Identifying SQL Injection Vulnerabilities Using SMO Algorithm

Author(s)	B Suganya, E C Dhasna, K S Sagli
Country	India
Abstract	The proliferation of online applications and services has sparked concerns regarding cybersecurity threats. Among these, SQL injection stands out as a prevalent attack vector exploiting vulnerabilities in online applications to gain unauthorized access to databases. Safeguarding the integrity and security of online systems hinges on the ability to detect and prevent SQL injection attacks. This research employs the Sequential Minimal Optimization (SMO) algorithm to introduce a novel approach for identifying SQL injection attacks in network traffic data. The study proposes a unique methodology that utilizes machine learning to address the critical need for efficient and effective detection methods. Specifically, the research focuses on leveraging the SMO technique to detect and mitigate SQL injection threats using network traffic data. Analyzing the sequence of interactions between hosts, known as network flow data, provides valuable insights into detecting anomalous patterns indicative of potential attack activities.
Keywords	Arduino Uno, Insulated Gate Bipolar Transistor (IGBT), Transformers, Alternating Current (AC), Direct Current (DC), Proteus simulation
Field	Engineering
Published In	Volume 3, Issue 2, March-April 2022
Published On	2022-03-13

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Advanced International Journal for Research