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@article{195012,
        author = {ASAN NAINAR M and J.ARUN},
        title = {Integrated Malicious Attack Detection System Machine Learning Approach for Cyber Threat},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6362-6368},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195012},
        abstract = {Cyberthreats like phishing URLs, malicious SMS messages, and deceptive QR code payloads have become much more common due to the quick growth of digital communication platforms. These attacks steal confidential data by taking advantage of user trust and system flaws, resulting in financial loss and data breaches. Intelligent detection mechanisms are crucial because traditional rule-based security systems frequently miss newly evolving attack patterns. This study suggests a machine learning-based malicious attack detection system that can instantly recognize and evaluate dubious digital content. The suggested system combines a web-based user interface with a backend detection engine that can scan URLs, decode payloads from QR codes, and examine SMS messages for signs of phishing. The structural and lexical features of URLs and message content, such as domain irregularities, suspicious keywords, and redirection patterns, are captured using sophisticated feature extraction techniques. High detection accuracy is achieved by classifying inputs as either malicious or legitimate using a trained Random Forest classification model.},
        keywords = {},
        month = {March},
        }