, Dehghantanha A, Newton DE, Parizi RM, Karimipour H. Fuzzy pattern tree for edge malware detection and categorization in IoT. Journal of Systems Architecture. 2019;97:1–7. Available from: https://dx.doi.org/10.1016/j.sysarc.2019.01.017
  8. Rathore S, Kwon BW, Park JH. BlockSecIoTNet: Blockchain-based decentralized security architecture for IoT network. Journal of Network and Computer Applications. 2019;143:167–177. Available from: https://dx.doi.org/10.1016/j.jnca.2019.06.019
  9. Deng L, Li D, Yao X, Cox D, Wang H. Mobile network intrusion detection for IoT system based on transfer learning algorithm. Cluster Computing. 2019;22(S4):9889–9904. Available from: https://dx.doi.org/10.1007/s10586-018-1847-2
  10. Stylianopoulos C, Johansson L, Olsson O, Almgren M. CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs. Nordic Conference on Secure IT Systems. 2018;p. 187–202. Available from: https://doi.org/10.1007/978-3-030-03638-6_12
  11. Hasan M, Islam MM, Zarif MII, Hashem MMA. Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches. Internet of Things. 2019;7. Available from: https://dx.doi.org/10.1016/j.iot.2019.100059
  12. Li W, Tug S, Meng W, Wang Y. Designing collaborative blockchained signature-based intrusion detection in IoT environments. Future Generation Computer Systems. 2019;96:481–489. Available from: https://dx.doi.org/10.1016/j.future.2019.02.064
  13. Pan Z, Hariri S, Pacheco J. Context aware intrusion detection for building automation systems. Computers & Security. 2019;85:181–201. Available from: https://dx.doi.org/10.1016/j.cose.2019.04.011
  14. Yahalom R, Steren A, Nameri Y, Roytman M, Porgador A, Elovici Y. Improving the effectiveness of intrusion detection systems for hierarchical data. Knowledge-Based Systems. 2019;168:59–69. Available from: https://dx.doi.org/10.1016/j.knosys.2019.01.002
  15. Diro AA, Chilamkurti N. Distributed attack detection scheme using deep learning approach for Internet of Things. Future Generation Computer Systems. 2018;82:761–768. Available from: https://dx.doi.org/10.1016/j.future.2017.08.043
  16. Liang C, Shanmugam B, Azam S, Karim A, Islam A, Zamani M, et al. Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems. Electronics. 2020;9(7):1120. Available from: https://dx.doi.org/10.3390/electronics9071120
  17. Pahl MO, Aubet FX. DS2OS traffic traces. 2019. Available from: https://www.kaggle.com/francoisxa/ds2ostraffictraces
  18. Zhang P, Liu F, Aujla GS, Vashisht S. VNE strategy based on chaos hybrid flower pollination algorithm considering multi-criteria decision making. Neural Computing and Applications. 2020;4:1–2. Available from: https://dx.doi.org/10.1007/s00521-020-04827-5
  19. Karim A, Azam S, Shanmugam B, Kannoorpatti K, Alazab M. A Comprehensive Survey for Intelligent Spam Email Detection. IEEE Access. 2019;7:168261–168295. Available from: https://dx.doi.org/10.1109/access.2019.2954791


© 2021 Pani et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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