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@article{186921,
        author = {Abhishek Sachin Satpute and Vishwajit Vikram Gaikwad and Yash Sambhaji Kakade and Swapnil Chaudhari},
        title = {AI-DRIVEN INTRUSION DETECTION SYSTEM USING SSH HONEYPOTS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {4334-4343},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186921},
        abstract = {With the rapid evolution of cyber threats targeting critical services like SSH, traditional Intrusion Detection Systems (IDS) are often unable to handle zero-day attacks and advanced persistent threats. This work proposes an intelligent IDS powered by SSH honeypots combined with machine learning. The honeypots simulate vulnerable SSH services to capture attacker behavior, which is then analyzed using Random Forest classifiers and Autoencoders for accurate intrusion detection. Our AI-based framework shows robust detection rates across multiple attack vectors, offering dynamic adaptability to evolving threats. The proposed system demonstrates a promising defense mechanism, bridging the gap between traditional signature-based systems and modern AI-driven security solutions.},
        keywords = {Intrusion Detection System (IDS), SSH Honeypot, Machine Learning, Anomaly Detection, Cybersecurity},
        month = {November},
        }