| Title |
Identifying SQL Injection Risks Using the SMO Algorithm |
| Abstract |
The rapid expansion of online applications and services has heightened concerns about potential
cyberattacks. SQL injection, a prevalent attack method, exploits vulnerabilities in online applications
to gain unauthorized access to databases. Ensuring the security and integrity of online systems requires
effective identification and prevention of SQL injection attacks. This study introduces a novel approach
for detecting SQL injection attacks in network traffic data using the Sequential Minimal Optimization
(SMO) algorithm. By leveraging machine learning, this research addresses the urgent need for efficient
and accurate detection methods. The focus is on applying the SMO technique to identify and counter
SQL injection threats through analysis of network traffic data. Network flow data, which captures
interactions between hosts, offers valuable insights for detecting unusual patterns indicative of potential
attacks |
| Keywords |
Network, SQL injection, Attack, Sequential minimal optimization algorithm, Defense Mechanism. |
| Reserch Area |
Engineering |
| Reserch Paper |
AIJFR2404003 - V2 I4 - 21-26.pdf |
| Author(s) |
N. Dhansukh Rao, D. Lalitha, D. P. Laxman |
| Country |
India |
