ItemArticle

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

Title
An intelligent black-box testing model for isolating logical flaws and anomalies in applications using GTMRM
Creator
Ambrose, Adebanjo Falade; Agarwal, Gaurav; Sanghi, Akash; Panwar, Sarika; Upreti, Kamal; Jain, Rituraj
Description
Web Applications (WAs) are becoming more vulnerable to attacks as they are more popular. Nevertheless, the conventional testing methodologies didnt 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%. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.
Source
International Journal of Information Technology (Singapore);
Date
01-01-2025
Publisher
Springer Science and Business Media B.V.
Subject
Automated black-box testing; Data privacy; Exponent belled fuzzy inference system; Logical flaws; Web security
Coverage
Ambrose A.F., Department of Computer Science Engineering, Invertis University, Bareilly, India; Agarwal G., Department of Computer Science Engineering, Invertis University, Bareilly, India; Sanghi A., Department of Computer Applications, Invertis University, Bareilly, India; Panwar S., Department of Electronics and Telecommunication Engineering, MIT Academy of Engineering, Pune, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 25112104;
Format
online
Language
English
Type
Article

Citation

Ambrose, Adebanjo Falade; Agarwal, Gaurav; Sanghi, Akash; Panwar, Sarika; Upreti, Kamal; Jain, Rituraj, “An intelligent black-box testing model for isolating logical flaws and anomalies in applications using GTMRM,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/22104.

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