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@article{199970,
        author = {SUHAS BHIMRAO VEER},
        title = {Intrusion Detection in Operating Systems Using Artificial Intelligence Methods.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {523-530},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199970},
        abstract = {Operating systems are the backbone of computing infrastructure, handling memory management, process scheduling, file systems, and device interactions. Due to their central role, OS-level attacks such as rootkits, privilege bypassing, kernel code injection, and unauthorized system calls pose extreme risk to system integrity. Traditional host-based intrusion detection systems (HIDS) depend on static rules and predefined patterns, which are unable to identify emerging zero-day threats. AI-driven methods have the capacity to learn hidden relationships within OS-level logs and system call sequences, making them ideal for detecting abnormal behaviors. This paper studies AI approaches for OS intrusion detection, proposes an AI-enabled HIDS model, and presents a full case study including diagrams and GUI prototypes.},
        keywords = {Operating System Security, Intrusion Detection System, Machine Learning, Deep Learning, Anomaly Detection, AI Security, Host-based IDS.},
        month = {May},
        }