| Title | Random Decision Forest approach for Mitigating SQL Injection Attacks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Aggarwal, Pranjal, Kumar, Akash, Michael, Kshitiz, Nemade, Jagrut, Sharma, Shubham, C, Pavan Kumar |
| Conference Name | 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) |
| Date Published | jul |
| Keywords | Communications technology, composability, Computational modeling, Conferences, Ensemble classifier, Forestry, Human Behavior, Metrics, pubcrawl, Random Decision Forest and SQL injection attacks, relational database security, relational databases, resilience, Resiliency, SQL Injection, Structured Query Language |
| Abstract | Structured Query Language (SQL) is extensively used for storing, manipulating and retrieving information in the relational database management system. Using SQL statements, attackers will try to gain unauthorized access to databases and launch attacks to modify/retrieve the stored data, such attacks are called as SQL injection attacks. Such SQL Injection (SQLi) attacks tops the list of web application security risks of all the times. Identifying and mitigating the potential SQL attack statements before their execution can prevent SQLi attacks. Various techniques are proposed in the literature to mitigate SQLi attacks. In this paper, a random decision forest approach is introduced to mitigate SQLi attacks. From the experimental results, we can infer that the proposed approach achieves a precision of 97% and an accuracy of 95%. |
| DOI | 10.1109/CONECCT52877.2021.9622689 |
| Citation Key | aggarwal_random_2021 |
Random Decision Forest approach for Mitigating SQL Injection Attacks