DESIGNING AN ADAPTIVE HONEYPOT FOR ADVANCED CYBERSECURITY THREAT DETECTION

Abstract

Honeypots, which mimic real systems and attract attackers into controlled environments, are essential parts of contemporary cybersecurity. They are made to identify, evaluate, and lessen cyberthreats. The categories of honeypots, such as low-, medium-, and high-interaction kinds, as well as hybrid models that maximize resource use and threat intelligence collection, are thoroughly examined in this research. The efficiency of tools like Honeyd, Dionaea, Kippo, and Honeypot-as-a-Service (HaaS) in cloud, industrial, and Internet of Things ecosystems is evaluated. Honeypots are incorporated with new tactics, such as quantum-enhanced unpredictability and Deepfake-driven deception, to increase their resistance to APTs and zero-day vulnerabilities. The integration of ChatGPT to dynamically engage attackers and collect actionable intelligence, as well as containerized honeypot deployments utilizing Kubernetes, are also highlighted in the paper. Their usefulness is illustrated by real-world use cases, such as ransomware detection and web portal honeypots. To handle changing cybersecurity issues, this study suggests sophisticated honeypot frameworks by combining modern technologies and flexible strategies. These frameworks highlight how important honeypots are for protecting vital infrastructures, improving threat intelligence, and offering a strong, proactive defence against the intricacies of contemporary cyberattacks.