Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)

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A Deep Learning-Based Framework for Arp Spoofing Attack Detection

Authors
S. Pavithraa1, *, V. Khanaa2
1Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India
2Department of Information Technology, Bharath Institute of Higher Education and Research, Chennai, India
*Corresponding author. Email: pavithraa.it@bharathuniv.ac.in
Corresponding Author
S. Pavithraa
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-654-8_4How to use a DOI?
Keywords
ARP spoofing; deep learning; LSTM; CNN; network security; temporal modeling
Abstract

ARP spoofing attacks are a serious risk to network security because they allow malevolent actors to intercept and alter network traffic, which frequently results in data breaches and information leaks. This paper introduces a deep learning-based method for identifying ARP spoofing that makes use of Long ShortTerm Memory (LSTM) networks and Convolutional Neural Networks (CNNs). The ARP traffic dataset was used to train and assess both models, which capitalized on the advantages of CNNs for spatial feature extraction and LSTMs for temporal sequence modeling. Recall, accuracy, precision, F1-score, false positive rate, and false negative rate were among the important performance indicators used to evaluate the models. Both CNN and LSTM demonstrated good detection accuracy in the experimental data, with CNN offering faster detection and LSTM exhibiting superior temporal sensitivity. These results underline the potential of deep learning approaches to improve real-time network security and demonstrate how well they detect ARP spoofing attacks.

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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Volume Title
Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
Series
Advances in Engineering Research
Publication Date
24 April 2026
ISBN
978-94-6239-654-8
ISSN
2352-5401
DOI
10.2991/978-94-6239-654-8_4How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - S. Pavithraa
AU  - V. Khanaa
PY  - 2026
DA  - 2026/04/24
TI  - A Deep Learning-Based Framework for Arp Spoofing Attack Detection
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 32
EP  - 45
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_4
DO  - 10.2991/978-94-6239-654-8_4
ID  - Pavithraa2026
ER  -