Honeypot-Based Thread Detection using Machine Learning Techniques
Honeypot-Based Thread Detection using Machine Learning Techniques |
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| © 2023 by IJETT Journal | ||
| Volume-71 Issue-8 |
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| Year of Publication : 2023 | ||
| Author : Diandra Amiruddin Firmansyah, Amalia Zahra |
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| DOI : 10.14445/22315381/IJETT-V71I8P221 | ||
How to Cite?
Diandra Amiruddin Firmansyah, Amalia Zahra, "Honeypot-Based Thread Detection using Machine Learning Techniques," International Journal of Engineering Trends and Technology, vol. 71, no. 8, pp. 243-252, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I8P221
Abstract
This paper explores the application of machine learning techniques to honeypot-based thread detection in cybersecurity. Honeypot is a decoy system designed to lure attackers and gather information about their methods and objectives. Honeypot-based thread detection is a proactive approach to cybersecurity that can identify and prevent attacks before they cause damage. However, the sheer volume of data generated by honeypots can be overwhelming for human analysts. In this context, machine learning techniques can help automate the analysis of honeypot data and improve threat detection accuracy. The performance is evaluated using real-world honeypot data. Based on the experiment results, the Random Forest algorithm demonstrated superior performance compared to other algorithms, with an accuracy rate of 99.20% for detecting malware. The results show that machine learning can significantly enhance the effectiveness of honeypot-based thread detection, enabling cybersecurity analysts to identify and respond to threats more quickly and efficiently.
Keywords
Malware, Machine learning, Honeypot.
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