Detecting Spoofing Attacks in IoT Networks Using Machine Learning Techniques

DOI

10.2991/978-94-6239-654-8_3How to use a DOI?

Keywords

IoT security; ARP spoofing; DNS spoofing; machine learning; Random Forest; ensemble learning; network integrity

Abstract

As Internet of Things (IoT) devices proliferate in retail and commercial sectors, maintaining strong security has emerged as a crucial concern. Spoofing attacks, including DNS spoofing and Address Resolution Protocol (ARP) spoofing, are among the many vulnerabilities that IoT systems must contend with. These attacks represent serious hazards to system dependability and data integrity. By tampering with domain resolution procedures, DNS spoofing allows attackers to reroute network traffic and divert users to malicious or fake websites. Similar to this, ARP spoofing allows hackers to intercept, change, or redirect interactions among devices by mapping a genuine IP address to a fake MAC address, thereby taking advantage of weaknesses in local networks. In order to detect DNS and ARP spoofing operations in IoT networks, this study proposes an integrated machine learning-based detection method. The suggested methodology makes use of a feature-rich dataset with various parameters that covers a variety of network behavior topics. The classifier using Random Forests performed better than the other algorithms that were assessed, with an F1 score of 94.1%, an accuracy rate of 95%, and a precision of 94.2%. These findings demonstrate how ensemble learning approaches might improve IoT security. The dual-spoofing detection method used in this study is its main innovation; it provides a scalable and effective way to protect IoT environments from sophisticated cyberthreats.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Pavithraa
AU  - V. Khanaa
PY  - 2026
DA  - 2026/04/24
TI  - Detecting Spoofing Attacks in IoT Networks Using Machine Learning Techniques
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 18
EP  - 31
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_3
DO  - 10.2991/978-94-6239-654-8_3
ID  - Pavithraa2026
ER  -