Website Vulnerability Scanning System

- In an era where web applications are increasingly targeted by sophisticated cyber threats, ensuring robust and continuous security testing has become essential. This paper presents Autopen Test, a real-time automated framework designed to identify and assess vulnerabilities in web applications with minimal human intervention. Autopen Test integrates dynamic analysis, intelligent crawling, and adaptive scanning techniques to detect common and complex security flaws such as SQL injection, cross-site scripting (XSS), and insecure configurations. By leveraging automation and real-time feedback mechanisms, the framework provides immediate insights into potential threats, significantly reducing the time between vulnerability discovery and mitigation. Extensive experiments demonstrate Autopen Test’s effectiveness and efficiency compared to existing tools, showcasing its potential as a valuable asset in modern DevSecOps pipelines. This research contributes a scalable and extensible solution to the field of web application security, emphasizing the importance of real-time, automated approaches in proactive vulnerability management. Key Words: Web Application Security, Automated Vulnerability Assessment, Real-Time Scanning, Autopen Test, Dynamic Analysis, Intelligent Crawling, Adaptive Scanning, SQL Injection, Cross-Site Scripting (XSS). DevSecOps.

Web technologies are typically built with time constraints and security vulnerabilities. Automatic software vulnerability scanners are common tools for detecting such vulnerabilities among software developers. It helps to illustrate the program for the attacker by creating a great deal of engagement within the program. SQL Injection and Cross-Site Scripting (XSS) are two of the most commonly spread and dangerous vulnerabilities in web apps that cause to the user. It is very important to trust the findings of the site vulnerability scanning software. Without a clear idea of the accuracy and the coverage of the open-source tools, it is difficult to analyze the result from the automatic vulnerability scanner that provides. The important to do a comparison on the key figure on the automated vulnerability scanners because there are many kinds of a scanner on the market and this comparison can be useful to decide which scanner has better performance in term of SQL Injection and Cross-Site Scripting (XSS) vulnerabilities. In this paper, a method by Jose Fonseca et al, is used to compare open-source automated vulnerability scanners based on detection coverage and a method by Yuki Makino and Vitaly Klyuev for precision rate. The criteria vulnerabilities will be injected into the web applications which then be scanned by the scanners. The results then are compared by analyzing the precision rate and detection coverage of vulnerability detection. Two leading open source automated vulnerability scanners will be evaluated. In this paper, the scanner that being utilizes is OW ASP ZAP and Skipfish for comparison. The results show that from precision rate and detection rate scope, OW ASP ZAP has better performance than Skipfish by two times for precision rate and have almost the same result for detection coverage where OW ASP ZAP has a higher number in high vulnerabilities.

In Current scenario, many Web applications go through rapid development phases like adopting agile methodology of development with very short turnaround time, making it difficult to identify and eliminate vulnerabilities. This paper provides analysis of requirement of Secure Development and Web application security assessment mechanisms in order to identify poor coding practices that render Web applications vulnerable to attacks such as SQL injection and cross-site scripting. This paper also list out the different categories of vulnerability with the small examples along with prevention guidance and a sample of research by different vendors about the State of Software Security Report and Web Application Vulnerability Statistics of 2012. This paper reviews need of secure development, resource s available for creating secure Web applications. These resources ranges from the security features of the development, to automated tools evaluating an existing Web application, to Web sites dedicated to all facets of Web application security. In Web application security, making one single mistake can lead to many unwanted flaws. By using the different resources available, the risk of the applications to be vulnerable can be reduced to an acceptable level. In addition, some risk can be avoided at the very beginning of the project life cycle when the requirements for the system are defined.

Strong security in web applications is critical to the success of your online presence. Security importance has grown massively, especially among web applications. Dealing with web application or website security issues requires deep insight and planning, not only because of the many tools that are available but also because of the industry immaturity. Thus, finding the proper tools requires deep understanding and several steps, including analyzing the development environment, business needs, and the web applications’ complexity. In this paper, we demonstrate the architecture of web applications then list and evaluate the widespread security vulnerabilities. Those vulnerabilities are: Fingerprinting, Insufficient Transport Layer Protection, Information Leakage, Cross-Site Scripting, SQL Injection, and HTTP Splitting. In addition, this paper analyzes the tools that are used to scan for these widespread vulnerabilities in web applications. Finally, it evaluates tools due to security vulnerabilities and gives recommendations to the web applications’ users and administrators aiming to educate them.

With the fast development of 5G networks, the need to ensure that these networks are secured against cyber threats, which are advanced and constantly evolving, is of immense importance. In this paper, an intrusion detection system based on deep learning using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture is introduced to meet the security requirements of 5G wireless networks. The model also showed high accuracy, precision, and recall when training and testing on various publicly available datasets (CICIDS 2017 and NSL-KDD) by identifying different types of attacks, namely Distributed Denial of Service (DDoS), SQL Injection, and Advanced Persistent Threats (APT). Findings reveal that the CNN-LSTM model performs better compared to standard machine learning models such as Support Vector Machines (SVM) and Decision Tree, scoring high with regard to detection abilities and optimizing computing requirements. The model is an effective intrusion detection method in real-time despite training time and latency issues, it is a very useful solution to the fast-developing intrusion detection scenario involving the dynamic 5G network. The study offers an insight into the possibility of deep learning methods in boosting cybersecurity in 5G networks and lays the foundation for newer advances in network security systems. Keywords: 5G Networks, Intrusion Detection System (Ids), Deep Learning, Cnn, Lstm, Hybrid Model, Cybersecurity, Real-Time Detection, Network Security, Machine Learning, Ddos, Apt, Attack Detection.

To facilitate the detection of various vulnerabilities, there are many different tools (scanners) that can help analyze the security of web applications and facilitate the development of their protection. But these tools for the most part can only identify problems, and they are not capable of fixing them. Therefore, the knowledge of the security developer is a key factor in building a secure Web resource. To resolve application security problems, developers must know all the ways and vectors of various attacks in order to be able to develop various protection mechanisms. This review discusses two of the most dangerous vulnerabilities in the field of Web technologies: SQL injections and XSS attacks (cross-site scripting – XSS), as well as specific cases and examples of their application, as well as various approaches to identifying vulnerabilities in applications and threat prevention. Cross-site scripting as well as SQL-injection attacks are related to validating input data. The mechanisms of these attacks are very similar, but in the XSS attacks the user is the victim, and in the SQL injection attacks, the database server of the Web application. In XSS attacks, malicious content is delivered to users by means of a client-side programming language such as JavaScript, while using SQL injection, the SQL database query language is used. At the same time, XSS attacks, unlike SQL injections, harm only the client side leaving the application server operational. Developers should develop security for both server components and the client part of the web application.

The increasing use of the internet is attributed to the growing reliance on web-based systems, as nearly every aspect of present-day life utilizes such systems. According to the HackerOne 2021 report, there has also been a rise in cyberattacks, particularly targeting web-based systems. One method of identifying vulnerabilities in web-based systems is through penetration testing. The conducted research involves the implementation of distributed penetration testing on web-based systems using the Dynamic Infrastructure Framework Axiom as a tool for constructing distributed infrastructure. The researchers tested three vulnerabilities: Cross-site Scripting (XSS), SQL Injection, and Arbitrary Code Injection. These three vulnerabilities are among the top ten most frequently identified vulnerabilities according to the HackerOne 2021 report. The results of the research reveal a significant difference in testing times between distributed and non-distributed automated penetration testing. Distributed automated penetration testing was notably faster compared to both non-distributed and non-automated penetration testing.

In current information era, every small-scale industry, MNC's, Schools, and Colleges utilise Web Applications to promote their organizations and provide services to society. Web Applications have become a simple and vital medium for communicating with society. This leads to an increase in website utilization. Threats and cyber-attacks are increasing according to the rate of growth of Web Applications. Any organization must ensure the privacy and security of its users' data in order to maintain the company's and users' integrity. Web application vulnerabilities represent major security risks, making it critical to conduct extensive assessments and penetration testing to discover and remediate such flaws. The paper provides a complete overview of web application vulnerability assessment and penetration testing, emphasizing the need of proactive security measures in protecting sensitive data and preserving application integrity. This study aims to identify and categorize web application vulnerabilities (OWASPs) that may compromise the security of the application and result in user data breaches. The primary focus of this project is the security and privacy of user data. Integrating security practices into the software development life cycle through investigating real-world case studies and industry standards. Ultimately, our research adds to the growing body of knowledge in web application security, assisting organizations in creating resilient defenses against cyber threats.

The article discusses key issues of web application security and methods of protection against hacker attacks. With businesses increasingly dependent on online services, web application security is becoming one of the top priorities for organizations. Sufficient attention should be paid to security measures to protect user data from unwanted access and malicious use. This article reviewed the main types of hacker attacks on web applications, such as SQL injection (SQLi), Cross-site scripting (XSS), Cross-site request forgery (CSRF), Server-side request forgery (SSRF) and file attacks, as well as examples of protection measures against each type of attacks. Updated and improved basic principles of web application security are proposed based on threat modeling techniques.

Web application security is a threat to the world's information technology infrastructure. The most widely used accepted solution to this threat is to deploy an Intrusion Detection System(IDS). Such systems currently rely on either signature of the attack or changes in the behavior patterns of the system to identify an intruder. These systems, either signature-based or anomaly-based, are readily understood by attackers. Problem occurs when attacks are not detected by the existing IDS because the attack does not fit the pre-defined attack signatures. This work intends to address this problem. In this paper, we implemented two IDS model which includes an anomaly detection technique measured by cross entropy and a signature-based attack detection using genetic algorithm. Both the methods, that is signature based as well as anomaly based IDS are used to detect even more attacks than either approach could detect alone. By compiling each of the approaches, additional false positive and false negative attacks can be discovered as well. Those included in this work are SQL injection, cross-site scripting and remote file inclusion.

This capstone project shows the creation and implementation of a virtual environment for the use of penetration testing. This aims to identify vulnerabilities within IT in- frastructure. With a world that is heavily reliant on digital systems, the cybersecurity threats are evolving at a rapid pace which highlights the necessary measurements needed for effective security measures. This project showcases the challenges that are faced by organizations using outdated systems with unpatched vulnerabilities. This exposes them to potential data breaches and substantial financial losses as well as reputational damage.The virtual lab replicates a corporate IT infrastructure with different operating systems including windows, Linux, android as well as web application and network configurations. using both internal and external penetration testing, this is targeting vulnerabilities such as SQL injection, cross-site scripting (XSS), privilege escalation etc. Tools like SQLMap, Metasploit and others are used to simulate real-world attack scenarios. Each one thoroughly documented, a detailed analysis will provide insight into potential exploitation and mitigation strategies.The outcome of this project will include identifying critical security flaws and offering recommendations to enhance security. In addition, this project gives an educational standpoint onto showcasing the value of pen testing, or ethical hacking in the practices of today’s cyber security. These findings showcase how the need for penetration testing will give organizations a framework to protect against threats effectively.For future work, this will involve expanding the lab for more modern threats as they are evolving, as well as introducing more automated penetration testing processes to ease the workload and to keep up with the expanding of IoT’s, In addition this project contributes to the broader field of cybersecurity by giving a more clear understanding Aswell as implementation of practical security measures in the world, specifically IT environments.

DDoS (Distributed Denial of Service) attacks consist of a large number of compromised computer systems that launch joint attacks at a targeted victim, such as a server, website, or other network equipment, simultaneously. DDoS has become a widespread and severe threat to the integrity of computer networks. DDoS can lead to system paralysis, making it difficult to troubleshoot. As a key building block in the development of an integrated defense system, it is essential to detect DDoS attacks as early as possible. With the popularization of artificial intelligence, more and more researchers apply machine learning (ML) and deep learning (DL) to the detection of DDoS attacks and have achieved satisfactory accomplishments. The complexity and sophistication of DDoS attacks have kept increasing and evolving since the first DDoS attack was reported in 1996. Regarding the headways in this problem, a new type of DDoS attack, named adversarial DDoS attack, is investigated in this study. The generating adversarial DDoS traffic is carried out using a symmetric Generative Adversarial Networks (GAN) architecture called Cycle-GAN to demonstrate the severe impact of adversarial DDoS attacks. Experiment results reveal that the synthesized traffic can easily penetrate ML-based detection systems, including Random Forest, KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and Naïve Bayes. These alarming results intimate the urgent need for countermeasures against adversary DDoS attacks.

This study aims to analyze and improve the security of the SawitGoDigi Palm Oil Harvest Recording Information System using grey-box and white-box testing approaches. The system is used by farmers, agents, drivers, and administrators to manage land data, harvest results, distribution, and transaction records, which makes it highly exposed to security risks if vulnerabilities are present. The security testing process was conducted based on the OWASP Web Security Testing Guide (WSTG) v4.2 and the OWASP Risk Rating Methodology. The testing stages included reconnaissance, automated scanning using OWASP ZAP, manual exploitation, risk evaluation, implementation of security improvements, and retesting. The results revealed several significant vulnerabilities, including SQL Injection in the search feature, weak session management through the trusted_device cookie, and the absence of a rate-limiting mechanism that enabled brute-force attacks during the login process. The risk assessment indicated that SQL Injection and session hijacking were classified as High risk, while brute-force attacks were categorized as Medium risk. Security improvements were implemented through the use of prepared statements, strengthening cookie attributes, adding security headers, and implementing rate limiting. Retesting results confirmed that all identified vulnerabilities were successfully mitigated and reduced to a Low-risk level. This study demonstrates that a comprehensive security testing approach, which includes exploitation, remediation, and verification through retesting, can significantly enhance the security of agribusiness web applications. Furthermore, the findings show that before remediation, the system contained four vulnerabilities with High and Medium risk levels, namely SQL Injection, Session Hijacking, Brute-Force Login, and Security Misconfiguration. After the remediation and retesting process, all High- and Medium-risk vulnerabilities were successfully reduced to Low risk or marked as Closed, indicating that the system is secure for operational use.

The present research focuses on the security of Macedonian websites. It involves the analysis of HTTP Security header responses for 756 websites in the country, of which 246 are the most popular. This analysis is conducted across 13 different categories of websites, including government bodies and institutions, public institutions and enterprises, educational, commercial, news and media, entertainment, sports, etc. We intend to create a comprehensive security profile for the country's websites, which will help raise their overall security level. It is critical to understand and implement proper HTTP security headers to prevent or limit the dangers that can cause website attacks such as Denial of Service (DoS), Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), SQL Injection, clickjacking, etc. Our analysis was performed with the help of the Mozilla Observatory tool. We have discovered a significant lack of implementation and/or misconfiguration of HTTP security headers in all categories. Almost half of the websites (n=375; 49.60\%) have an F grade, while more than a quarter of all websites (n=214; 28.31\%) have a minimal security score of 0.

SQL injection attack (SQLIA) is considered one of most threats used to attack web applications. Therefore, attackers used SQL injection vulnerability to gain ultimate access to databases that belong to applications and expose their sensitive information. Thus, attackers use SQL injections vulnerability to manipulate data also it could be used to take full control of the target machine. Accordingly, several methods were proposed in the literature to address this vulnerability widely because of its importance and high impact on the security of web applications. Thus, we propose a model to detect and prevent SQL injection attacks, which uses runtime validation to detect the occurrence of such attacks, our proposed model is adaptable to any existing system, with no need to modify the client or server and either no need to know web application source code. Furthermore, the modification independence is done by adding additional middleware between client and server. Thus, any check process is done on this middleware, and it is represented as a proxy that can do sanitize the inputs for detecting and preventing SQLIA. Furthermore, our proposed model accuracy reaches 86:6% for detecting and preventing SQLIA.