Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{200367,
        author = {Swapnil Chaudhari and Abhishek Sachin Satpute and Vishwajit Vikram Gaikwad and Yash Sambhaji Kakade},
        title = {AI-DRIVEN INTRUSION DETECTION SYSTEM USING SSH HONEYPOTS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1249-1258},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200367},
        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 AIdriven
security solutions.},
        keywords = {Intrusion Detection System (IDS), SSH Honeypot, Machine Learning, Anomaly Detection, Cybersecurity},
        month = {May},
        }