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

In today’s contemporary digital landscape, chat-bots 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 monitors 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.

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Publication Details

Published
2024-10-15
DOI
https://doi.org/10.1109/hite63532.2024.10777142
Citations
3
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
1.05
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article

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

Muhammad Umair, Mamoona Naveed Asghar, Abdul Rehman, Muhammad Asif et al.
3 citations
Network Security and Intrusion Detection
1.05
article

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

Muhammad Umair, Mamoona Naveed Asghar, Abdul Rehman, Muhammad Asif, Abdul Manan
article en
3 citations

Abstract

In today’s contemporary digital landscape, chat-bots 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 monitors 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.

University of Central Punjab (PK)
Openalex Percentile: Top 20%
Network Security and Intrusion Detection
1.05
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