The Open Web Application Security Project (OWASP) is a nonpro t organization battling for improvements in software protection and enhancing the security of web applications. Moreover, its goal is to make application security \accessible" so that individuals and organizations can make educated decisions about security threats. The OWASP is a repository of tools and standards for web security studies. OWASP released an annual listing of the top 10 most common vulnerabilities on the web in 2013 and 2017. This research paper proposed a comprehensive study on Components with known vulnerabilities attack, which is the ninth attack (A9) among the top 10 vulnerabilities. Components with known vulnerabilities are the third-party components that the focal system uses as authentication frameworks. Depending on the vulnerability it could range from subtle to seriously bad. This danger arises because the app's modules, like libraries and frameworks, are almost always run with the highest privileges. If a compromised aspect is abused, the hacker's task of causing signi cant loss of information or server takeover is easier.

SQL injection (SQLi) is one of the most common attacks against database servers and has the potential to threaten server services by utilizing SQL commands to change, delete, or falsify data. In this study, researchers tested SQLi attacks against websites using a number of tools, including Whois, SSL Scan, Nmap, Open Web Application Security Project (OWASP) Zap, and SQL Map. Then, researchers identified SQLi vulnerabilities on the tested web server. Next, researchers developed and implemented mitigation measures to protect the website from SQLi attacks. Test results using OWASP Zap identified 14 vulnerabilities, with five of them at a medium level of 35%, seven at a low level of 50%, and two at an informational level of 14%. Meanwhile, testing using SQL Map succeeded in gaining access to the database and username on the web server. The next step in this research is to provide recommendations for installing a firewall on the website as a mitigation measure to reduce the risk of SQLi attacks. The main contribution of this research is the development of a structured methodology to identify and address SQLi vulnerabilities in web servers, which play an important role in maintaining data security and integrity in a rapidly evolving online environment.

Web application vulnerability scanners cannot detect business logic vulnerabilities (vulnerabilities related to logic) because they are not able to understand the business logic of the web application. To identify the business logic of the web application, this paper presents BLProM, Business-Layer Process Miner, the black-box approach that identi es business processes of the web application. Detecting business processes of the web applications can be used in dynamic security testing to identify business logic vulnerabilities in web applications. BLProM  rst extracts the navigation graph of the web application then identi es business processes from the navigation graph. The evaluation conducted on three well-known open-source web applications shows that BLProM can detect business logic processes. Experimental results show that BLProM improves web application scanning because it clusters web application pages and prevents scanning similar pages. The proposed approach is compared to OWASP ZAP, an open-source web scanner. We show that BLProM improves web application scanning about %96.

Despite all the efforts of security experts to detect SQL injection attacks, according to OWASP report’ s, SQL injection attack is still used as the most important cyber attack by attackers. In order to detect attacks, two methods are used: signature-based and behavior-based. Signature-based methods are used for known attacks, and behavior-based methods are suitable for detecting unknown attacks. Behavior-based intrusion detection systems are more useful because attacks are implemented in different ways. Behavior can be analyzed by methods such as classification, clustering, etc. One of the most important classification algorithms is the random forest algorithm which has high accuracy and on the other hand the implementation and interpretation of the results can be done easily using this algorithm. According to the studies, the accuracy of the random forest algorithm is highly dependent on its input parameters. These parameters include 9 items, including the number of trees, their depth, voting method, information gain, and so on. Optimal determination of these parameters is an optimization problem with large state space. In this research, a method based on genetic algorithm to determine the optimal values of these parameters is presented. Due to the optimal determination of the parameters, the obtained results show an improvement in the detection accuracy compared to the default state of the algorithm and other researches. The evaluation results indicate that the intrusion detection accuracy in the proposed method was %98, which is about %11 higher than the random forest algorithm with default parameters and %08 higher than previous studies.