<![CDATA[SQL Injection (SQLi) attacks are a major threat to web application security, with attackers frequently attempting to modify query patterns repeatedly to evade detection. This research aims to develop a method for detecting repeated SQL Injection attacks in Apache logs using a semantic embedding approach. The method employed includes preprocessing log data using Natural Language Processing (NLP) techniques, generating vector representations through semantic embedding, and classifying using machine learning algorithms. The novelty of this research lies in the application of semantic embedding on Apache log queries to identify recurring and polymorphic SQL Injection attacks based on contextual similarity rather than exact syntactic matching. Experimental results show that the Support Vector Machine (SVM) model achieved the best performance with an accuracy of 94.5%, precision of 92.0%, recall of 91.0%, and F1-score of 91.5%, outperforming conventional rule-based detection methods.. The results show that the proposed approach is capable of recognizing semantic similarities between queries despite syntactic differences, thereby improving detection accuracy and reducing false positive rates compared to rule-based methods. Therefore, this method is effective for large-scale log analysis and is capable of detecting repeated and polymorphic SQL Injection attack pattern in web application environments.]]>
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