Web Application Attack Pattern Detection System
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Solution Overview
Problem
Web applications face challenges in detecting malicious requests that resemble benign ones, leading to potential unauthorized access and data compromise, which can result in loss of user trust and revenue for businesses.
Innovation Solution
A security module is implemented to analyze HTTP requests by parsing them, matching against stored attack patterns, and taking appropriate actions such as locking out sources that send malicious requests, with a system architecture including a request parser, attack determination module, white lists store, black lists store, attack patterns store, attack handling module, and lockout module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security mechanisms are used to prevent unauthorized access, then security is improved, but false positives increase and legitimate requests may be blocked
Solution Approach 1:
The system performs preliminary analysis of request patterns by monitoring and storing attack patterns in memory before making blocking decisions. This advance preparation allows the system to quickly distinguish malicious from legitimate requests without blocking valid traffic, resolving the contradiction between security and legitimate request processing.
Solution Approach 2:
The system continuously monitors request patterns and uses this feedback to update its attack pattern database in real-time. This feedback mechanism allows the system to learn from actual traffic patterns, improving its ability to distinguish malicious requests from legitimate ones and reducing false positives while maintaining security.
2Measurement precision
If pattern matching is performed on all requests, then detection accuracy is improved, but processing time increases
Solution Approach 1:
Attack patterns are pre-loaded into memory before runtime, and the system maintains a cached copy of these patterns for rapid comparison. This preliminary preparation eliminates the need for repeated disk I/O operations during request processing, maintaining high detection accuracy while minimizing processing time delays.
Solution Approach 2:
The system applies pattern matching selectively based on request characteristics and risk assessment. Rather than uniformly applying full pattern matching to all requests, the system focuses detailed analysis on suspicious requests while using faster filtering methods for obviously benign traffic, thus improving detection accuracy where needed without universally increasing processing time.
3Speed
If attack patterns are stored in memory, then detection speed is improved, but memory usage increases
Solution Approach 1:
The system loads only the most critical and frequently occurring attack patterns into memory, rather than storing complete comprehensive databases. This partial loading approach maintains high detection speed for the most common threats while limiting memory consumption, accepting that less common attack patterns may require additional processing time.
Solution Approach 2:
The system dynamically adjusts the amount of pattern data stored in memory based on available resources and threat levels. When memory pressure is detected or threat levels change, the system modifies its memory allocation for attack patterns, balancing speed requirements against memory constraints by loading additional patterns only when necessary.
Data Source
AI summary
A system and a method are disclosed for determining malicious web requests. The system processes incoming web requests to determine whether a request matches predetermined patterns of suspicious requests. The systems stores associations between patterns of suspicious requests, parts of the web request where each pattern occurs, and attack type associated with the pattern. If the system determines that an incoming web request matches a pattern of a suspicious request, the system determines whether to forward the request to the web server for processing or to hold the request. The system locks out a source of request for a period of time if the source sends requests that match certain attack patterns.