Integrating Machine Learning-Powered Smart Agents into Cyber Honeypots: Enhancing Security Frameworks

Software systems are installed and configured by default; there are security loopholes that could be exploited even though the vast majority of internet users do not yet have a strong sense of security even though more. As the number of nodes with Internet access increases, more and more data is being generated on the Internet itself and among those nodes. Because of the emergence of new methods for attacking networks, every host on the Internet say now be considered a potential target. Because of this, the issue of the security of the information sent across networks cannot be overlooked. The honeypot approach can not only be used passively as an information system to cope with zero-day and future assaults. Still, it can also be used actively to strengthen old systems against attacks in the future. As a consequence of this, we use machine learning to build a Cyber Security Honeypot-Based Smart Agent. In this, we integrate several different machine learning approaches with honeypot mechanisms so that we may anticipate the attacker's profile and stop them. In this section, we compare the two findings achieved with and obtained without the assistance of the honeypot and depict the comparison results.