Proceedings of International Conference on Applied Innovation in IT
2025/07/26, Volume 13, Issue 3, pp.165-172

Advanced Network Security System: Honeypot-Based Intrusion Detection with Machine Learning and Visualization


Ashwini Lakshmipathy, Muthupandi Gurusamy, Siti Fatmawati Lalo, Nonia Sakka Lebang, Karthikeyan Selvaraj and Aws A. Abdulsahib


Abstract: This paper focuses on developing a proactive approach to network intrusion detection through integration of honeypots with machine learning for improved security in complex network system. The system utilizes honeypots to capture attackers whereby the honeypots capture real-time traffic details which the system maps and analyzes packet content related to protocols. Blending with machine learning, the detection model analyzes the accurate data to detect known as well as unknown forms of cyber threats. There is a new feature called Visualization Dashboard that gives analytics and reports to network administrators. It provides information about honeypot engagements, traffic, and intrusion detected empowering the monitoring and management process. Incorporating the proactive defense measures into the proposed system eliminates the weakness of the conventional intrusion detection approach in managing new forms of cyber threats. The honeypots are designed to contain the attackers and simultaneously acquiring useful information about the intrusive activities. The effectiveness is enhanced by the ability of the Machine Learning model in enhancing the detection rates besides the flexibility in accommodating new techniques of detection of attacks. The Visualization Dashboard improves usability since it contains an easily navigable interface for current security monitoring and past performance examination. This approach guarantees the entirety of network protection by integrating the effectiveness of the deception-based honeypot systems and the machine learning approach based on big data. The paper reveals that the system is capable of enhancing detection rates, reducing false positives and providing valuable information regarding the network status to administrators. Thus, the provided system is considered ideal for contemporary cybersecurity issues. Advanced technologies combined in this system offer a flexible and expandable system to protect networks from the steadily growing number of threats.

Keywords: Honeypots, Intrusion Detection Systems (IDS), Machine Learning in Cybersecurity, Network Security Visualization, Anomaly Detection.

DOI: Under Indexing

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