Defending Against Arpspoofing In Wifi Networks Using Rf Fingerprinting

Address Resolution Protocol (ARP) spoofing is a critical security threat in wireless networks where an attacker sends forged ARP messages to link their device with the IP address of a legitimate user. This attack allows malicious users to intercept, modify, or block data traffic between communicating devices, leading to serious issues such as data theft, session hijacking, and denial-of-service attacks. Traditional detection mechanisms mainly rely on software-based identifiers such as IP addresses and MAC addresses. However, these identifiers can be easily manipulated by attackers, making conventional solutions less effective in detecting sophisticated attacks. To overcome this limitation, this study proposes a defense mechanism against ARP spoofing in Wi-Fi networks using Radio Frequency (RF) fingerprinting. RF fingerprinting identifies wireless devices based on unique hardware-level characteristics of their transmitted signals. Features such as frequency offset, phase noise, and signal transient patterns are analyzed to generate distinct RF signatures for each device. The proposed system continuously monitors wireless transmissions and compares them with stored RF fingerprints to identify anomalies and detect unauthorized devices. By leveraging physical layer characteristics, the approach provides a reliable and difficult-to-forge method of authentication. Experimental results indicate that RF fingerprinting significantly improves the accuracy of ARP spoofing detection and strengthens overall wireless network security without requiring major modifications to existing infrastructure.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-01
DOI
https://doi.org/10.5281/zenodo.19221424
Primary Topic
Internet Traffic Analysis and Secure E-voting
Type
article
Field-Weighted Citation Impact
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article

Defending Against Arpspoofing In Wifi Networks Using Rf Fingerprinting

Mukilan R, Bharath K, Ms.K.Madhunitha, Deva Senathipathi M
Zenodo (CERN European Organization for Nuclear Research)
Internet Traffic Analysis and Secure E-voting
article

Defending Against Arpspoofing In Wifi Networks Using Rf Fingerprinting

Mukilan R, Bharath K, Ms.K.Madhunitha, Deva Senathipathi M
article en

Abstract

Address Resolution Protocol (ARP) spoofing is a critical security threat in wireless networks where an attacker sends forged ARP messages to link their device with the IP address of a legitimate user. This attack allows malicious users to intercept, modify, or block data traffic between communicating devices, leading to serious issues such as data theft, session hijacking, and denial-of-service attacks. Traditional detection mechanisms mainly rely on software-based identifiers such as IP addresses and MAC addresses. However, these identifiers can be easily manipulated by attackers, making conventional solutions less effective in detecting sophisticated attacks. To overcome this limitation, this study proposes a defense mechanism against ARP spoofing in Wi-Fi networks using Radio Frequency (RF) fingerprinting. RF fingerprinting identifies wireless devices based on unique hardware-level characteristics of their transmitted signals. Features such as frequency offset, phase noise, and signal transient patterns are analyzed to generate distinct RF signatures for each device. The proposed system continuously monitors wireless transmissions and compares them with stored RF fingerprints to identify anomalies and detect unauthorized devices. By leveraging physical layer characteristics, the approach provides a reliable and difficult-to-forge method of authentication. Experimental results indicate that RF fingerprinting significantly improves the accuracy of ARP spoofing detection and strengthens overall wireless network security without requiring major modifications to existing infrastructure.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Openalex Percentile: Top 10%
Internet Traffic Analysis and Secure E-voting
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