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@article{198638,
        author = {Jidnyasa Patil and Priti Palkar and pragati patil and mansi teltumbde},
        title = {Automated log analysis and anomaly detection system},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {676-684},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198638},
        abstract = {In modern IT infrastructures, a massive amount of log data is generated continuously from servers, applications, and network devices. Manual log monitoring becomes inefficient and error-prone due to the increasing scale and complexity of data. Traditional log analysis tools rely heavily on predefined rules and thresholds, which fail to detect unknown or evolving anomalies. This paper presents an Automated Log Analysis and Anomaly Detection System, a hybrid framework that integrates rule-based techniques with machine learning models to identify abnormal patterns in log data. The system performs log collection, preprocessing, feature extraction, and anomaly detection using algorithms such as Random Forest and statistical methods. The proposed system also includes a visualization dashboard that enables administrators to monitor system behavior and detect anomalies in real time. Experimental evaluation demonstrates that the system effectively identifies unusual activities such as system failures, unauthorized access attempts, and abnormal traffic patterns. The results highlight the importance of automation and intelligent analysis in improving system security and operational efficiency.},
        keywords = {Log Analysis, Anomaly Detection, Machine Learning, Isolation Forest, Real-time Monitoring, Web Application Security},
        month = {May},
        }