A Novel IDS System based on Hedge Algebras to Detect DDOS Attack in IoT Systems

Hoang Trong Minh, Vu Nhu Lan, Nguyen Nam Hoang
Author affiliations

Authors

  • Hoang Trong Minh Posts and Telecoms Institute of Technology/Telecoms Faculty, 122 Hoang Quoc Viet Street, Cau Giay district, Ha Noi, Viet Nam https://orcid.org/0000-0001-8486-2940
  • Vu Nhu Lan Thang Long University/ Informatics Faculty, Nghiem Xuan Yem Street, Hoang Mai district, Ha Noi, Viet Nam
  • Nguyen Nam Hoang University of Engineering and Technology, Vietnam National University Hanoi, 144 Xuan Thuy Street, Cau Giay district, Ha Noi, Viet Nam

DOI:

https://doi.org/10.15625/2525-2518/18233

Keywords:

Internet of Things, Intrusion Detection System, DDOS, hedge algebra, PSO algorithm

Abstract

In recent years, we have experienced IoT solutions' rapid and beneficial development throughout all aspects of life. In addition to the apparent advantages, the increased number and variety of devices have resulted in more security issues. The DDOS attack, which originates from a broad range of sources and is a significant challenge for IoT systems, is one of the most prevalent but devastating attacks. IoT devices are typically simple and have few computing resources, which puts them at risk of being infected and attackers. IDS intrusion detection systems are considered superior protection against DDOS attacks. Therefore, the IDS system attracts many researchers and implements intelligent techniques such as machine learning and fuzzy logic to detect these DDOS attacks quickly and precisely. Along with the approach of intelligent computation, this study presents a novel technique for detecting DDOS attacks based on hedge algebra, which has never been implemented on IDS systems. We use the PSO swarm optimization algorithm to optimize the proposed model's parameters for optimized performance. Our experiment on the IoT-23 dataset shows that the proposed model's accuracy and performance metrics for DDOS attack detection are better than those proposed by other previous authors.

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References

Khanna A. and Kaur S. - Internet of things (IoT), applications and challenges: a comprehensive review, Wireless Personal Communications 114 (2) (2020) 1687-176. DOI: https://doi.org/10.1007/s11277-020-07446-4

Jayashree M., Mishra S., Patra S., Pati B., and Panigrahi C. R. - IoT security, challenges, and solutions: a review, Progress in Advanced Computing and Intelligent Engineering, 2021, pp. 493-504. DOI: https://doi.org/10.1007/978-981-15-6353-9_46

Ruchi V. and Jain A. K. - A survey of DdoS attacking techniques and defense mechanisms in the IoT network, Telecommunication systems 73 (1) (2020) 3-25. DOI: https://doi.org/10.1007/s11235-019-00599-z

Mohammed S. M., Rathore S., and Park J. H. - Distributed denial of service attacks and its defenses in IoT: a survey, The Journal of Supercomputing 76 (7) (2020) 5320-5363. DOI: https://doi.org/10.1007/s11227-019-02945-z

Markus R., Wunderlich S., Scheuring D., Landes D., and Hotho A. - A survey of network-based intrusion detection data sets, Computers and Security 86 (2019) 147-167. DOI: https://doi.org/10.1016/j.cose.2019.06.005

Gilberto F., Rodrigues J., Carvalho L. F., Al-Muhtadi J. F., and Proenca M. L. - A comprehensive survey on network anomaly detection, Telecommunication Systems 70 (3) (2019) 447-489. DOI: https://doi.org/10.1007/s11235-018-0475-8

Ankit T., and Lohiya R. - A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges, Archives of Computational Methods in Engineering 28 (4) (2021) 3211-3243. DOI: https://doi.org/10.1007/s11831-020-09496-0

Mendonca P., Robson V., Teodoro A., Rosa R. L., Saadi M., Melgarejo D. C., Nardelli P., and Rodrıguez D. R. - Intrusion detection system based on fast hierarchical deep convolutional neural network, IEEE Access 9 (2021) 61024-61034. DOI: https://doi.org/10.1109/ACCESS.2021.3074664

Rasheed A., and Alsmadi I. - Machine learning approaches to IoT security: A systematic literature review, Internet of Things 14 (2021) 100365. DOI: https://doi.org/10.1016/j.iot.2021.100365

Mohammed M., and Al-sultan G. A. - Network intrusion detection system using deep neural networks, In Journal of Physics: Conference Series 1804 (1) (2021) 012138. DOI: https://doi.org/10.1088/1742-6596/1804/1/012138

Amjad A., Sampalli S., and Bodorik P. - DdoS detection system: Using a set of classification algorithms controlled by the fuzzy logic system in Apache spark, IEEE Transactions on Network and Service Management 16 (3) (2019) 936-949. DOI: https://doi.org/10.1109/TNSM.2019.2929425

Mohammad M., and Khezri H. - Towards fuzzy anomaly detection-based security: a comprehensive review, Fuzzy Optimization and Decision Making 20 (1) (2021) 1-49. DOI: https://doi.org/10.1007/s10700-020-09332-x

Tohid J., Masdari M., Ghaffari A., and Majidzadeh K. - A survey and classification of the security anomaly detection mechanisms in software-defined networks, Cluster Computing 24 (2) (2021) 1235-1253. DOI: https://doi.org/10.1007/s10586-020-03184-1

Pajouh H., Dehghantanha H. A., Parizi R. M., Aledhari M., and Karimipour H. - A survey on Internet of things security: Requirements, challenges, and solutions. Internet of Things 14 (2021) 100129. DOI: https://doi.org/10.1016/j.iot.2019.100129

Parmisano A., Garcia S., and Erquiaga M. J. - A labeled dataset with malicious and benign IoT network traffic. https://www.stratosphereips.org/datasets-IoT-23. https://www.stratosphereips.org/datasets-IoT-23.">

Stoian N. A. - Machine Learning for anomaly detection in IoT networks: Malware analysis on the IoT-23 data set. Bachelor's thesis, University of Twente, 2020.

Imtiaz U., Ullah A., and Sajjad M. - Towards a Hybrid Deep Learning Model for Anomalous Activities Detection in Internet of Things Networks, Internet of Things 2 (3) (2021) 428-448. DOI: https://doi.org/10.3390/iot2030022

Imtiaz U., and Mahmoud Q. H. - Design and development of a deep learning-based model for anomaly detection in IoT networks, IEEE Access 9 (2021) 103906-103926. DOI: https://doi.org/10.1109/ACCESS.2021.3094024

Rameem Z. S., and Chishti M. A. - A generic and lightweight security mechanism for detecting malicious behavior in the uncertain Internet of Things using fuzzy logic and fog-based approach, Neural Computing and Applications 34 (9) (2022) 6927-6952. DOI: https://doi.org/10.1007/s00521-021-06823-9

Mohammad A., Al-Sawwa J., and Alkasassbeh M. - Anomaly-based intrusion detection system using fuzzy logic, In 2021 IEEE International Conference on Information Technology (ICIT), 2021, pp. 290-295.

Nguyen V. T., Nguyen T. X., Hoang T. M., and Vu N. L. - A new anomaly traffic detection based on a fuzzy logic approach in wireless sensor networks, In Proceedings of the Tenth International Symposium on Information and Communication Technology, 2019, pp. 205-209. DOI: https://doi.org/10.1145/3368926.3369714

Hoang T. M. - A Study on Anomaly Data Traffic Detection Method for Wireless Sensor Networks, In The International Conference on Intelligent Systems & Networks, 2021, pp. 429-436. DOI: https://doi.org/10.1007/978-981-16-2094-2_52

Ho N. C., and Wechsler W. - Hedge algebras: an algebraic approach to the structure of sets of linguistic truth values, Fuzzy sets and systems 35 (3) (1990) 281-293. DOI: https://doi.org/10.1016/0165-0114(90)90002-N

Ngo H. H., Ho N. C., and Nguyen V. Q. - Multichannel image contrast enhancement based on linguistic rule-based intensification, Applied Soft Computing 76 (2019) 744-762. DOI: https://doi.org/10.1016/j.asoc.2018.12.034

Hoang T., Nguyen T., Vu N., and Nguyen H. - A Novel Fuzzy Inference System Based on Hedge Algebras to Enhance Energy Efficiency in Wireless Sensor Networks, 2018 IEEE 3rd International Conference on Communication and Information Systems (ICCIS), 2018, pp. 73-78. DOI: https://doi.org/10.1109/ICOMIS.2018.8644986

Shami T. M., El-Saleh A. A., Alswaitti M., Al-Tashi Q., Summakieh M. A., and Mirjalili S. - Particle Swarm Optimization: A Comprehensive Survey, In IEEE Access 10 (2022) 10031-10061. DOI: https://doi.org/10.1109/ACCESS.2022.3142859

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Published

15-12-2023

How to Cite

[1]
H. T. Minh, Vu Nhu Lan, and Nguyen Nam Hoang, “A Novel IDS System based on Hedge Algebras to Detect DDOS Attack in IoT Systems”, Vietnam J. Sci. Technol., vol. 61, no. 6, pp. 1089–1101, Dec. 2023.

Issue

Section

Electronics - Telecommunication