Neural-Network Based Test Cases Generation Using Genetic Algorithm

Web applications have become an integral part of the daily lives of millions of users. Unfortunately, web applications are also frequently targeted by attackers, and critical vulnerabilities such as cross-site scripting and SQL injection are still common. As a consequence, much effort in the past decade has been spent on mitigating web application vulnerabilities. Current techniques focus mainly on sanitization: either on automated sanitization, the detection of missing sanitizers, the correctness of sanitizers, or the correct placement of sanitizers. However, these techniques are either not able to prevent new forms of input validation vulnerabilities such as HTTP Parameter Pollution, come with large runtime overhead, lack precision, or require significant modifications to the client and/or server infrastructure. In this paper, we present IPAAS, a novel technique for preventing the exploitation of cross-site scripting and SQL injection vulnerabilities based on automated data type detection of input parameters. IPAAS automatically and transparently augments otherwise insecure web application development environments with input validators that result in significant and tangible security improvements for real systems. We implemented IPAAS for PHP and evaluated it on five real-world web applications with known cross-site scripting and SQL injection vulnerabilities. Our evaluation demonstrates that IPAAS would have prevented 83% of SQL injection vulnerabilities and 65% of cross-site scripting vulnerabilities while incurring no developer burden.

- 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 application development is error prone and results in applications that are vulnerable to attacks by malicious users. The global accessibility of Web applications makes this an extremely serious problem. According to the Open Web Application Security Project (OWASP)’s top ten list that identifies the most serious web application vulnerabilities, the top three vulnerabilities in 2007 [84] were: (1) Cross Site Scripting (XSS) and (2) Injection Flaws (such as SQL Injection). Even after it has been widely reported that web applications suffer from these vulnerabilities, XSS and SQL Injection vulnerabilities remained among the top three vulnerabilities listed in OWASP’s top ten list in 2010 [85] and 2013 [86].

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.

As web applications become increasingly popular for offering data and services among businesses and organizations they also become more susceptible to security risks. Many organizations rely today on Web Vulnerability Scanners (WVSs) to identify vulnerabilities in their web applications. However, one of the most prevalent types of web application vulnerabilities, SQL Injections (SQLi), can often go undetected by WVSs. OWASP ZAP is an open-source web vulnerability scanner that allows security professionals to develop rules to improve vulnerability detection capability. In this paper, a new method is proposed to improve the detection capability of OWASP ZAP for SQLi. We aim to accurately detect the four major types of SQLi: Error-based, Union-based, Time-based blind, and Authentication Bypass. We implemented this new method using OWASP ZAP detection policies. The method was tested against two benchmark vulnerable web applications, Mutillidae and WebGoat, and was shown to significantly improve the detection of SQL injection attacks.

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.

As web applications is becoming more prominent due to the ubiquity of web services, web applications have become main targets for attackers. In order to steal or leak sensitive user data managed by web applications, attackers exploit a wide range of input validation vulnerabilities such as SQL injection, path traversal (or directory traversal), cross-site scripting (XSS), etc. This paper propose a technique that can verify input values of Java-based web applications using static bytecode instrumentation and runtime input validation. The technique searches for target methods or object constructors in compiled Java class files, and statically inserts bytecode modules. At runtime, the instrumented bytecode modules validate input values of the targets, and take countermeasure against malicious inputs. The proposed technique can mitigate the input validation vulnerabilities in Java-based web applications without source codes. To evaluate the effectiveness of the proposed technique, experiments are carried out with an insecure web application maintained by OWASP WebGoat Project. The experimental results show that the proposed technique successfully mitigates input validation vulnerabilities such as SQL injection and path traversal.

SQL Injection Attack (SQLIA) is one of the most severe attack that can be used against web database-driven applications. Attackers use SQLIA to obtain unauthorized access and perform unauthorized data modifications due to initial improper input validation by the web application developer. Various studies have shown that, on average, 64% of web applications worldwide are vulnerable to SQLIA due to improper input. To mitigate the devastating problem of SQLIA, this research proposes an automatic black box testing for SQL Injection Vulnerability (SQLIV). This acts to automate an SQLIV assessment in SQLIA. In addition, recent studies have shown that there is a need for improving the effectiveness of existing SQLIVS in order to reduce the cost of manual inspection of vulnerabilities and the risk of being attacked due to inaccurate false negative and false positive results. This research focuses on improving the effectiveness of SQLIVS by proposing an object-oriented approach in its development in order to help and minimize the incidence of false positive and false negative results, as well as to provide room for improving a proposed scanner by potential researchers. To test and validate the accuracy of research work, three vulnerable web applications were developed. Each possesses a different type of vulnerabilities and an experimental evaluation was used to validate the proposed scanner. In addition, an analytical evaluation is used to compare the proposed scanner with the existing academic scanners. The result of the experimental analysis shows significant improvement by achieving high accuracy compared to existing studies. Similarly, the analytical evaluations showed that the proposed scanner is capable of analyzing attacked page response using four different techniques.

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.

Owing to the efficient resource management, accessibility, and high service availability, cloud computing has been leveraged by several intensive-data processing applications such as big data analytics, social media applications. These applications are typically based on the development of web service and web application. Even though web-based technology offers effective communication and implementation, it has been susceptible to various kinds of attack. In this paper, we investigate possible attacks on REST which is a commonly used protocol for the web service implementation. In REST, HTTP requests are mapped to GET, POST, PUT, and DELETE that have been proven to be prone to common attacks including Automated Brute Forcing on web-based login, HTTP flood attacks, SQL injections (SQLi), and Cross-Site Scripting (XSS). To this end, we propose a design and implementation of the cloud-based IDS to detect such attacks by employing Apache Kafka and Spark streaming to classify and process the high volume of user inputs in REST HTTP communication. To detect the anomalous inputs, we apply the signature-based approach to construct an IDS engine based on a set of known attack patterns that will be leveraged by the Spark Streaming. Specifically, we introduce a new string comparison collection that improves the False Positive (FP) rate in SQL injection detection, which has been a major issue in most proposed IDS currently available. In our experiment, the system is able to determine malicious patterns with high performance as well as to generate SMS alerts and log the event in a Google Cloud Storage Bucket in an efficient manner.

Dependence on web applications is increasing very rapidly in recent time for social communications, health problem, financial transaction and many other purposes. Unfortunately, the presence of security weaknesses in web applications allows malicious user's to exploit various security vulnerabilities and become the reason of their failure. Currently, SQL Injection (SQLI) attacks exploit most dangerous security vulnerabilities in various popular web applications i.e. eBay, Google, Facebook, Twitter etc. Research on taint based vulnerability detection has been quite intensive in the past decade. However, these techniques are not free from false positive and false negative results. In this paper, we propose an approach to minimize false positive in SQLI vulnerability detection techniques using data mining concepts. We have implemented a prototype tool for PHP, MySQL technologies and evaluated it on six real world applications and NIST Benchmarks. Our evaluation and comparison results show that proposed technique detects SQLI vulnerabilities with low percentage of false positives.

According to Government Computer News (GCN) web attacks have been marked as all- time high this year. GCN says that some of the leading security software like SOPHOS detected about 15,000 newly infected web pages daily in initial three months of 2008 [13]. This has lead to the need of efficient software to make web applications robust and sustainable to these attacks. While finding information on different types of attacks, I found that SQL injection and cross site scripting are the most famous among attackers. These attacks are used extensively since, they can be performed using different techniques and it is difficult to make a web application completely immune to these attacks. There are myriad detection tools available which help to detect vulnerabilities in web applications. These tools are mainly categorized as white-box and black-box testing tools. In this writing project, we aim to develop a detection tool which would be efficient and helpful for the users to pinpoint possible vulnerabilities in his/her PHP scripts. We propose a technique to integrate the aforementioned categories of tools under one framework to achieve better detection against possible vulnerabilities. Our system focuses on giving the developer a simple and concise tool which would help him/her to correct possible loopholes in the PHP code snippets.

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.

Access control vulnerabilities that lead to elevated privileges are among the most dangerous vulnerabilities in Web applications. Most of the existing detection methods use dynamic or static analysis techniques alone, which suffer from high manual involvement, low automation, high leakage rate, low page coverage, and other deficiencies. To this end, this paper proposes a novel access control vulnerability detection method (DetAC) based on a sitemap model with global information representation. This method first constructs a static site-wide sitemap model based on the page link addresses in the Web application source code through static analysis techniques. After that, the application is logged in and executed dynamically with different role users. During this process, execution traces and request parameters are collected and converted into annotations to fill the corresponding edges of the static site-wide sitemap model. Then, the sitemap model with global information representation is obtained. This model can represent both the global control flow and data flow of the application. Then DetAC analyzes the role-based and user-based access control policies of the Web application based on the node reachability and annotated data features of the model. And according to the information such as role, user, and access resources, it generates attack vectors to achieve different roles and the same role of different users to access each other’s resources. Finally, access control vulnerabilities are detected based on the equivalence of the results obtained using attack vector access and normal access to the Web application server. DetAC was validated on five real open-source Web applications, and the results showed that DetAC successfully detected up to 12 access control vulnerabilities, which are more than those of the traditional seven tools. The dynamic analysis page coverage rate was significantly improved during the detection process, reaching an average of 91.37%.

Most people in the industrial world use a wide variety of web applications daily with the majority being insecure and vulnerable. This gives hackers the opportunity to steal data from the user’s web application, which may contain sensitive information. Vulnerability detection may be conducted by a rigorous penetration test. A penetration tester’s duty is to define and exploit the web applications’ vulnerabilities.This paper describes a technique for automatic vulnerable web application generation application. Firstly, the prepared web application is sent to the tool to create the vulnerable web application version. This tool does this by the injection of Cross Site Request Forgery (CSRF) and Cross Site Scripting (XSS) into the web application. Different variant vulnerabilities may be injected too, so different methods are needed, in order to exploit vulnerabilities dependent on the variant. One of the tool’s tasks is to produce web applications, which will be used to train the penetration testers.