AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent

TL;DR

This paper introduces an AI-driven chatbot architecture that utilizes machine learning to detect network intrusions in edge networks, emphasizing ethical user consent to promote transparency and trust in cybersecurity.

Contribution

It presents a novel chatbot-based framework for intrusion detection in edge networks, integrating ethical considerations and leveraging Raspberry Pi devices for enhanced security.

Findings

Effective detection of network intrusions demonstrated

Enhanced security with ethical user consent implemented

Edge network monitoring with Raspberry Pi proved feasible

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.