A METHOD FOR COUNTERING OBVIOUS AND HIDDEN ATTACKS ON WEB APPLICATIONS USING AN INTELLIGENT TRAFFIC ANALYSIS SYSTEM

Abstract

The article proposes a method for countering attacks on web applications, which is based on the use of an intelligent traffic analysis system. Modern web applications face numerous threats, among the most common of which are SQL injection attacks, cross-site scripting (XSS), and distributed denial-of-service attacks (DDoS), which can be used both explicitly and using obfuscation, steganographic, or cryptographic modification methods. In this regard, the importance of timely detection of such attacks and their effective blocking is critical for the functioning of comprehensive information protection systems.

Attention is paid to developing methods for adapting intelligent systems to new, previously unknown attacks that arise as a result of the evolution of hacking techniques. Given the limitations of standard comprehensive information security systems, this research aims to create an effective, adaptive, and scalable mechanism for protecting web applications, capable of ensuring a high level of security in a dynamically changing information environment. The method proposed in this article relies on the use of intelligent algorithms for traffic analysis, particularly machine learning and artificial intelligence algorithms, to detect anomalies and identify potential threats in real time. A key feature of this approach is the system's ability to automatically learn and adapt to new types of attacks that emerge as a result of constant changes in technological approaches to carrying out malicious activities.

The results confirm that the use of intelligent network traffic analysis systems is an effective approach to strengthening web application security. Such systems demonstrate the ability not only to identify known malicious patterns, but also to adapt to detecting new, previously unknown attack vectors. At the same time, there are still some big problems that need to be solved. You need a lot of computing power to handle massive data flows, minimize false positives, and regularly retrain models to keep threat detection rates high. Integrating machine learning models with SIEM systems will enable automated threat detection and response, improving incident response times.