A Hybrid Deep Learning-Based ARP Attack Detection and Classification Method

Yeshareg Muluneh

A Hybrid Deep Learning-Based ARP Attack Detection and Classification Method

Files

Yeshareg Muluneh 2023.pdf (2.8 MB)

Date

2023-12

Authors

Yeshareg Muluneh

Publisher

Addis Ababa University

Abstract

To map the Internet Protocol (IP) addresses to the Media Access Control (MAC) addresses and vice versa in local area network communication, the Address Resolution Protocol (ARP) is the most crucial protocol. ARP, however, is an unauthenticated protocol that lacks security features and is stateless in nature. Therefore, ARP is vulnerable to many attacks, and it can be easily exploited to gain unauthorized access to one's sensitive data and transmit bogus ARP messages to poison the ARP caches of the hosts within the local area network. These attacks may result in a loss of data integrity, confidentiality, and the availability of an organization's information. Many researchers have struggled to detect ARP attacks using different methods. However, some of these papers are not time-effective, require more human effort and involvement, and have high communication overhead. The other works use machine learning and deep learning methods, which have better solutions for detecting ARP attacks. However, those approaches have a significant false alarm rate of 13%, a low attack detection rate, and a classification accuracy of 87%. This thesis work aims to solve those problems using a hybrid deep learning-based ARP attack detection and classification method. In this work, we used a Sparse Autoencoder for important feature extraction and dimensionality reduction for input data and a Convolutional Neural Network for attack detection and classification to achieve the highest attack detection rate and classification accuracy with a minimized false alarm rate. To evaluate the performance of the proposed model, we used an open-source benchmark NSL-KDD dataset for training and testing. The results obtained by the implementation and evaluation are measured in comparison with a single Convolutional Neural Network model with different evaluation metrics. Hence, the proposed approach scores the highest results for attack detection rate of 98.97%, classification accuracy of 99.26%, and minimum false alarm rate of 0.74%.