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Modeling of Hybrid Intrusion Detection System in Internet of Things using Support Vector Machine and Decision Tree

by John Kwesi Amfo, James Ben Hayfron-Acquah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 15
Year of Publication: 2018
Authors: John Kwesi Amfo, James Ben Hayfron-Acquah
10.5120/ijca2018917799

John Kwesi Amfo, James Ben Hayfron-Acquah . Modeling of Hybrid Intrusion Detection System in Internet of Things using Support Vector Machine and Decision Tree. International Journal of Computer Applications. 181, 15 ( Sep 2018), 45-52. DOI=10.5120/ijca2018917799

@article{ 10.5120/ijca2018917799,
author = { John Kwesi Amfo, James Ben Hayfron-Acquah },
title = { Modeling of Hybrid Intrusion Detection System in Internet of Things using Support Vector Machine and Decision Tree },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 181 },
number = { 15 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 45-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number15/29902-2018917799/ },
doi = { 10.5120/ijca2018917799 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:06:07.012435+05:30
%A John Kwesi Amfo
%A James Ben Hayfron-Acquah
%T Modeling of Hybrid Intrusion Detection System in Internet of Things using Support Vector Machine and Decision Tree
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 15
%P 45-52
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Internet of Things (IoT) system, according to literature is prone to many attacks from its interconnected devices. Most of these security treats that IoT is likely to encounter have been identified. These attacks and other security issues have called for the modeling and implementation of algorithms that can identify the current and emerging intrusion and vulnerabilities in IoT so that best security preventive methods may be deployed against them. In this study, a new algorithm, is proposed; cascading Decision Tree (DT) algorithm and Support Vector Machine (SVM) algorithm to improve classification of attacks and consequently the security systems. The proposed algorithm used Support Vector Machine for selection of features based on correlation in the features of the Network Socket Layer - Knowledge Discovery Data (NSL-KDD) data sets. And classification of intrusions was based on the DT algorithm, due to its performance over the SVM. The result of the proposed algorithm proved that the classification of attacks in the decision tree algorithm is improved in terms of prediction speed and training time. In addition, it enhanced the performance of Decision Tree in classifying misclassified classes such as “rootkit” in intrusion detection.

References
  1. Gokul P., Sreeram S., Chandra M. and Reddy S. (2015). Development of Industrial Intrusion Detection and Moni-toring Using Internet of Things. International Journal of Technical Research and Applications, 84-89.
  2. Le A., Loo J, Luo Y, and Lasebae A., (2011) Specificationbased IDS for securing RPL from topology attacks. IEEE, 978-1-4577-2028-4/11.
  3. . Li S., Li Da Xu, and Zhao S. (2015). The Internet of things: a survey. Springer Information Systems Frontiers, 17(2), 243259
  4. Mahbod T., Ebrahim B., Wei L., and Ali A. G. (2009). A Detailed Analysis of the KDD CUP 99 Data Set. National Research Council, Canada
  5. Okan C., and Ozgur K. S. (2015). A Survey of Intrusion Detection Systems in Wireless Sensor Networks. 6th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO), 40-43
  6. Pongle P., and Chavan G. (July 2015.). Real Time Intrusion and Wormhole Attack Detection in Internet of Things. International Journal of Computer Applications, 1-9.
  7. Rahaman A. S., Rashmi R. S., Moutushi S., Souvik S., Jamuna K. S., and Koushik M. (2014). Intelligent Intrusion Detection System in Wireless Sensor Network. Advances in Intelligent Systems and Computing 328. Springer, 707-711.
  8. Ren, L. (2015). IoT security: problems, Challenges and Solution. Santa Clara, CA, 1-32
  9. Schneier, B. (2014). Schneier on security. Retrieved August 18, 2016, from https://www.schneier.com/blog/archives/2014/01/ security risks 9.html
  10. Shanzhi C., Hui X, Dake L., Bo H. and Hucheng W. (2014). A Vision of IoT: Applications, Challenges, and Opportunities with China Perspective. IEEE Internet of Things Journal, 1(4), 349 - 359.
  11. Shojaie, Z. (2005). Algorithms and Advanced Data mining Concept. Jahad Daneshgahi, Amirkabir University of Technology. Tehran, 4-9.
  12. Snehal S. S., and Daivshala R. D. (2016). Improved Intrusion Detection System using cascading of C4.5 Decision Tree and Support Vector Machine. International Journal of Emerging Technology and Advanced Engineering , 6(8), 167-169.
  13. Vaishali K. and Sangita S. C. (2014). Improved Intrusion Detection System using C4.5 Decision Tree and Support Vector Machine. International Journal of Computer Science and Information Technologies, 5(2), 1463-1467
  14. Vaishali Kosamkar (2013) Improved Intrusion Detection System using C4.5 Decision Tree and Support Vector Machine, Master’s Thesis, Department of Computer Engineering, University of Mumbai, Retrieved from: www.academia.edu/16645429/Vaishali mam report
  15. Wu, S. Y. (2009). Data mining-based intrusion detectors. Expert Systems with Applications, 56(1), 36
Index Terms

Computer Science
Information Sciences

Keywords

Internet of Things Intrusion Detection Support Vector Machine Decision Tree Feature Selection.

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