Web Application Vulnerabilities Research Articles

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.

A Proposed Framework of VAPT Services in Web Application Deployed on Infrastructure as a Service (Iaas)

Most companies in Malaysia require their employees to work from home due to the COVID-19 pandemic. This situation also increased the number of data generated from various sources, thus exposing them to different security risks. Even though the employees are encouraged to work from home because of the COVID-19 pandemic, they still need to communicate among themselves to do their work. However, working from home depends mainly on cloud computing (CC) applications that help employees accomplish their daily work efficiently. Injection attacks, such as SQL injection and Cross-Site Scripting (XSS), are critical security vulnerabilities that can lead to unauthorized access, data breaches, and potential service disruptions in web applications. With the increasing adoption of cloud computing, web applications deployed on cloud platforms like Amazon Web Services (AWS) are becoming more prevalent and vulnerable to such attacks. Therefore, it is crucial to develop practical Vulnerability Assessment and Penetration Testing (VAPT) techniques specifically tailored to identify and detect injection vulnerabilities in web applications deployed on AWS. However, existing VAPT methodologies often need more comprehensive coverage for injection vulnerabilities in cloud-based web applications, and they may not consider the unique characteristics and challenges associated with the AWS environment. This research addresses this gap by proposing an enhanced VAPT framework focusing specifically on injection attacks in web applications deployed on AWS.

Machine Learning for Web Vulnerability Detection: The Case of Cross-Site Request Forgery

Cross-site request forgery (CSRF) vulnerabilities pose a significant threat to web application security, enabling attackers to execute unauthorized actions on behalf of authenticated users. Conventional CSRF detection methods, such as manual code review and static analysis, are often time-consuming, error-prone, and inefficient. Proposes Mitch, a novel machine learning (ML)-based solution for the black-box detection of CSRF vulnerabilities. Mitch employs supervised learning, trained on a comprehensive dataset of HTTP requests and responses, to effectively identify security-sensitive HTTP requests and uncover CSRF vulnerabilities within them. Rigorous evaluations on a diverse set of real-world web applications demonstrate Mitch's remarkable ability to detect CSRF vulnerabilities with high accuracy, outperforming traditional methods. Mitch's automated nature eliminates the need for manual code review and static analysis, saving time and effort while reducing the risk of human error. Additionally, Mitch's scalability allows seamless integration into continuous integration and continuous delivery (CI/CD) pipelines, enabling continuous security monitoring and vulnerability detection. Mitch's efficacy extends beyond detecting known CSRF vulnerabilities. Its ability to identify patterns and relationships enables it to uncover obscure CSRF vulnerabilities that may have been overlooked by traditional methods, including zero-day vulnerabilities. In conclusion, Mitch emerges as a powerful tool for enhancing web application security, offering a comprehensive and automated solution for detecting CSRF vulnerabilities. Its ability to handle complex web applications, uncover hidden CSRF vulnerabilities, and integrate into CI/CD pipelines makes it an indispensable tool for web security professionals. Mitch's adoption has the potential to significantly reduce the risk of CSRF attacks and safeguard sensitive user data. We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software. In this project, we propose a methodology to leverage Machine Learning (ML) for the detection of web application vulnerabilities. Web applications are particularly challenging to analyses, due to their diversity and the widespread adoption of custom programming practices. ML is thus very helpful for web application security it can take advantage of manually labeled data to bring the human understanding of the web application semantics into automated analysis toolsMitch allowed us to identify 35 new CSRFs on 20 major websites and 3 new CSRFs on production software. Keywords: Mitch, CSRF, CI/CD pipelines, Security Token Service (STS), Same-Origin Policy (SOP).

Automating SQL Injection and Cross-Site Scripting Vulnerability Remediation in Code

Internet-based distributed systems dominate contemporary software applications. To enable these applications to operate securely, software developers must mitigate the threats posed by malicious actors. For instance, the developers must identify vulnerabilities in the software and eliminate them. However, to do so manually is a costly and time-consuming process. To reduce these costs, we designed and implemented Code Auto-Remediation for Enhanced Security (CARES), a web application that automatically identifies and remediates the two most common types of vulnerabilities in Java-based web applications: SQL injection (SQLi) and Cross-Site Scripting (XSS). As is shown by a case study presented in this paper, CARES mitigates these vulnerabilities by refactoring the Java code using the Intercepting Filter design pattern. The flexible, microservice-based CARES design can be readily extended to support other injection vulnerabilities, remediation design patterns, and programming languages.

Automated Extension-Based Penetration Testing for Web Vulnerabilities

Depending on manual solutions for finding security vulnerabilities in web applications is time-consuming, error-prone, and requires high levels of expertise. This paper proposes a model for an automated extension-based penetration testing for web vulnerabilities that is compatible with any web application. It consists of three stages that start with information gathering, followed by a vulnerability assessment process to test for exploit. The results are then automatically generated as a report at the conclusion. To examine the model, a tool is developed based on the suggested model. Results demonstrate that the proposed technique has effectively and promptly identified application’s vulnerabilities.

Enhancing data protection in a web application using Django

This article explores the theme of enhancing data protection in web applications using the Django framework. It identifies the primary vulnerabilities of web applications and analyzes Django's features for defense against attacks such as SQL injections, XSS, CSRF, clickjacking, and data interception. Examining Django's built-in protection mechanisms, the article not only emphasizes their effectiveness against most common attacks but also proposes methods to improve and strengthen security measures. A detailed analysis of Django's authentication module is provided, revealing its key features in ensuring a high level of reliability in user access management. The article describes the stages of password hashing and the logic of its processing. The importance of utilizing HTTPS, SSL, and TLS protocols to enhance the security level of web applications is highlighted. Special attention is given to configuring HTTPS and using security headers, such as HTTP Strict Transport Security (HSTS), which secure connections and prevent data interception attacks. In the analysis of major attack types, including SQL injections, XSS, CSRF, focus is placed on the built-in protection functions of the web system, potential vulnerabilities, and recommendations for improving data security are presented. Additionally, protective measures against clickjacking are discussed, and recommendations for employing intermediary software to effectively counteract such attacks are provided. Emphasis is placed on the necessity of closely monitoring the possibility of page embedding and restricting this functionality to ensure the security of the web system.

Deep Learning and Web Applications Vulnerabilities Detection: An Approach Based on Large Language Models

Deep Learning and Web Applications Vulnerabilities Detection: An Approach Based on Large Language Models

Comprehensive Exploration of Web Application Security Testing with Burp Suite Tools

This research paper investigates the pivotal role of Burp Suite tools in web application security testing, aligned with the foundational principles of secure coding practices outlined by the Open Web Application Security Project (OWASP). The paper provides an in-depth overview of Burp Suite tools, emphasizing their features and the significance of web application vulnerability scanning in enforcing robust security practices. The exploration includes a detailed examination of Burp Repeater and Burp Intruder, with a practical demonstration of a Burp Suite cluster bomb attack. This research contributes to advancing the understanding and application of Burp Suite tools for effective web application security testing within the framework of OWASP secure coding practices.

Enhancing SQL Injection Attack Prevention: A Framework for Detection, Secure Development, and Intelligent Techniques

SQL injection attacks (SQLIAs) pose increasing threats as more organizations adopt vulnerable web applications and databases. By manipulating queries, SQLIAs access and destroy confidential data. This paper delivers three contributions around improving SQLIA detection research: first, a literature review assessing current detection/prevention systems to produce an SQL injection detection framework; second, specialized deep learning models optimizing session pattern analysis and feature engineering to enhance performance; third, comparing proposed models against previous defenses to surface promising research directions. Results highlight opportunities like real-time systems generalizing across attack variants through emerging techniques. Additionally, with attack complexity rising, systematized SQLIA investigation is warranted. Despite extensive study, current perspectives lack cohesive guidance informing mitigation strategies. Therefore, a framework is proposed holistically mapping knowledge gaps around contemporary SQLIAs, seminal threats in web applications, and security solutions. Furthermore, a multi-faceted framework examines research trends divided into hardening existing apps, detecting attacks on production systems, and integrating secure development practices. Literature suggests comprehensive resilience requires concurrent strength across these areas. Finally, future work remains in integrated frameworks, deep reinforcement learning adoption, automated AI auditing, and differential privacy to advance real-world SQL injection detection and prevention.

Data Leaks Using SQL Injection

The security of digital data is paramount in today's interconnected world. Among the various cyber threats, SQL injection attacks represent a significant menace to the confidentiality, integrity, and availability of sensitive information stored within databases. SQL injection is a technique employed by malicious actors to exploit vulnerabilities in web applications that interact with databases, allowing unauthorized access to or manipulation of the data. This paper presents an in-depth analysis of SQL injection attacks, their mechanisms, and the potential risks they pose to organizational data. It examines various preventive measures and best practices to mitigate the vulnerabilities that lead to SQL injection. Techniques such as input validation, parameterized queries, and the use of prepared statements are explored as effective defences against these attacks