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@article{196279,
        author = {B Karthik and N Rahulsatwik and T Rahul and M Gopi and D Mamatha},
        title = {WAFinity - Infinite Protection, Intelligent Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2973-2981},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196279},
        abstract = {Web application attacks, including SQL injection, cross-site scripting (XSS), path traversal, and command injection, continue to represent a significant proportion of reported security incidents worldwide. Traditional web application firewalls (WAFs), which rely primarily on signature-based and rule-based mechanisms, are increasingly inadequate against polymorphic payloads and previously unseen attack patterns. This paper presents the design, implementation, and evaluation of a machine learning-based web application firewall capable of detecting malicious HTTP requests in real time. The proposed system incorporates a request parsing module, a feature extraction pipeline, and a classification engine trained on the CSIC 2010 HTTP dataset, with additional validation conducted using a subset of the CICIDS 2017 dataset. Three classifiers -- Random Forest, Support Vector Machine (SVM), and XGBoost -- are evaluated using accuracy, precision, recall, F1-score, and false positive rate. Random Forest achieved the highest overall F1-score of 0.974, with XGBoost performing comparably at 0.971. SVM demonstrated competitive precision but exhibited slightly elevated latency under high-throughput conditions. The results suggest that ensemble-based classifiers are well-suited for real-time web threat detection, and the proposed architecture represents a feasible approach for augmenting or replacing conventional signature-based WAF systems},
        keywords = {},
        month = {April},
        }