Detection and classification of input validation attacks using machine learning - Mixed-method analytics

Because of the widespread and extensive deployment of susceptible web applications, application security is still a challenging field. This maintains the vulnerability of easily accessible entry points that can compromise entire apps. The primary cause of this problem is the widespread lack of strong validation procedures on the client and server sides. Input Validation Attacks (IVA)—which include risks such as Cross-Site Scripting (XSS), SQL injection (SQLi), path traversal, and command injection (CMDi)—occur when inputs are not sufficiently sanitized. Security is a top priority in the application area, particularly as web application security is an essential interface for communicating with computer systems and the vast Internet. Given these difficulties, the goal is to improve web application security by implementing hybrid analysis-based static and dynamic deterministic pushdown automata that are enhanced by an intelligent framework. This will allow for the proactive detection of vulnerabilities and the differentiation of normal from abnormal requests. Even while the methods available today are successful in detecting web attacks, there are situations where a combination of approaches is required. As such, the research explores hybrid approaches, enhanced by a machine learning framework. Next, we assess if an unsupervised method for input validation attacks based on quick mining tool is feasible. The results of our study demonstrate a significant improvement in accuracy when compared to baselines. We highlight the improved accuracy of our technique with a 3% improvement over DEKANT, 6% over WAP, 11% over PhpMinerII, 54% over Pixy, and 21% over RIPS. Moreover, its precision is 20% higher than static taint analysis.