| Title | Enhanced Automated-Scripting Method for Improved Management of SQL Injection Penetration Tests on a Large Scale |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Abdul Raman, Razman Hakim |
| Conference Name | 2019 IEEE 9th Symposium on Computer Applications Industrial Electronics (ISCAIE) |
| Keywords | Collaboration, enhanced automated-scripting method, Human Behavior, large-scale SQL injections, Metrics, penetration tests, policy-based governance, privacy, program testing, pubcrawl, resilience, Resiliency, security of data, SQL, SQL detection, SQL Injection, SQL injection penetration tests, SQL injection test, SQLi penetration tests, SQLiAutoScript method |
| Abstract | Typically, in an assessment project for a web application or database with a large scale and scope, tasks required to be performed by a security analyst are such as SQL injection and penetration testing. To carry out these large-scale tasks, the analyst will have to perform 100 or more SQLi penetration tests on one or more target. This makes the process much more complex and much harder to implement. This paper attempts to compare large-scale SQL injections performed with Manual Methods, which is the benchmark, and the proposed SQLiAutoScript Method. The SQLiAutoScript method uses sqlmap as a tool, in combination with sqlmap scripting and logging features, to facilitate a more effective and manageable approach within a large scale of hundreds or thousands of SQL injection penetration tests. Comparison of the test results for both Manual and SQLiAutoScript approaches and their benefits is included in the comparative analysis. The tests were performed over a scope of 24 SQL injection (SQLi) tests that comprises over 100,000 HTTP requests and injections, and within a total testing run-time period of about 50 hours. The scope of testing also covers both SQLiAutoScript and Manual methods. In the SQLiAutoScript method, each SQL injection test has its own sub-folder and files for data such as results (output), progress (traffic logs) and logging. In this way across all SQLi tests, the results, data and details related to SQLi tests are logged, available, traceable, accurate and not missed out. Available and traceable data also facilitates traceability of failed SQLi tests, and higher recovery and reruns of failed SQLi tests to maximize increased attack surface upon the target. |
| DOI | 10.1109/ISCAIE.2019.8743936 |
| Citation Key | abdul_raman_enhanced_2019 |
Enhanced Automated-Scripting Method for Improved Management of SQL Injection Penetration Tests on a Large Scale