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@article{192412,
        author = {Amar Surushe and Vishal Ghanwat and Irfan Shaikh and Sakshi Patil and Gayatri Patange},
        title = {Mindguard: A Dynamic Alert System for Cyber Threat Mitigation Using Behavioral Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1190-1191},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192412},
        abstract = {Cybersecurity systems generate a massive number of alerts, many of which are false positives, leading to alert fatigue in Security Operations Centers (SOC). Traditional rule-based intrusion detection systems fail to adapt to evolving attack patterns. This paper proposes Mindguard, a dynamic alert system that leverages behavioral analysis and machine learning techniques to identify anomalous activities and prioritize alerts based on risk severity. By learning normal user and system behavior, the system detects deviations that indicate potential cyber threats. The proposed approach improves alert relevance, reduces false positives, and enhances incident response efficiency, making it suitable for modern cybersecurity environments.},
        keywords = {Cybersecurity, Behavioral Analysis, Anomaly Detection, Alert Fatigue, Artificial Intelligence},
        month = {February},
        }