Evaluation of Campus Wi-Fi Network Security Against Man-in-the-Middle Attacks Using AI-Based Intrusion Detection Sistems
DOI:
https://doi.org/10.70062/jeci.v1i3.222Keywords:
AI-Based IDS, Detection Accuracy, Machine Learning, MITM Attacks, Real-Time MonitoringAbstract
This research focuses on the effectiveness of an AI-based Intrusion Detection System (IDS) in detecting Man-in-the-Middle (MITM) attacks within campus Wi-Fi networks. MITM attacks are a significant threat to network security, as they allow attackers to intercept and manipulate communications between users and network services. The objective of this study is to evaluate the AI-based IDS’s ability to detect MITM attacks with higher accuracy and faster detection times compared to traditional signature-based IDS. The research employs machine learning (ML) and deep learning (DL) techniques, such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN), to analyze Wi-Fi traffic for MITM attack detection. The study also investigates the effectiveness of hybrid models, combining multiple AI algorithms, to improve detection rates and reduce false positives. The main findings indicate that the AI-based IDS achieved 98% accuracy, significantly outperforming traditional IDS, which only reached 85%. The AI-based system also demonstrated low false positive (1.5%) and false negative (2%) rates, with an average detection time of 0.5 seconds per packet. These results highlight the superiority of AI-based IDS in terms of detection speed, accuracy, and adaptability to evolving attack methods. The study concludes that AI-powered IDS offer a more reliable and efficient solution for protecting campus networks against MITM attacks, with recommendations for future improvements, including integrating advanced AI models and expanding the dataset.
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