SQL Injection, Cross-site scripting and Buffer Overflow attacks detection using Machine Learning

The frequency and severity of web application attacks are increasing nowadays at an alarming rate. The abundance of electronic services on the internet enables cybercriminals to initiate novel attacks. Structured query language injection, cross-site scripting and buffer overflow are some examples of web attacks that raise a major concern. Numerous studies have been done to find measures to reduce the impact of these attacks, either by stopping them in their early stage or identifying them as they happen. In this paper, we investigate the aforementioned attacks and formulate a mechanism to predict these attacks by classifying them as malicious or benign. Accordingly, an adequate mitigation could be timely triggered ahead of the incident. Moreover, we implement and evaluate different machine learning-based techniques to proactively identify such attacks. Finally, we discuss the performance of the models, provide our recommendations and share our lessons-learned.