Biblio

The advancements in technology can be seen in recent years, and people have been adopting the emerging technologies. Though people rely upon these advancements, many loopholes can be seen if you take a particular field, and attackers are thirsty to steal personal data. There has been an increasing number of cyber threats and breaches happening worldwide, primarily for fun or for ransoms. Web servers and sites of the users are being compromised, and they are unaware of the vulnerabilities. Vulnerabilities include OWASP's top vulnerabilities like SQL injection, Cross-site scripting, and so on. To overcome the vulnerabilities and protect the site from getting down, the proposed work includes the implementation of a Web Application Firewall focused on the Application layer of the OSI Model; the product protects the target web applications from the Common OWASP security vulnerabilities. The Application starts analyzing the incoming and outgoing requests generated from the traffic through the pre-built Application Programming Interface. It compares the request and parameter with the algorithm, which has a set of pre-built regex patterns. The outcome of the product is to detect and reject general OWASP security vulnerabilities, helping to secure the user's business and prevent unauthorized access to sensitive data, respectively.

Cross-Site scripting (XSS) is a common class of vulnerabilities in the domain of web applications. As it re-mains prevalent despite continued efforts by practitioners and researchers, site operators often seek to protect their assets using web application firewalls (WAFs). These systems employ filtering mechanisms to intercept and reject requests that may be suitable to exploit XSS flaws and related vulnerabilities such as SQL injections. However, they generally do not offer complete protection and can often be bypassed using specifically crafted exploits. In this work, we evaluate the effectiveness of WAFs to detect XSS exploits. We develop an attack grammar and use a combinatorial testing approach to generate attack vectors. We compare our vectors with conventional counterparts and their ability to bypass different WAFs. Our results show that the vectors generated with combinatorial testing perform equal or better in almost all cases. They further confirm that most of the rule sets evaluated in this work can be bypassed by at least one of these crafted inputs.

Cross-site scripting (XSS) attack is a kind of code injection that allows an attacker to inject malicious scripts code into a trusted web application. When a user tries to request the injected web page, he is not aware that the malicious script code might be affecting his computer. Nowadays, attackers are targeting the web applications that holding a sensitive data (e.g., bank transaction, e-mails, healthcare, and e-banking) to steal users' information and gain full access to the data which make the web applications to be more vulnerable. In this research, we applied three approaches to find a solution to this most challenging attacks issues. In the first approach, we implemented Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (k-NN), and Support Vector Machine (SVM) algorithms to discover and classify XSS attack. In the second approach, we implemented the Content Security Policy (CSP) approach to detect XSS attacks in real-time. In the last approach, we propose a new approach that combines the Web Application Firewall (WAF), Intrusion Detection System (IDS), and Intrusion Prevention System (IPS) to detect and prevent XSS attack in real-time. Our experiment results demonstrated the high performance of AI algorithms. The CSP approach shows the results for the detection system report in real-time. In the third approach, we got more expected system results that make our third model system a more powerful tool to address this research problem than the other two approaches.