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@article{190918,
        author = {Shivansh Pandey and Sahil Kerekar and Jayesh Shinde},
        title = {AI-Driven Honeypot System for Proactive Detection: A Comprehensive Architectural and Empirical Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {5310-5319},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190918},
        abstract = {The escalating complexity of cyberattacks, marked by polymorphism and the prolific use of zero-day exploits, has rendered conventional, signature-based security defences fundamentally inadequate. This inadequacy necessitates a shift toward systems capable of proactive, behavioural analysis focused on Tactics, Techniques, and Procedures (TTPs). This paper presents a novel AI-driven adaptive honeypot system designed for high-fidelity threat detection and intelligence gathering. The core solution involves the architectural fusion of a low-interaction honeypot (Cowrie) and a network Intrusion Detection System (IDS) (Suricata), channelled through a real-time Kafka/Logstash data pipeline into a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) deep learning engine. The efficacy of this synergistic integration is empirically demonstrated, quantifying its superiority over static systems. Key findings include a significant increase in attacker engagement time, a detection rate (Recall) exceeding against simulated threats, and a False Positive Rate (FPR) reduced to. The system contributes a resilient, proactive defence architecture optimized for temporal sequence analysis, effectively transforming the honeypot from a passive trap into an active, adaptive deception platform.},
        keywords = {},
        month = {February},
        }