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<p>Web Applications (WAs) are becoming more vulnerable to attacks as they are more popular. Nevertheless, the conventional testing methodologies didn’t differentiate the Logical Flaws (LFs) and anomalies in WAs, thereby increasing the misclassification rate. Hence, in this paper, a novel black-box testing framework that incorporates an advanced technique called Gated Transformer Memorized transferred Recurrent Mishswish unit (GTMRM) is proposed for distinguishing between LFs and other vulnerabilities, thus enhancing the reliability of WAs. Initially, the user registration is carried out, followed by Hash-based Message Authentication Code Hash-based Message Authentication Code (HMAC) creation. Afterward, the registered users log into the application to request a Uniform Resource Locator (URL) for access. In the meantime, to authenticate the user, the HMAC verification is performed. Once the authentication is successful, the user is granted for accessing the functionalities. Thereafter, the black-box-centric LF and anomaly identification is done; here, the raw dataset is initially pre-processed. Subsequently, concerning a similar domain, the pre-processed data is clustered. Next, the features are extracted, followed by feature selection. Then, from the grouped data, the graph is constructed. The pattern labelling is carried out centered on the graph features. Lastly, the Logical Flaws (LF), anomaly, and legitimate access are proficiently classified by the proposed GTMRM. A compensation measure is applied in the case of a LF. After that, the data is securely stored in the cloud server with an accuracy of 99.14%.</p>

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An intelligent black-box testing model for isolating logical flaws and anomalies in applications using GTMRM

  • 期刊论文

  • 发表日期：

    2025年11月21日

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  • Adebanjo Falade Ambrose,
  • Gaurav Agarwal,
  • Akash Sanghi,
  • Sarika Panwar,
  • Kamal Upreti,
  • Rituraj Jain

摘要

Web Applications (WAs) are becoming more vulnerable to attacks as they are more popular. Nevertheless, the conventional testing methodologies didn’t differentiate the Logical Flaws (LFs) and anomalies in WAs, thereby increasing the misclassification rate. Hence, in this paper, a novel black-box testing framework that incorporates an advanced technique called Gated Transformer Memorized transferred Recurrent Mishswish unit (GTMRM) is proposed for distinguishing between LFs and other vulnerabilities, thus enhancing the reliability of WAs. Initially, the user registration is carried out, followed by Hash-based Message Authentication Code Hash-based Message Authentication Code (HMAC) creation. Afterward, the registered users log into the application to request a Uniform Resource Locator (URL) for access. In the meantime, to authenticate the user, the HMAC verification is performed. Once the authentication is successful, the user is granted for accessing the functionalities. Thereafter, the black-box-centric LF and anomaly identification is done; here, the raw dataset is initially pre-processed. Subsequently, concerning a similar domain, the pre-processed data is clustered. Next, the features are extracted, followed by feature selection. Then, from the grouped data, the graph is constructed. The pattern labelling is carried out centered on the graph features. Lastly, the Logical Flaws (LF), anomaly, and legitimate access are proficiently classified by the proposed GTMRM. A compensation measure is applied in the case of a LF. After that, the data is securely stored in the cloud server with an accuracy of 99.14%.