Random Decision Forest approach for Mitigating SQL Injection Attacks

Countermeasure to Structured Query Language Injection Attack for Web Applications using Hybrid Logistic Regression Technique

“…The precision score of 0.99 was achieved for the developed model in this research and the research by [27], however, the precision that determine the exactness of the model developed in this research for the detection and prevention of SQLiA in DBMS environment outperformed that of [27] as well as [28] with a score of 0.99782 superseding 0.993 and 0.97 for [27] and [29] respectively. The recall performance score recorded in this research which entails the measure of the completeness of the performance of detection of SQLiA achieved 0.99781, against that of [27], F1-score of 0.99781 with a significant difference against [23] as well as [27], though, [27] score 0.9934 that is slightly above 0.99, [29] had the worst performance score of 0.989, the robustness of the developed detection and prevent model for SQLiA in DBMS environment have established its optimality capability across all performance metrics relevant in the field of this research area. Though specificity and FPR was not recorded for the baseline journal model, this research used the performance metrics based on the fact that they are being employed for analysis engaging detection-based machine learning models, the developed detection and prevention model achieved the scores of 0.99409 and 0.00591 for specificity and FPR respectively, showing the efficient capability in detection of SQLiA in DBMS environment…”

“…In addition, deducing from Table 5, accuracy record score achieved is the must employed performance evaluation metric in the field of SQLiA detection involving machine learning based models, this is based on the fact that it is most common in the baseline reviewed literatures. The developed model for detection and prevention of SQLiA achieved an optimal accuracy of 0,99781, which is followed by the technique employed by [27], [28] and [29] with a distinct wide margin in accuracy of 0.9934, 0.98 and 0.95 respectively while [29] have the least accuracy record of 0.95. The significant performance recorded by this research reflects how correctly the developed model can detect and prevent SQLiAs in DBMS environment, a low FPR of 0.00591 was recorded in the developed model, although baseline literature reviewed in this research did not capture FPR, FPR is an important evaluation metric in detection of attacks in computing environment as it is recorded for literatures earlier than 2021 articles reviewed.…”

“…It was claimed that the proposed model achieved the following optimal performance for accuracy, F-score, and AUC of 98%, 0.989, 0.999 respectively, however, with an optimised model a more enhanced performance can be achieved when compared to contemporary techniques of model optimisation and dataset enhancement. A Random Forest-based SQLi attack detection approach was introduced in [29] to address the challenge of detection efficiency for SQLi attack detection, the performance score achieved in the experiment was 97% and 95% for precision and accuracy respectively, however, contemporary research has it that more effective means of detection of malware attack exist that can offer a robust performance result.…”

“…Although, these solutions have contributed immensely towards providing an understanding on how SQLIAs attacks do occur and addressed, however despites these giant leaps, an ideal solution is far from been achieved. With the negative impact of SQLIAs, several researchers in [23][24][25][26][27][28][29] have tried to address concerns arising from such attacks through provision of techniques to serve as potential solutions. Despites the giant strides made in the detection and prevention of SQLIAs, the following limitations still exist:…”