LEVERAGING EFFICIENT ALGORITHMIC TECHNIQUES IN EFFICIENT FEATURE SELECTION IN IOT BASED IDS

Prachi Juneja

Vol. 6, Issue 1, Jan-Dec 2020

Page Number: 73 - 82

Abstract:

IoT is an arising innovation that includes checking the climate, and the IoT networks are generally powerless against assaults because of the number of devices incorporated with the organization. The Intrusion detection method has been applied to investigate the abnormality in the organization. The Existing models have the limit of failure in the interruption discovery because of the models' overfitting. In this analysis, the Flower Pollination Algorithm (FPA) has been applied in the interruption identification technique to expand the IoT organization's productivity. The FPA technique has the benefit of significant distance fertilization and blossom consistency to investigate the highlights viably. The FPA chooses the IoT network includes and applies the highlights for the classifier to identify the charges. The classifiers like Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Artificial Neural Network (ANN) are utilized to distinguish the interruptions in the organization. This trial result shows that the proposed FPA strategy with ANN has an accuracy of 99.5 % in the location, and the current ANN has 99.4 % precision in recognition. The FPA technique has the upsides of significant distance fertilization and blossom consistency, which adequately investigations the company.

References

• Mukherjee A, Deb P, De D, Buyya R. IoT-F2N: An energy-efficient architectural model for IoT using Femtoletbased fog network. The Journal of Supercomputing. 2019;75(11):7125–7146. Available from: https://dx.doi.org/10.1007/s11227-019-02928-0.
• 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.
• 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.
• Mukherjee B, Wang S, Lu W, Neupane RL, Dunn D, Ren Y, et al. Flexible IoT security middleware for end-toend cloud–fog communication. Future Generation Computer Systems. 2018;87:688–703. Available from: https://dx.doi.org/10.1016/j.future.2017.12.031.
• Casola V, Benedictis AD, Riccio A, Rivera D, Mallouli W, de Oca EM. 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.
• 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.
• 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.
• Prabhakaran V, Kulandasamy A. Integration of recurrent convolutional neural network and optimal encryption scheme for intrusion detection with secure data storage in the cloud. Computational Intelligence. 2020;2020:1–27. Available from: https://dx.doi.org/10.1111/coin.12408.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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):1120–1120. Available from: https://dx.doi.org/10.3390/electronics9071120.
• Pahl MO, Aubet FX. DS2OS traffic traces. 2019. Available from: https://www.kaggle.com/francoisxa/ds2ostraffictraces.
• Pahl MO, Aubet FX. All eyes on you: Distributed Multi-Dimensional IoT microservice anomaly detection. In: and others, editor. 14th International Conference on Network and Service Management (CNSM). 2018;p. 72–80.
• Yang XS. Flower pollination algorithm for global optimization. In: and others, editor. International conference on unconventional computing and natural computation. Springer. 2012;p. 240–249. Available from: https://doi.org/10.1016/j.eswa.2016.03.047.
• 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.
• 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.

Back Download