NEURAL NETWORK-BASED WEB SECURITY FIREWALL

Web application firewall (WAF) examines malicious traffic to and from a web application via a set of security rules. It plays a significant role in securing Web applications against web attacks. However, as web attacks grow in sophistication, it is becoming increasingly difficult for WAFs to block the mutated malicious payloads designed to bypass their defenses. In response to this critical security issue, we have developed a novel learning-based framework called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WAFBooster</small>, designed to unveil potential bypasses in WAF detections and suggest rules to fortify their security. Using a combination of shadow models and payload generation techniques, we can identify malicious payloads and remove or modify them as needed. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WAFBooster</small> generates signatures for these malicious payloads using advanced clustering and regular expression matching techniques to repair any security gaps we uncover. In our comprehensive evaluation of eight real-world WAFs, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WAFBooster</small> improved the true rejection rate of mutated malicious payloads from 21% to 96%, with no false rejections. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WAFBooster</small> achieves a false acceptance rate 3× lower than State-of-the-Art methods for generating malicious payloads. With <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WAFBooster</small>, we have taken a step forward in securing web applications against the ever-evolving threats.

Web application security has become a major requirement for any business, especially with the wide web attacks spreading despite the defensive measures and the continuous development of software frameworks and servers. In this study, we present a proposed model for a web application firewall that used machine learning and features engineering to detect common web attacks. Our proposed model analyses incoming requests to the webserver, parses these requests to extract four features that describe completely HTTP request parts (URL, payload, and headers), and classifies whether a request is normal or an anomaly. We took into consideration the limitation of previous works that use URL and payload only in classification and provided five features that describe and summarize all parts of the HTTP request using features engineering and previous experience in the field of the software security domain. Extracted features are length of request, percentage of characters allowed, percentage of special characters, and attack weight. These features were calculated for four different datasets CSIC 2010, HTTPParams 2015, Hybrid dataset (CSIC 2010 and HTTPParams), and real logs for the compromised web server. We evaluated our proposed model by using these updated datasets with four classification algorithms (Naive Bayes, logistic regression, decision tree, and support vector machine) with two methods (train test split and cross-validation) to negate the probability of overfitting and ensure that features are effective. Features values for a normal request are usually short request length, large allowed character ratio, small special character ratio, and zero attack weight or close to zero. Features values for anomaly requests are large request length, small allowed character percentage, large special character percentage, and very large numerically attack weight. Our proposed model achieved a classification accuracy of 99.6% with datasets used in research studies in this field and 98.8% with datasets of real web servers.

Web applications are not protected by today's network level firewalls, because they allow access to TCP port 80 without restrictions. However, many successful attacks today are not on the network level, but on application level. For protecting against application level attacks, a firewall must understand the application protocols, that are used on its open ports. This happens in application level firewalls and for Web application in Web application firewalls. The underlying concepts of Web application firewalls differ much from the concepts of traditional network level firewalls. This book explains the underlying concepts of Web application firewalls. Afterwards, they are applied to a collection of security requirements, that application developers should respect today for developing a secure Web application. A Web application firewall is capable of automatically implementing many of these requirements. As a result, Web application developers can ignore these requirements, because the Web applications firewall already ensures their implementation and therefore the security of the Web application. This book is intended for anyone, who is interested in securing his Web application.

Finding differences between programs with similar functionality is an important security problem as such differences can be used for fingerprinting or creating evasion attacks against security software like Web Application Firewalls (WAFs) which are designed to detect malicious inputs to web applications. In this paper, we present SFADIFF, a black-box differential testing framework based on Symbolic Finite Automata (SFA) learning. SFADIFF can automatically find differences between a set of programs with comparable functionality. Unlike existing differential testing techniques, instead of searching for each difference individually, SFADIFF infers SFA models of the target programs using black-box queries and systematically enumerates the differences between the inferred SFA models. All differences between the inferred models are checked against the corresponding programs. Any difference between the models, that does not result in a difference between the corresponding programs, is used as a counterexample for further refinement of the inferred models. SFADIFF's model-based approach, unlike existing differential testing tools, also support fully automated root cause analysis in a domain-independent manner. We evaluate SFADIFF in three different settings for finding discrepancies between: (i) three TCP implementations, (ii) four WAFs, and (iii) HTML/JavaScript parsing implementations in WAFs and web browsers. Our results demonstrate that SFADIFF is able to identify and enumerate the differences systematically and efficiently in all these settings. We show that SFADIFF is able to find differences not only between different WAFs but also between different versions of the same WAF. SFADIFF is also able to discover three previously-unknown differences between the HTML/JavaScript parsers of two popular WAFs (PHPIDS 0.7 and Expose 2.4.0) and the corresponding parsers of Google Chrome, Firefox, Safari, and Internet Explorer. We confirm that all these differences can be used to evade the WAFs and launch successful cross-site scripting attacks.

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 increasing complexity and sophistication of web-based applications have made them vulnerable to various security threats. Web Application Firewalls (WAFs) have emerged as an essential security measure to protect web applications from common attacks such as SQL injection, cross-site scripting (XSS), and others. This research paper explores the techniques and challenges involved in controlling web traffic using a Web Application Firewall. The paper discusses the working principles of WAFs, the various methods employed to analyze and filter web traffic, and the effectiveness of these techniques in mitigating different types of attacks. Additionally, the paper highlights the challenges faced in deploying and managing WAFs and provides insights into potential future developments in the field. This research paper delves into the techniques and challenges associated with controlling web traffic using a Web Application Firewall. It explores the fundamental principles of WAFs, including signature-based filtering, behavior-based analysis, whitelisting, blacklisting, rate limiting, and protection against common vulnerabilities. The paper evaluates the effectiveness of these techniques in mitigating various attacks and highlights the challenges involved in deploying and managing WAFs, such as configuration complexity, false positives and negatives, performance impact, and integration with existing security infrastructure. Furthermore, the paper discusses emerging trends and potential future directions in the field, including the integration of machine learning and artificial intelligence, advanced behavioral analysis techniques, and cloud-based architectures.

Web applications are the most common platforms for the exchange of information and services on the Internet. With the launch of web 2.0, information has flourished through social networking and business online. Therefore, websites are often attacked directly. As a result, the industry has paid more attention to the security of web applications in addition to security under computer networks. Intelligent systems based on machine learning have demonstrated excellent result on tasks such as anomaly detection in web requests. However, current methods based on traditional models cannot extract high‐level features from huge data. In this study, the authors proposed methods based on deep‐neural‐network and parallel‐feature‐fusion that features engineering as an integral part of them and plays the most important role in their performance. The proposed methods use stacked autoencoder and deep belief network as feature learning methods, in which only normal data is used in the classification of training phase, then, one‐class SVM, isolation forest, and elliptic envelope are applied as classifiers. The authors compared the proposed model with different strategies on CSIC 2010 and ECML/PKDD 2007 datasets. Results show that deep model and feature fusion model demonstrated as hierarchical feature learning which had better performance in terms of accuracy and generalisation in a reasonable time.

With the increasing reliance on web applications for business and personal use, ensuring website security has become a critical concern. Cyber threats such as SQL injection, cross-site scripting (XSS), malware infections, and unauthorized access pose significant risks to websites, leading to data breaches and service disruptions. This project aims to develop a comprehensive website security scanner that systematically identifies vulnerabilities and potential security risks.The proposed system integrates automated vulnerability scanning, penetration testing techniques, and real-time monitoring to detect security loopholes. Using machine learning and heuristic-based analysis, the scanner can identify malicious scripts, outdated software versions, weak authentication mechanisms, and misconfigured security policies. The system also performs network security assessments, analyzing potential DDoS (Distributed Denial-of-Service) attack risks and firewall configurations. The scanner generates detailed security reports, providing actionable insights and recommendations for website owners and administrators to mitigate risks effectively. Designed for continuous monitoring and proactive defense, the tool enhances cybersecurity resilience against evolving threats. This project contributes to web security advancements by offering an intelligent, automated, and scalable solution for safeguarding websites from cyberattacks. Keywords: Website Security | Vulnerability Scanner | Cyber Threats | SQL Injection | Cross-Site Scripting (XSS) | Penetration Testing | Machine Learning | Malware Detection | DDoS Protection | Authentication Security | Firewall Analysis | Web Application Security | Risk Assessment | Cybersecurity Resilience

Internet and computer systems are an indispensable part of daily life. The number of web applications have increased with the development of technology and digital transformation. Web applications have high risk for security since the applications is not developed securely, contains vulnerabilities and easily accessible by hackers. Web application firewall is used to protect web applications from attacks. Signature-based and anomaly-based methods are used in web application firewalls. In this research, anomaly-based web application firewall model was developed using natural language processing techniques and linear support vector machine learning algorithm. Word n-gram and character n-gram natural language processing techniques were compared by performing separate models. Proposed model achieve higher detection performance with using the character n-gram compared to other studies. According to the results of the experiment proposed model is capable of detection web attacks effectively with the overall detection accuracy rate of 99.53%.

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.

Protecting Web Based Applications: A Best Practices Guide

Internet users and its usage have grown almost exponentially during last decade. Most of the web applications contain both private(sensitive) and public information in theircorresponding database that brings the security of private information on the forefront of the challenge of this domain. Cyber criminals can attempt to stealor tamperwith private information from these insecure or vulnerable web applications by exploiting. In this paper, we have analyzed different approaches of web application security used in current practices since their development such as secure coding, Web Application firewall, vulnerability assessment and penetration testing. In addition, we have also discussed various approachesand repositories which support vulnerability assessment and penetration testing processes.

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.

SummaryThis work presents an extended and enhanced gray‐box combinatorial security testing methodology for SQL injection vulnerabilities in web applications. We propose multiple new attack grammars modelling SQLi attacks against MySQL‐compatible databases, each one targeting a different injection context. Additionally, these grammars are also dynamically refined at the beginning of each attack against an endpoint of a web application, as a further optimization of the used attack model by taking into account the specifics of the generated query of that endpoint. Our goal is to enhance existing combinatorial approaches for detecting SQL injection vulnerabilities. The newly developed methodology is implemented in a prototype security testing tool called SQLInjector+, which is an extension of an earlier prototype developed by us in prior work. This improved tool can attack (i.e. test) any web application that uses a MySQL‐compatible database management system. We evaluate our revised approach and improved prototype tool in a case study comprising of different kinds of web applications to which SQLi is a potential security threat. The case study contains the well‐known verification framework WAVSEP among other five real‐world web applications and one web application firewall. Our generated attack vectors, constructed via combinatorial methods applied to our improved and dynamically optimized attack grammars, are capable of injecting every known vulnerable endpoint in WAVSEP and also of finding new vulnerable parameters in some of the real‐world applications investigated in this paper. Our approach performs equally well or better when compared with existing state‐of‐art of SQL injection security testing tools (sqlmap, w3af, wapiti and fuzzdb) across all tested web applications in the case study.

Web Application Firewalls (WAFs) serve as a critical defense mechanism against various web-based attacks such as SQL Injection (SQLi), Cross-Site Scripting (XSS), Server-Side Request Forgery (SSRF), Remote Code Execution (RCE), and NoSQL Injection. However, modern adversaries often craft evasive and obfuscated payloads capable of bypassing traditional WAF rules. To effectively assess and challenge the robustness of WAFs, we propose DEG-WAF, a Deep Evasion Generation framework that leverages Large Language Models (LLM) in conjunction with Reinforcement Learning (RL) to generate evasive payloads against WAFs. The system consists of four core components: a payload generation agent based on a pre-trained LLM (OPT-125M), a reward model that approximates WAF behavior, a grammar-based sampling agent that ensures syntactic validity, and an RL agent trained with either Proximal Policy Optimization (PPO) or Advantage Actor-Critic (A2C) to fine-tune generation strategies. Experimental evaluations on real-world WAFs, including ModSecurity and SafeLine, demonstrate that the A2C-based model significantly outperforms baseline LLMs—achieving a bypass success rate of 80.16% on SQLi and 74.70% on NoSQLi for ModSecurity, and 97.8% on RCE for SafeLine. These results underscore the potential of our LLM-RL framework to serve as a robust foundation for evaluating and enhancing the resilience of WAF systems under adversarial conditions.