<![CDATA[The security of web applications is becoming increasingly crucial with the growing use of web platforms in education and business, especially due to the management of sensitive data. Attacks such as SQL Injection often pose serious threats to data integrity by exploiting weaknesses in input validation. Signature-based approaches are employed to detect known attacks, but they are often ineffective against new threats. On the other hand, anomaly-based approaches using Machine Learning can identify anomalous patterns but are typically slow for real-time detection. This study implements PSO-SVM (Particle Swarm Optimization-Support Vector Machine) to enhance the detection of attacks on web applications by combining anomaly and signature-based approaches. PSO is utilized to optimize SVM parameters, aiming to improve the accuracy of detecting new attacks and reduce the number of undetected threats. Evaluation through testing scenarios demonstrated an accuracy improvement of up to 99.3%, confirming that this hybrid approach is effective in enhancing the security of web applications.]]>
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