Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 1459-1466· 0 citations· 16 references
Abstract
Automated Web Application Testing plays a vital role in identifying vulnerabilities, analyzing attacks, and generating cybersecurity documentation for modern web environments. Artificial Intelligence (AI) significantly enhances web application testing performance by enabling adaptive threat detection, intelligent payload verification, anomaly-based response evaluation, and automated threat documentation. These AI-driven capabilities reduce manual effort while improving the accuracy, scalability, and reliability of vulnerability assessment and security reporting. However, existing automated web testing frameworks suffer from fragmented workflows, lack of contextual awareness, inefficient vulnerability prioritization, excessive false positive generation, and limited adaptive scanning capabilities. The framework integrates adaptive reconnaissance, vulnerability analysis, and automated documentation within a unified security assessment workflow. The framework introduces the Adaptive Reconnaissance Intelligence Engine (ARIE) to intelligently discover attack surfaces and perform dynamic reconnaissance analysis. A Secure Threat Enumeration Mechanism (STEM) is designed to conduct automated multilayer vulnerability scanning and threat correlation across web components. Furthermore, the Deep Response Payload Analyzer (DRPA) utilizes behavioral response correlation intelligence to detect SQL Injection and Cross-Site Scripting (XSS) attacks through contextual payload-response analysis. In addition, the Cognitive Risk Documentation Framework (CRDF) automates vulnerability severity assessment, exploit validation, and structured cybersecurity report generation for efficient threat documentation. The proposed modular workflow enables a systematic transition from reconnaissance to vulnerability validation and automated risk documentation. Experimental evaluation demonstrates that the proposed framework achieves accurate vulnerability detection, reliable adaptive scanning, automated attack validation, and reduced false positive rates. The system provides an intelligent and efficient cybersecurity evaluation and documentation support framework for modern web application environments.
HawkEye is introduced, a modular, web-based vulnerability auditing platform designed to streamline security analysis by integrating multiple scanning tools within a unified dashboard and illustrates how consolidated reporting improves vulnerability prioritization for development teams.
D. R. Patil, Varad Salgare, Devaj Arya et al.· International Journal for Re...· 0 citations
Experimental results show that AST-based structural features substantially improve recall compared with the TF-IDF baseline, while the combined TF-IDF and AST representation maintains this improved performance.
Vani Pasupula, Manne Naga VJ Manikanth, Nagaraju Vassey· International Journal of Cre...· 0 citations
The importance of Web Application Security grows daily as more organizations are threatened and attacked by cyber criminals. With the growing threat from cyber criminals, performing security testing to identify vulnerabilities in web systems is critical. Of all security testing techniques, fuzz testing is perhaps the best technique available today. Fuzz testing involves injecting input into a target application, including malformed, unexpected, or random data to see how it reacts when it receives bad data. In the case of web applications, fuzz testing is done by sending numerous HTTP requests (each request contains different forms of crafted or invalid data) to a web server to measure the response generated by the server.
This study will create an Automated Web Application Fuzzer which will be integrated with Jenkins so that continuous security testing of Web Applications can occur. Test cases were created using known security vulnerabilities within web applications. Testing revealed that the automation tool found vulnerabilities in thirteen (13) out of fifteen (15) test cases. Therefore, testing reveals that the majority of web vulnerabilities can be easily identified simply by reviewing the content of HTTP responses, thereby validating the effectiveness of the proposed automated web application fuzzing methodology
Pooja Dahiya, Shalini Bhadola, Kavita Rathi· International journal of res...· 0 citations
Web applications are ubiquitous and serve various business and technical functions. As the number of web applications continues to increase, they are also becoming one of the most common targets for cyber-attacks. This paper proposes a web application penetration testing framework that helps identify, classify, and mitigate security vulnerabilities in web applications. The proposed framework is based on the OWASP Top 10 and leverages several security tools to detect vulnerabilities with higher accuracy and a minimized number of false-positive outcomes. The proposed framework classifies detected vulnerabilities based on their categories and assigns a priority level to the detected vulnerabilities based on their risk assessment. Mitigation recommendations are also provided along with a complete security report that helps developers in strengthening the application security. The effectiveness of the proposed framework is evaluated using a number of web applications including DVWA and OWASP Juice Shop.
Akbar Sk., Bhasakra Venkat Sai Devarapalli, Bushra Md.· International Journal of Com...· 0 citations
It is concluded that web application security requires continuous assessment and proactive security practices throughout the software development lifecycle, and adopting OWASP guidelines and implementing effective security controls can significantly enhance the protection and resilience of modern web applications.
S. Banu, H. Shanmatha, Mehdi Gheisari et al.· BOHR International Journal o...· 0 citations
Web applications have become an essential part of modern businesses, making them a common target for cyberattacks. Among various security threats, SQL injection remains one of the most dangerous because it allows attackers to manipulate database queries and gain unauthorized access to sensitive information. Traditional detection techniques often rely on predefined rules or signatures, which are less effective against newly emerging attack patterns. This work presents an intelligent approach for improving web application security through the prediction and detection of SQL injection attacks using machine learning techniques. The proposed framework processes SQL queries by performing data cleaning, feature extraction, and text preprocessing before training an ensemble classification model. The trained model distinguishes normal queries from malicious ones with high accuracy, enabling early identification of potential attacks. Performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score to verify the effectiveness of the system. The developed framework also provides a simple interface for analyzing new SQL queries and predicting their security status. This approach supports proactive protection of web applications by enabling faster, more reliable, and automated detection of SQL injection vulnerabilities.
D. Aravind Reddy, G.Rajini· International Journal of Eng...· 0 citations