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http://dx.doi.org/10.33778/kcsa.2022.22.4.017

Attack Detection and Classification Method Using PCA and LightGBM in MQTT-based IoT Environment  

Lee Ji Gu (국방대학교 국방과학학과)
Lee Soo Jin (국방대학교 국방과학학과)
Kim Young Won (국방대학교 국방과학학과)
Publication Information
Convergence Security Journal / v.22, no.4, 2022 , pp. 17-24 More about this Journal
Abstract
Recently, machine learning-based cyber attack detection and classification research has been actively conducted, achieving a high level of detection accuracy. However, low-spec IoT devices and large-scale network traffic make it difficult to apply machine learning-based detection models in IoT environment. Therefore, In this paper, we propose an efficient IoT attack detection and classification method through PCA(Principal Component Analysis) and LightGBM(Light Gradient Boosting Model) using datasets collected in a MQTT(Message Queuing Telementry Transport) IoT protocol environment that is also used in the defense field. As a result of the experiment, even though the original dataset was reduced to about 15%, the performance was almost similar to that of the original. It also showed the best performance in comparative evaluation with the four dimensional reduction techniques selected in this paper.
Keywords
MQTTset; PCA; LightGBM; IoT attack detection;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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1 Young-Teak Oh, In-June Jo, "Data Modeling for Cyber Security of IoT in Artificial Intelligence Technology", International JOURNAL OF CONTENTS, Vol. 21, No. 12, pp 58-65, 2021.
2 Markts and Markets, "IoT Solutions and Service Market", https://www.marketsandmarkets.com/Market-Reports, TC7719, 2022.
3 IEEE Computer Society, "Internet of Things Meets the Military and Battlefield", https://www.computer.org/publications/tech-news/research/internet-of-military-battlefield-things-iomt-iobt, 2022.
4 Korea Defense Industry Accociation, "Development of defense IoT platform and solution for future intelligent resource management and battlefield management system", Defense & Technology, Vol 470, No. 28, pp 28-29, 2018.
5 Ahmad, Rasheed, and Izzat Alsmadi. "Machine learning approaches to IoT security: A systematic literature review." Internet of Things Vol. 14, 2021.
6 Y. Jang, J. Shim, and S. Park, "Analysis Standardized of IoT-based Low-power.Light -weight Protocol," Journal of the Korea Institute of Information and Communication Engineering, vol. 20, no. 10, pp. 1895-1902, Oct. 2016.   DOI
7 I. Skerritt, "IoT Developer Survey 2016," Eclipse IoT Work. Gruop, IEEE IoT Agil. IoT, 2016.
8 Ivan Vaccari, Giovanni Chiola, Maurizio Aiello, Maurizio Monelli, Enrico Cambiaso, "MQTTset, a New Dataset for Machine Learning Techniques on MQTT", Sensors, Vol. 20, 2020.
9 Kuriakose, Neenu, and Uma Devi. "MQTT Attack Detection Using AI and ML Algorithm." Pervasive Computing and Social Networking. Springer, Singapore, Vol. 317, 2022. 13-22.
10 Dissanayake, Maheshi B. "Feature Engineering for Cyber-attack detection in Internet of Things.", I.J Wireless and Microwave Technologies, Vol. 6, pp 46-54, 2021.
11 Rachmadi, Salman, Satria Mandala, and Dita Oktaria. "Detection of DoS Attack using AdaBoost Algorithm on IoT System", 2021 International Conference on Data Science and Its Applications (ICoDSA). IEEE, 2021.
12 Lee, Joohwa, and Keehyun Park. "Network Intrusion Detection System Using Feature Extraction Based on AutoEncoder in IOT environment." KTSDE, Vol. 8, No. 12, pp 483-490, 2019.
13 Hyoseon Kyew, Minhae Kwon, "PCA-Based Low-Complexity Anomaly", KCIS, Vol. 46, No. 6, pp 941-955, 2021.
14 Ke, Guolin, et al. "Lightgbm: A highly efficient gradient boosting decision tree." Advances in neural information processing systems 30, 2017.
15 Waskle, Subhash, Lokesh Parashar, and Upendra Singh. "Intrusion detection system using PCA with random forest approach." 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, pp. 803-808, 2020.
16 Martinez, Aleix M., and Avinash C. Kak. "Pcaversus lda." IEEE transactions on pattern analysis and machine intelligence Vol. 23, No. 2, pp 228-233, 2001.   DOI
17 Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D. and Saeed, J. "A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction.", Journal of Applied Science and Technology Trends Vol. 1, No. 2, pp. 56-70, 2020.   DOI