An Intelligent Honeypot System for Proactive Threat Detection and Engagement – Volume 12 Issue 5

The increasing sophistication of cyber threats made traditional intrusion observation and defense systems inadequate for modern attack approaches. Honeypot is a cybersecurity technique where a decoy system is created to set up which looks like a real computer, network, or service, helps to attract, detect, and study attackers. The paper hands an intelligent honeypot system augmented with adaptive artificial intelligence to preparedly find, categorize, and involve cyber adversaries. The considered system integrates real-time traffic monitoring, machine learning-based aims segmentation and flexible artifice methods that dynamically transform based on attacker’s interaction. Key advancements integrate context- aware record and document creation, sandboxed malware accomplishment for behavioral evaluation, and structured trap services that change to various attack vectors. Moreover, the framework embeds anomaly detection, attacker profiling, and automated reporting to elevate situational awareness for network administrators. By growing attacker attention span and truthfulness, the system obtains deeper threat assessment while residual resilient to elusion. Detailed modeling and evaluation demonstrate that the suggested system decreases false positives, enhances threat classification authenticity, and assists proactive countermeasures. The adaptive honeypot not only notices and inspects attacks but also supplies to predictive cybersecurity defense by schooling from evolving attack patterns. The conclusions emphasize the potential of AI- driven deception systems to transform honeypots from passive traps into active, intelligent cybersecurity mechanisms capable of addressing to rising threats in real time.

Honeypots evolved from rudimentary, rule-based decoys to advanced, intelligent systems that can identify and dissect sophisticated cyber threats. Initial implementations were mainly proof-of-concept tools for gathering limited attack evidence, yet recent innovations in machine learning, deep learning, and automation have made them an integral part of next-generation cybersecurity defense. By offering contained environments that entice attackers without risking production systems, honeypots will remain a significant asset for examining adverse tactics as well as creating threat intelligence that can be acted upon. Not with standing these advances, numerous significant challenges persist such as adversarial fingerprinting, scalability in IoT as well as cloud settings, as well as the legal/ethical implications of deception-oriented security. Attackers increasingly use AI-motivated evasion methods, pushing honeypots to increase more than static signatures and manually created rules. Additionally, maintaining data quality, system segregation assurance, as well as preventing misuse of gathered data, continue to be unending issues that hamper mass deployment. Overcoming these challenges will help maintain the reliability and credibility of honeypot-driven systems. The future thus for honeypot research is in the creation of adaptive deception systems that scale securely and interoperable across larger cybersecurity ecosystems and that are artificial intelligence-driven. Prospects such as federated honeypots, cloud-native systems, and IoT/ICS deception systems portend an integrated and intelligent future direction. Through the integration with explainable artificial intelligence, threat intelligence sharing, as well as ethical safe- guards, honeypots can transition from being passive traps towards being proactive, resilient tools for cyber defence that can no longer be avoided.

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