A NOVEL APPROACH FOR ADDRESSING IOT NETWORKS VULNERABILITIES IN DETECTION AND CLASSIFICATION OF DOS/DDOS ATTACKS
DOI:
https://doi.org/10.15282/ijsecs.10.1.2024.5.0123Keywords:
IoT security, Real-time DDoS detection, Kth Nearest Neighbor, Dense neural networksAbstract
The substantial growth of Internet-connected devices within the Internet of Things (IoT) has given rise to significant security challenges. Among the various threats confronting these interconnected devices, Denial of Service (DoS)/Distributed Denial of Service (DDoS) attacks emerge as significant concerns. The attacks, which seek to disrupt IoT services by flooding networks with unnecessary traffic, there is a critical need for robust security measures. Intrusion Detection Systems (IDS) are vital in identifying suspicious activities, yet many existing systems lack real-time capabilities to address evolving attack strategies. This study investigates the vulnerabilities of IoT networks and the pressing need to detect and classify DoS/DDoS attacks in real time. Traditional IDS, while effective in recognizing known attack patterns, fall short in identifying new attack types due to their reliance on historical data. To bridge this gap, this research focuses on developing an enhanced hybrid network intrusion detection and classification system. This study seeks to make contribution to the advancement of resilient security measures within IoT environments. The framework uses the Kth Nearest Neighbor (KNN) algorithm and dense neural networks to efficiently detect and categorize DoS/DDoS attacks in real time. To achieve this, a simulation model implementing the proposed hybrid algorithm will be developed. The framework utilized the MQTT-IoT-IDS2020 dataset, and a comparison was presented with recent approaches found in the literature. The results demonstrated enhanced performance in terms of true positive rate, accuracy, speed of detection.
References
M. Z. e. al., "Protocol-Based Deep Intrusion Detection for DoS and DDoS Attacks Using UNSW-NB15 and Bot-IoT Data-Sets, IEEE Access, vol. 10, pp. 2269-2283, 2022, doi: 10.1109/ACCESS.2021.3137201, 2022.
R. K. a. D. Eleyan, "Survey of DoS/DDoS attacks in IoT," Sustainable Engineering and Innovation ISSN 2712-0562, p. 7, 2021.
Barati, "Distributed Denial of Service Detection Using Hybrid Machine Learning Technique," International Symposium on Biometrics and Security Technologies (ISBAST), p. 6, 2014.
L. Huraj, "IoT measuring of UDP-based Distributed Reflective DoS Attack," SISY 2018 • IEEE 16th
International Symposium on Intelligent Systems and Informatics • September 13-15, 2018, Subotica, Serbia, p. 6, 2018.
B. Stackpole, [Symantec](https://www.symantec.com/blogs/feature-stories/iot-attacks-rise), 2020.
Y. Wu, "Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey," Security and Communication Networks Volume 2020, Article ID 8872923, , p. 17, 2020.
Hussain, "IoT DoS and DDoS Attack Detection using ResNet," 2020 IEEE 23rd International Multitopic Conference (INMIC), Bahawalpur, Pakistan, 2020, pp. 1-6, , p. 7, 2020.
Y. Y. a. S. U. Keval Doshi, "Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks," https://doi.org/10.48550/arXiv.2006.08064, p. 13, 2020.
Ge, "Deep Learning-based Intrusion Detection for IoT Networks," 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), p. 10, 2019.
Z. B. ,. A. I. &. C. V. Naeem Firdous Syed, "Denial of service attack detection through machine learning for the IoT," Journal of Information and Telecommunication, DOI: 10.1080/24751839.2020.1767484, p. 23, 2020.
Asad, "DeepDetect: Detection of Distributed Denial of Service Attacks Using Deep Learning," The British Computer Society 2019. All rights reserved., p. 12, 2019.
Munshi, Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction07, https://doi.org/10.1371/journal.pone.0296107, 2022.
R. A. B. A. a. X. Hanan Hindy, "MQTT-IOT-IDS2020: MQTT INTERNET OF THINGS INTRUSION
DETECTION DATASET," 2020.
G. X. F. &. Z. B. Mengmeng, "Deep Learning-based Intrusion Detection for IoT Networks," 2019 IEEE 24th Pacific Rim International Symposium on Dependable Computing (PRDC), p. 10, 2019.
Rezvy, "Intrusion Detection and Classification with Autoencoded Deep Neural Network," Springer Nature Switzerland AG J.-L. Lanet and C. Toma (Eds.): SecITC LNCS 11359, pp. 142–156,, p. 15, 2019.
S. F. C. H. B. A. H. R. R. Mohammad, "Classification and Detection ofMalicious Attacks in Industrial IoT Devices via Machine Learning," K.-Y. Kim et al. (Eds.): FAIM 2022, LNME, pp. 99–106,
https://doi.org/10.1007/978-3-031-18326-3_10, p. 10, 2023.
Doshi, "Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks,"
https://doi.org/10.48550/arXiv.2006.08064, p. 13, 2020.
Rezvy., "An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks," 978-1-7281-1151-3/19/$31.00 ©2019 IEEE, p. 6, 2019.
F. S. Z. B. ,. A. I. &. C. V. Naeem, "Denial of service attack detection through machine learning for the IoT," Journal of Information and Telecommunication, DOI: 10.1080/24751839.2020.1767484, p. 23, 2020.
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