Enhancing IoT Network Security through Prompt Intrusion Detection Using Machine Learning

International Journal of Computer Science and Engineering

© 2024 by SSRG - IJCSE Journal

Volume 11 Issue 4

Year of Publication : 2024

Authors : Ramineni Padmasree, Keerthana Muthyam

10.14445/23488387/IJCSE-V11I4P102

How to Cite?

Ramineni Padmasree, Keerthana Muthyam, "Enhancing IoT Network Security through Prompt Intrusion Detection Using Machine Learning," SSRG International Journal of Computer Science and Engineering , vol. 11,  no. 4, pp. 10-18, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I4P102

Abstract:

This study aims to enhance IoT network security by deploying rapid intrusion detection mechanisms fortified with machine learning techniques. Addressing the escalating security concerns surrounding IoT devices, the research develops effective strategies for swift intrusion identification and mitigation, encompassing various intrusion types such as Distributed Denial of Service (DDoS), Internet Control Message Protocol (ICMP), and Transmission Control Protocol Synchronize (TCP SYN). Leveraging supervised machine learning algorithms such as Support Vector Machines (SVM), Logistic Regression, Random Forest, and K-Nearest Neighbors (KNN), a highly accurate intrusion detection model is proposed. Evaluation of the model's performance, utilizing diverse datasets sourced from platforms like Kaggle, showcases notable accuracy rates across different intrusion types. Specifically, DDOS achieves 82% accuracy, TCP SYN attains 99.96%, and ICMP reaches 99.8% accuracy on average. Notably, Random Forest exhibits the highest accuracy among the tested algorithms. This research significantly contributes to strengthening IoT network security, bolstering overall resilience against malicious activities and unauthorized access.

Keywords:

DDoS, ICMP, Intrusion detection, IoT Network Security, Machine learning, TCP SYN.

References:

[1] Ansam Khraisat et al., “Survey of Intrusion Detection Systems: Techniques, Datasets and Challenges,” Cybersecurity, vol. 2, no. 1, pp. 1-22, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Nadia Chaabouni et al., “Network Intrusion Detection for IoT Security Based on Learning Techniques,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2671-2701, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Bruno Bogaz Zarpelão et al., “A Survey of Intrusion Detection in Internet of Things,” Journal of Network and Computer Applications, vol. 84, pp. 25-37, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Khalid Albulayhi et al., “IoT Intrusion Detection using Machine Learning with a Novel High Performing Feature Selection Method,” Applied Sciences, vol. 12, no. 10, pp. 1-30, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Mohamed Faisal Elrawy, Ali Ismail Awad, and Hesham F.A. Hamed, “Intrusion Detection Systems for IoT-Based Smart Environments: A Survey,” Journal of Cloud Computing, vol. 7, no. 1, pp. 1-20, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Emad E. Abdallah, Wafa’ Eleisah, and Ahmed Fawzi Otoom, “Intrusion Detection Systems Using Supervised Machine Learning Techniques: A Survey,” Procedia Computer Science, vol. 201, pp. 205-212, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Ala Al-Fuqaha et al., “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347-2376, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[8] P. Ray, and A. Mohapatra, “Security in Healthcare Applications using Wireless Medical Sensor Networks: A Survey,” Journal of Medical Systems, vol. 37, no. 3, p. 9975, 2013.
[9] Rick Hofstede et al., “Towards Real-Time Intrusion Detection for NetFlow and IPFIX,” Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013), Zurich, Switzerland, pp. 227-234, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Phurivit Sangkatsanee, Naruemon Wattanapongsakorn, and Chalermpol Charnsripinyo, “Practical Real-Time Intrusion Detection Using Machine Learning Approaches,” Computer Communications, vol. 34, no. 18, pp. 2227-2235, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Han Qiu et al., “Adversarial Attacks against Network Intrusion Detection in IoT Systems,” IEEE Internet of Things Journal, vol. 8, no. 13, pp. 10327-10335, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Marcello Rinaldo Martina, and Gian Luca Foresti, “A Continuous Learning Approach for Real-Time Network Intrusion Detection,” International Journal of Neural Systems, vol. 31, no. 12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Valerio Morfino, and Salvatore Rampone, “Towards Near-Real-Time Intrusion Detection for IoT Devices Using Supervised Learning and Apache Spark,” Electronics, vol. 9, no. 3, pp. 1-13, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Roberto Magán-Carrión et al., “Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches,” Applied Sciences, vol. 10, no. 5, pp. 1-21, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Nahida Islam et al., “Towards Machine Learning Based Intrusion Detection in IoT Networks,” Computers, Materials & Continua, vol. 69, no. 2, pp. 1801-1821, 2021. [CrossRef] [Google Scholar] [Publisher Link]
[16] Parag Verma et al., “A Novel Intrusion Detection Approach using Machine Learning Ensemble for IoT Environments,” Applied Sciences, vol. 11, no. 21, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Bambang Susilo, and Riri Fitri Sari, “Intrusion Detection in IoT Networks Using Deep Learning Algorithm," Information, vol. 11, no. 5, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Mohanad Sarhan et al., “Feature Extraction for Machine Learning-Based Intrusion Detection in IoT Networks,” Digital Communications and Networks, vol. 10, no. 1, pp. 205-216, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Dhanke Jyoti Atul et al., “A Machine Learning Based IoT for Providing an Intrusion Detection System for Security,” Microprocess and Microsystems, vol. 82, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Abhishek Verma, and Virender Ranga, “Machine Learning Based Intrusion Detection Systems for IoT Applications," Wireless Personal Communications, vol. 111, pp. 2287-2310, 2020.
[CrossRef] [Google Scholar] [Publisher Link]