Query String Signature Detection for Botnet Command Channels
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Solution Overview
Problem
Botnets are difficult to detect and block due to their stealthy communication methods, which resemble normal HTTP requests, making it challenging to identify command and control channels.
Innovation Solution
A method involving the normalization of query strings from HTTP requests, grouping requests by signatures, and analyzing factors such as the number of requested domains, domain access rate, content sizes, and time-to-live to identify suspicious botnet behavior and block malicious requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bot detection methods are used, then detection capability is maintained at current levels, but detection accuracy and effectiveness are insufficient against stealthy botnet communication
Solution Approach 1:
The patent segments the HTTP request into distinct components (domain, path, query string) and processes each separately. The query string is further segmented into parameters, which are then normalized and grouped to create signatures. This segmentation allows detailed analysis of request patterns without being overwhelmed by the complexity of entire request structures.
Solution Approach 2:
The patent extracts the query string from the HTTP request and separates it from other request components. By taking out the query string specifically for analysis, the system can identify botnet behavior patterns without being distracted by legitimate request variations in other parts of the URL structure.
2Reliability
If honeypot technique is used to detect botnets, then malicious content can be captured, but the process takes time and is highly dependent on effectiveness in luring malware source
Solution Approach 1:
The patent performs preliminary analysis of HTTP requests in real-time as they occur, rather than waiting for botnets to actively attack a honeypot. By continuously monitoring and analyzing query string patterns, the system can identify botnet behavior proactively, significantly reducing detection time.
Solution Approach 2:
The system analyzes request patterns and provides feedback by identifying suspicious signatures that indicate botnet activity. This feedback mechanism allows the system to adapt to evolving botnet techniques and maintain high detection accuracy without requiring time-consuming manual analysis.
3Measurement precision
If query string normalization and signature grouping is performed, then botnet behavior can be identified through pattern recognition, but system complexity increases
Solution Approach 1:
The patent applies parameter changes by normalizing the query string - transforming it into a standardized format where parameter values are removed and only parameter names remain. This transformation creates consistent signatures that can be reliably grouped and compared, significantly improving detection accuracy without requiring complex analysis algorithms.
Solution Approach 2:
The patent creates a simplified copy of the query string structure for analysis purposes. By copying the essential pattern information while removing variable parameter values, the system can analyze botnet behavior patterns without processing the full complexity of actual request data.
Data Source
AI summary
Detection and prevention of botnet behavior is accomplished by monitoring access request in a network. Each request includes a domain of content to access and a path of content to access, and each path includes a file name and query string. Once obtained, the query strings for each of these requests are normalized. A signature is then created for each of the normalized query strings. The obtained requests can then be grouped by signature. Once the requests have been grouped by signature, each grouping is examined to identify suspicious signatures based on common botnet behavior. Suspicious requests are used in back-end and front-end defenses against botnets.