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@article{171977,
        author = {Bharath P and Jeevan S. M and K. M Dhanush and Karthik K. N and Sonia Peal K. P},
        title = {AIML IN CYBERSECURITY: DETECT INTRUSIONS AND INSIDER THREATS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1960-1964},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171977},
        abstract = {Any company can suffer greatly from insider threat attacks; identifying them early on with possible behavioral actions can help that organization avoid bad outcomes. By putting in place two levels of defense—one at the network’s entrance and another at the network’s core—our proposed approach tackles this problem. Insider threat detection finds any possible insiders in the network, while intrusion detection filters out known attacks. When used in an organizational setting, the insider threat detection model can distinguish between abnormal behaviors that are classified as insider threats and those that deviate from the norm. This is because the model is trained on the Long-Short- Term Memory (LSTM) model, which was able to learn normal user behavior patterns.},
        keywords = {LSTM, Insider Threats, Autoencoder},
        month = {January},
        }