Hossain, Muhammad IqbalAlam, AunindaTahreen, MarjanAlam, Md MoinMohammad, Shahnewaz AliRana, Shohag2021-10-182021-10-1820212021-01ID 19241021ID 19241020ID 17101060ID 19241014ID 20141033http://hdl.handle.net/10361/15331Cataloged from PDF version of thesis.Includes bibliographical references (pages 21-22).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.Importance of cyber-security in protecting our valuable data and information is huge in this era of technology. Since numerous amounts of cyber-attacks take place every day, the development of a more secured system so that it can predict and stop cyber-attacks from happening, has been our concern for years. This research paper is focused on developing such a means that will be able to detect and prevent SQL Injection Attack successfully. SQL Injection attack is a type of cyber-attack that uses malicious SQL queries for internal data manipulation and retrieving hidden information from the back-end database that were not intended to be displayed. SQL Injection Attack even makes a database vulnerable to other kinds of attacks. Since most of the organizations use a SQL based back end database to store data, all of their data is exposed to a simple form of attack if they are not properly defended. The aim of this research is to develop a model by finding out the best machine learning algorithm to predict and prevent SQL Injection Attack. A brief explanation of our work plan, our experimentation and the results of our experiments are discussed in this paper.22 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Machine LearningSQL InjectionSCAMMNaive BayesKNNNeural Network ClassifierRandom ForestLogistic RegressionMachine learningThesis