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Research Paper Details

Title Identifying SQL Injection Vulnerabilities Using SMO Algorithm
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
Reserch Area Engineering
Reserch Paper AIJFR2402003 - V2 I2 17-23.pdf
Author(s) B Suganya, E C Dhasna, K S Sagli
Country India