Title: Network traffic analysis using machine learning techniques in IoT network

Authors: Shailendra Mishra

Addresses: Department of Computer Engineering, Majmaah University, Majmaah-11952, Saudi Arabia

Abstract: End-node internet-of-things devices are not very intelligent and resource-constrained; thus, they are vulnerable to cyber threats. They have their IP address, and once the hacker traces the IP, it becomes easy to get into the network and exploit the other devices. The support vector machine learning technique is used to classify normal and abnormal traffic. Mininet emulator is selected for network design, VMware fusion for creating a virtual environment, hosting OS is Ubuntu Linux, the network topology is a tree topology. Wireshark was used to open an existing packet capture file that contains network traffic. Signature-based and heuristic detection techniques were used to analyse the signature of the record found using a hex editor, and proposed rules are applied for searching and detecting that these files have this signature. The support vector machine classifier demonstrated the best performance with 99% accuracy.

Keywords: network traffic analysis; IoT; internet-of-things; cyber threats; cyber-attacks; machine learning.

DOI: 10.1504/IJVICS.2025.145792

International Journal of Vehicle Information and Communication Systems, 2025 Vol.10 No.2, pp.113 - 131

Received: 09 Jun 2020
Accepted: 24 Aug 2020
Published online: 24 Apr 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article