AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent
Mugheez Asif, Abdul Manan, Abdul Moiz ur Rehman, Mamoona Naveed Asghar, Muhammad Umair
TL;DR
The paper addresses intrusion detection in edge networks by deploying an AI-powered chatbot linked to a Raspberry Pi-based virtual network, integrating OTP-based user authentication and ethical consent. It combines a four-component architecture (Virtual Network, Chatbot, Authentication/Storage, Intrusion Detection) with ML-based detectors, evaluating on the $NSL-KDD$ dataset using a $DT$ and a $RF$. Results show the $DT$ generally offers higher accuracy and recall, supporting real-time intrusion detection while maintaining user transparency. The work highlights ethical consent as a core pillar for trustworthy, scalable edge cybersecurity, and points to future enhancements in AI algorithms and broader threat coverage.
Abstract
In today's contemporary digital landscape, chatbots have become indispensable tools across various sectors, streamlining customer service, providing personal assistance, automating routine tasks, and offering health advice. However, their potential remains underexplored in the realm of network security, particularly for intrusion detection. To bridge this gap, we propose an architecture chatbot specifically designed to enhance security within edge networks specifically for intrusion detection. Leveraging advanced machine learning algorithms, this chatbot will monitor network traffic to identify and mitigate potential intrusions. By securing the network environment using an edge network managed by a Raspberry Pi module and ensuring ethical user consent promoting transparency and trust, this innovative solution aims to safeguard sensitive data and maintain a secure workplace, thereby addressing the growing need for robust network security measures in the digital age.