Integration of Honeypots and Machine Learning in Network Security

Abstract

The integration of honeypots and machine learning technologies in network security offers a comprehensive and robust security management framework that enables organizations to secure their information systems from malwares like Trojan Horse, Adware and Spyware. There are a lot of proposed approaches in the area of network security but they still lack handling the newer malwares. System security software like firewall and antivirus fail to detect the malicious software. So the technique is the combination of these strategies allows security analysts to effectively conduct a dynamic analysis of network threats and automate threat management and modeling based on machine learning tools. Such automated analysis and control of breaches enable the company to understand the intent, source, nature, and functionality of the malware. This understanding leads to the development of robust security management frameworks that enhance the resilience and preparedness of the company. Support Vector Machine (SVM) and Decision Tree algorithms are implemented for classification of datasets. This paper examines this integration of honeypots in security network infrastructure and mitigating malicious software. Through this focus, the article offers a robust assessment of machine learning and honeypots in securing business information infrastructure.

Keywords: Decision tree, honeypot, machine learning, malware analysis, network security, SVM.