Web Application Vulnerabilities Research Articles

WEB VULNERABILITIES DETECTION USING A HYBRID MODEL OF CNN, GRU AND ATTENTION MECHANISM

The frequency of cyber-attacks has been rising in recent years due to the fact that startup developers have failed to overlook security issues in the core web services. This stated serious concerns about the security of the web. Therefore, this paper proposes a hybrid model built on the base of Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU) and an attention mechanism to detect vulnerabilities in application code. Particularly, the model can help detect attacks based on Structured Query Language Injection (SQLi), Cross-Site Scripting (XSS), and command injection. When using the dataset SXCM1, our model achieved 99.77%, 99.66% and 99.63% for training, validation and testing, respectively. The results obtained on data from the DPU-WVD dataset are even better because it was 99.97%, 99.98% and 99.99% for training, validation and testing, respectively. These results significantly outperform the state-of-the-art models and can strongly identify vulnerabilities in web applications. Through training, on both the SXCM1 and DPU-WVD datasets, the model achieved an accuracy rate of 99.99%. The results show that this combination model is highly effective at recognizing three vulnerability categories and surpasses cutting-edge models that usually specialize in just one type of vulnerability detection.

UniEmbed: A Novel Approach to Detect XSS and SQL Injection Attacks Leveraging Multiple Feature Fusion with Machine Learning Techniques

Abstract Web applications are essential in the digital age, but their security vulnerabilities expose sensitive data and organizational integrity to sophisticated attacks. Among the most prevalent and damaging vulnerabilities in web applications are cross-site scripting (XSS) and SQL injection attacks. In this paper, we introduce UniEmbed, a unified approach for detecting XSS and SQL injection attacks using machine learning classifiers. This novel approach leverages natural language processing techniques, combining features from Word2Vec, the Universal Sentence Encoder (USE), and FastText to extract meaningful data from web applications. Extensive experiments were conducted using various machine learning classifiers on three benchmark datasets to evaluate the performance of the unified detection approach, demonstrating exceptional results. Experimental results demonstrate the superior performance of the MLP classifier. For the XSS attack dataset, the MLP classifier achieved an accuracy of 0.9982 and an F1-score of 0.9983, with minimal false positives and false negatives. Similarly, the hard voting classifier yielded the same outstanding results. For SQL injection attacks, the MLP classifier maintained exceptional performance, achieving an F1-score of 0.9980 and accuracy rates exceeding 0.9980 across two datasets. The classifier effectively minimized false positives and false negatives. The ROC curves further corroborate the effectiveness of the proposed method, indicating high true positive rates and low false positive rates. Furthermore, comparative analysis showed that the UniEmbed method consistently outperformed individual feature extraction methods across all classifiers. These findings indicate that the proposed UniEmbed method, particularly when combined with the MLP classifier, is highly effective in detecting both XSS and SQL injection attacks, making it a promising approach for enhancing web application security.

SQL-Injection Vulnerability Scanning Tool for Automatic Creation of SQL-Injection Attacks

This research introduces an advanced automated scanning tool for detecting and analyzing SQL injection vulnerabilities in web applications, addressing the critical need for robust security testing mechanisms in modern web development. The proposed tool employs sophisticated dynamic analysis techniques combined with machine learning algorithms to automatically generate, execute, and validate SQL injection attack vectors. By implementing a multilayered detection approach, the system first identifies potential injection points through comprehensive input parameter analysis, followed by intelligent payload generation based on database fingerprinting and contextual analysis. The tool incorporates both syntactic and semantic analysis of database responses to effectively distinguish between successful and failed injection attempts, significantly reducing false positives. Advanced features include automated bypass techniques for common defensive mechanisms, support for multiple database management systems (MySQL, PostgreSQL, Oracle, and MS-SQL), and intelligent error pattern recognition. Experimental evaluation conducted across 100 diverse web applications demonstrated a 95% detection rate for known vulnerabilities and an 85% success rate in identifying previously undiscovered SQL injection vulnerabilities. The tool's automated approach significantly reduces the time and expertise required for security testing, making it valuable for both security professionals and development teams implementing secure coding practices. Additionally, the system generates detailed vulnerability reports with remediation recommendations, facilitating efficient security patch implementation. Performance analysis shows that the tool can scan complex web applications with minimal impact on system resources while maintaining high accuracy in vulnerability detection.

GenSQLi: A Generative Artificial Intelligence Framework for Automatically Securing Web Application Firewalls Against Structured Query Language Injection Attacks

The widespread adoption of web services has heightened exposure to cybersecurity threats, particularly SQL injection (SQLi) attacks that target the database layers of web applications. Traditional Web Application Firewalls (WAFs) often fail to keep pace with evolving attack techniques, necessitating adaptive defense mechanisms. This paper introduces a novel generative AI framework designed to enhance SQLi mitigation by leveraging Large Language Models (LLMs). The framework achieves two primary objectives: (1) generating diverse and validated SQLi payloads using in-context learning, thereby minimizing hallucinations, and (2) automating defense mechanisms by testing these payloads against a vulnerable web application secured by a WAF, classifying bypassing attacks, and constructing effective WAF security rules through generative AI techniques. Experimental results using the GPT-4o LLM demonstrate the framework’s efficacy: 514 new SQLi payloads were generated, 92.5% of which were validated against a MySQL database and 89% of which successfully bypassed the ModSecurity WAF equipped with the latest OWASP Core Rule Set. By applying our automated rule-generation methodology, 99% of previously successful attacks were effectively blocked with only 23 new security rules. In contrast, Google Gemini-Pro achieved a lower bypass rate of 56.6%, underscoring performance variability across LLMs. Future work could extend the proposed framework to autonomously defend against other web attacks, including Cross-Site Scripting (XSS), session hijacking, and specific Distributed Denial-of-Service (DDoS) attacks.

Web-based Vulnerability Analysis and Detection

Background: In today’s digital world, web-based applications are widely used across organizations, making them a primary target for breaches, hacking attempts, data theft, and unauthorized access. Ensuring robust security through vulnerability analysis and detection is critical to mitigate emerging threats and prevent potential exploits. Objective: This research aims to develop and demonstrate a novel approach to web-based vulnerability assessment by combining advanced automated scanning tools with human expertise. The study introduces a modular, flexible, and efficient scanning tool capable of detecting critical vulnerabilities in web applications. Methods: The proposed tool is built using Python and Selenium and employs a modular architecture with regular expression-based detection techniques. The scanner identifies vulnerabilities such as SQL injection, cross-site scripting (XSS), file inclusions, command injection, and more. A ranking framework is introduced to prioritize fixes based on exploiting potential, severity, and patch availability. The tool's performance was evaluated on real-world web applications to assess its accuracy, detection speed, and effectiveness compared to traditional methods. Results: Experimental results highlight significant improvements in vulnerability detection accuracy and scanning speed. The tool successfully identified a broad range of vulnerabilities, including complex and dynamic ones, outperforming traditional scanning techniques. The modular design and automation enabled faster and more flexible vulnerability detection across diverse web platforms. Conclusion: This research provides a holistic and practical approach to web-based vulnerability assessment, combining advanced automated scanning with human analysis for optimal results. The introduced tool offers a reliable solution for detecting and prioritizing vulnerabilities, empowering organizations to enhance their cybersecurity defenses. By improving detection accuracy and efficiency, this study contributes to advancing web security practices and provides actionable insights for organizations navigating the evolving digital landscape.

A method for finding web application vulnerabilities using the ChatGPT API

This paper presents a method for automating web application testing using the ChatGPT API, designed to help ethical hackers identify vulnerabilities. The goal of the research is to develop an approach that improves the efficiency and accuracy of pentesting, focusing on the automation of processes that are traditionally performed manually. The proposed method is based on the capabilities of the GPT model to generate test requests and analyze server responses, which allows detecting potential vulnerabilities without the need for detailed analysis of the source code. The presented results demonstrate the advantages of using GPT models for generating complex test scenarios and analyzing web application responses, which helps identify potential threats. The results of the experiments showed an increase in the accuracy of vulnerability detection by 15-20% and a reduction in testing time by 35% compared to traditional methods. The proposed approach is promising for implementation in the practice of ethical hacking and cyber security.

Identifying and Mitigating Web Application Vulnerabilities: A Comparative Study of Countermeasures and Tools

In the current age of technology, web applications and websites have experienced significant growth. This expansion has made their security a critical area of research. Web applications offer benefits, which makes user’s lives easier. In this paper, common web application vulnerabilities and effective strategies to mitigate the vulnerabilities are identified using a comparative study of countermeasures and open-source web application vulnerability assessment tools. Specifically, the top ten web application vulnerabilities and their countermeasures are investigated. Accordingly, several open-source vulnerability assessment tools are also introduced. The review highlights that with the developments and deployments of web applications on the internet, users are chased by a remarkable number of cyber-attacks. Attackers take advantage of available vulnerabilities in a web application or website, such as SQL injections, cross-site scripting, and broken authentications. This paper concludes by providing the best practices to mitigate cyber-attacks on web applications and suggests future directions for enhancing vulnerability assessment through machine learning techniques

Comparative Analysis of Machine Learning Algorithms for Cross-Site Scripting (XSS) Attack Detection

Cross-Site Scripting (XSS) attacks pose a significant cybersecurity threat by exploiting vulnerabilities in web applications to inject malicious scripts, enabling unauthorized access and execution of malicious code. Traditional XSS detection systems often struggle to identify increasingly complex XSS payloads. To address this issue, this research evaluated the efficacy of Machine Learning algorithms in detecting XSS threats within online web applications. The study conducts a comprehensive comparative analysis of XSS attack detection using four prominent Machine Learning algorithms, which consist of Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). This research utilizes a comparative methodology to assess the selected Machine Learning algorithms by analyzing their performance metrics, including confusion matrix, 10-fold cross-validation, and assessment of training time to thoroughly evaluate the models. By exploring dataset characteristics and evaluating the performance metrics of each selected algorithm, the study determined the most robust Machine Learning solution for XSS detection. Results indicate that Random Forest is the top performer, achieving 99.93% accuracy and balanced metrics across all criteria evaluated. These findings will significantly enhance web application security by providing reliable defenses against evolving XSS threats.

Identification of SQL Injection Security Vulnerabilities in Web applications Based on Binary Code Similarity

The existing SQL injection security vulnerability identification technology for Web applications has inherent flaws, which are relatively passive in defense methods, and cannot deal with increasingly changeable attack methods. In order to improve the accuracy of SQL injection security vulnerability identification of Web applications, this paper uses an improved skip-gram model to realize unsupervised learning of the embedding process, converts the information related to program functions contained in the vertices of the basic block into feature vectors to obtain the ACFG vector of the basic block, and measures the similarity of binary functions by evaluating the similarity of feature vectors. The experimental results show that the technical processing route proposed in this paper can effectively compare binary functions with different architectures and optimization levels, and use the advantages of neural networks to obtain higher accuracy and better analysis efficiency, thereby effectively improving the identification effect of SQL injection security vulnerabilities in Web applications. Therefore, it can play a certain role in the security management of subsequent Web applications.

Perbandingan Evaluasi Kerentanan Menggunakan Tenable Nessus Scanner dan Owasp Zed Attack Proxy untuk Meningkatkan Keamanan Sistem Informasi Kepegawaian di Universitas Merdeka Malang

This study aims to compare the vulnerability analysis between Tenable Nessus Scanner and OWASP Zed Attack Proxy (ZAP) for improving the security of the Human Resource Information System (HRIS) website at Universitas Merdeka Malang. The research methodology includes the use of both Nessus and OWASP ZAP tools to scan the HRIS website for potential vulnerabilities. The findings of this research indicate that OWASP ZAP identified several critical web application vulnerabilities such as the absence of Anti-CSRF tokens, lack of Content Security Policy (CSP) headers, and missing Anti-Clickjacking headers, which are essential for maintaining the security and integrity of user data. On the other hand, Nessus Scanner focused more on network and server infrastructure vulnerabilities. The results suggest that OWASP ZAP is more effective for web application security in this context. Recommendations are provided to address the identified vulnerabilities and enhance the overall security of the HRIS website.

OWASP A03 Injection Vulnerability in Web Application Development

Web applications are crucial for businesses and individuals by providing efficient communication, collaboration, and access to services and information via browsers, boosting connectedness, productivity, and creativity in the digital era. Insecure web applications pose risks of data breaches, malware, and unauthorized access which jeopardize user privacy, trust, and organizational security. Web developers must be knowledgeable and prepared to deal with common vulnerabilities in web applications. A prototype web application (https://webriska3.tech) with lesson and editor module is developed to train web developers on the Open Web Application Security Project (OWASP) Top Ten security risks, focusing on A03 - Injection vulnerability. OWASP A03 Injection vulnerability is one of the most common vulnerabilities that is at the heart of any database-driven web applications. Evaluation on the prototype in improvement knowledge on A03 – Injection vulnerability, testers are recruited to complete two coding tasks in laboratory environment. 80% of testers mastered Output escaping/encoding defensive technique while Prepared statement/Parameterized Query defensive technique is the hardest to master. The prototype obtained average System Usability Scale (SUS) score of 57 that is below average, indicating issues with the prototype interface. This work showed promising results of increase understanding on A03 Injection vulnerability and implementation skills to protect web application against attack and exploitations.

SQL injection attack: Detection, prioritization & prevention

Web applications have become central in the digital landscape, providing users instant access to information and allowing businesses to expand their reach. Injection attacks, such as SQL injection (SQLi), are prominent attacks on web applications, given that most web applications integrate a database system. While there have been solutions proposed in the literature for SQLi attack detection using learning-based frameworks, the problem is often formulated as a binary, single-attack vector problem without considering the prioritization and prevention component of the attack. In this work, we propose a holistic solution, SQLR34P3R, that formulates the SQLi attack as a multi-class, multi-attack vector, prioritization, and prevention problem. For attack detection and classification, we gathered 457,233 samples of benign and malicious network traffic, as well as 70,023 samples that had SQLi and benign payloads. After evaluating several machine-learning-based algorithms, the hybrid CNN-LSTM models achieve an average F1-Score of 97% in web and network traffic filtering. Furthermore, by using CVEs of SQLi vulnerabilities, SQLR34P3R incorporates a novel risk analysis approach which reduces additional effort while maintaining reasonable coverage to assist businesses in allocating resources effectively by focusing on patching vulnerabilities with high exploitability. We also present an in-the-wild evaluation of the proposed solution by integrating SQLR34P3R into the pipeline of known vulnerable web applications such as Damn Vulnerable Web Application (DVWA) and Vulnado and via network traffic captured using Wireshark from SQLi DNS exfiltration conducted with SQLMap for real-time detection. Finally, we provide a comparative analysis with state-of-the-art SQLi attack detection and risk ratings solutions.

Comprehensive review of machine learning models for sql injection detection in e-commerce

With the steady expansion of online commerce, e-commerce sites have become increasingly attractive targets for hackers. These sites serve millions of customers and often hold valuable, confidential, and sometimes financial information in their databases. One particularly dangerous type of attack is SQL injection, which exploits vulnerabilities in web applications to influence backend databases, posing significant threats to such platforms. Traditional defenses like desktop firewalls, input validation, and parameterized queries provide some level of protection but are often insufficient against newer injection variations and sophisticated attackers. The utilization of machine learning to enhance cybersecurity against more advanced threats has been demonstrated as a promising approach. This systematic review examines how various machine learning algorithms are applied to detect SQL injection attacks that could potentially harm e-commerce systems. By identifying and analyzing the relevant literature, this review highlights the effectiveness of different algorithms and their practical applications in enhancing the security of online commerce platforms. More specifically, five techniques were assessed on both real and synthetic datasets: Logistic Regression, Naive Bayes, Random Forest, Artificial Neural Network, and two combined models (Logistic Regression & Naive Bayes, and Artificial Neural Network & Random Forest). The findings indicate that Random Forest performed better than other algorithms in the decision tree family, attributed to its ability to balance precision and recall effectively. However, limitations such as using a single dataset and the computational complexity of some models were noted. This review provides insights for practitioners on selecting appropriate detection models and outlines approaches to address current limitations through future work. Addressing these limitations could involve using more diverse datasets, optimizing computational efficiency, and exploring advanced ensemble methods and neural network architectures.

Finding Vulnerability in Web Application by using Pentesting

Penetration testing, a proactive security measure, is indispensable in fortifying the resilience of web applications against cyber threats. This project explores the efficacy of penetration testing as a means to identify and mitigate vulnerabilities within web applications. By simulating real-world attacks, penetration testing uncovers potential weaknesses in the application’s architecture, codebase, and configurations. Through a systematic approach encompassing reconnaissance, vulnerability analysis, exploitation, and reporting, penetration testing provides invaluable insights for organizations to patch vulnerabilities and bolster their overall security posture. The abstract briefly describes the methodology involved in penetration testing and highlights its significance in mitigating risks and fortifying the security posture of web applications. This project examined further into the effectiveness of penetration testing as a preventative security strategy, uncovering vulnerabilities such as SQL injection and cross-site scripting. The investigation found subtle vulnerabilities in the online application’s architecture, programming, and customizations by painstakingly modeling actual cyberattacks. This thorough investigation gave firms a solid platform on which to strengthen their defenses against changing cyberthreats. The study also demonstrated how crucial penetration testing is for both finding vulnerabilities and enabling quick repair measures. Through thorough reporting, exploitation simulations, vulnerability analysis, and in-depth reconnaissance, the study demonstrated how penetration testing provides useful insights to improve online applications’ overall security posture. In the end, this project emphasizes how crucial penetration testing is to improving the robustness of web applications

Website Penetration Analysis Against XSS Attacks using Payload Method

This research aims to analyze the effectiveness of various penetration testing methods in identifying and mitigating XSS (Cross-Site Scripting) vulnerabilities in web applications. XSS is a type of web security attack that takes advantage of weaknesses in web applications to insert malicious code into web pages displayed to users. This attack can steal user data, take over user sessions, or spread malware. This research uses a penetration testing method with a black-box approach, where the researcher does not know the construction of the system being tested. Tests were conducted on 10 random websites, including 5 open-source websites and 5 commercial websites. The test results show that the payload method used is effective in exploiting XSS vulnerabilities on some websites. Of the 10 websites tested, 6 of them were successfully exploited using different payload methods. This research highlights the importance of using open-source penetration testing tools in detecting and addressing security vulnerabilities in web applications. These tools are easy to implement, supported by extensive documentation, and have a strong community. This research also emphasizes the importance of a deep understanding of how penetration testing tools work to identify and address security vulnerabilities. To address XSS vulnerabilities, this research recommends good programming techniques such as programming language updates, use of OOP (Object-Oriented Programming), MVC (Model-View-Controller) concepts, and use of frameworks. Further research can be done to develop and test new payload methods, explore the use of other penetration testing tools, and test security vulnerabilities in other types of web applications.

Applied Machine Learning Predictive Analytics to SQL Injection Attack Detection and Prevention

Abstract: The back-end database is pivotal to the storage of the massive size of big data Internet exchanges stemming from cloud-hosted web applications to Internet of Things (IoT) smart devices. Structured Query Language (SQL) Injection Attack (SQLIA) remains an intruder’s exploit of choice on vulnerable web applications to pilfer confidential data from the database with potentially damaging consequences. The existing solutions of mostly signature approaches were all before the recent challenges of big data mining and at such lacks the functionality and ability to cope with new signatures concealed in web requests. An alternative Machine Learning (ML) predictive analytics provides a functional and scalable mining to big data in detection and prevention of SQLIA. Unfortunately, lack of availability of readymade robust corpus or data set with patterns and historical data items to train a classifier are issues well known in SQLIA research. In this project, we explore the generation of data set containing extraction from known attack patterns including SQL tokens and symbols present at injection points. Also, as a test case, we build a web application that expects dictionary word list as vector variables to demonstrate massive quantities of learning data. The data set is pre-processed, labelled and feature hashing for supervised learning. The trained classifier to be deployed as a web service that is consumed in a custom dot NET application implementing a web proxy Application Programming Interface (API) to intercept and accurately predict SQLIA in web requests thereby preventing malicious web requests from reaching the protected back-end database. This project demonstrates a full proof of concept implementation of an ML predictive analytics and deployment of resultant web service that accurately predicts and prevents SQLIA with empirical evaluations presented in Confusion Matrix (CM) and Receiver Operating Curve (ROC).

An Experimental Study on Detecting and Mitigating Vulnerabilities in Web Applications

An Experimental Study on Detecting and Mitigating Vulnerabilities in Web Applications

An efficient artificial intelligence approach for early detection of cross-site scripting attacks

Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim’s browser. The consequences of XSS attacks can be severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications.

Sniping at web applications to discover input-handling vulnerabilities

Web applications play a crucial role in modern businesses, offering various services and often exposing sensitive data that can be enticing to attackers. As a result, there is a growing interest in finding innovative approaches for discovering vulnerabilities in web applications. In the evolving landscape of web security, the realm of fuzz testing has garnered substantial attention for its effectiveness in identifying vulnerabilities. However, existing literature has often underemphasized the nuances of web-centric fuzzing methodologies. This article presents a comprehensive exploration of fuzzing techniques specifically tailored to web applications, addressing the gap in the current research. Our work presents a holistic perspective on web-centric fuzzing, introduces a modular architecture that improves fuzzing effectiveness, demonstrates the reusability of certain fuzzing steps, and offers an open-source software package for the broader security community. By addressing these key contributions, we aim to facilitate advancements in web application security, empower researchers to explore new fuzzing techniques, and ultimately enhance the overall cybersecurity landscape.

WebSec : Exploring and Modulating Vulnerabilities

This research investigates the performance of the OWASP Zed Attack Proxy (OWASP ZAP) and Paros open-source vulnerability scanners on the Damn Vulnerable Web Application (DVWA). By evaluating their capability to identify vulnerabilities, along with assessing their user-friendliness and features, the study highlights each scanner's strengths and weaknesses. The insights aim to assist developers and security professionals in selecting the most effective tools for improving the security posture of web applications. Keywords—Web Application Security, Vulnerability Scanners, OWASP ZAP, Paros, Damn Vulnerable Web Application (DVWA), Open Source Tools, Cybersecurity, Penetration Testing.