HTTP Response Pattern Analysis for DDoS Mitigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems are ineffective in accurately differentiating between attack traffic and legitimate traffic during Distributed Denial of Service (DDOS) attacks, particularly in DNS amplification and reflection attacks, which can overwhelm networks and cause service outages.
Innovation Solution
A system and method that analyze HTTP responses to extract characteristics, determine patterns, and generate hash values and regular expressions to create mitigation filters, allowing for the differentiation of attack traffic from legitimate traffic, thereby mitigating DDOS attacks while preventing the blocking of valid hosts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional DDOS mitigation systems block traffic based on volume thresholds, then attack traffic can be reduced, but legitimate traffic may be incorrectly blocked (overblocking)
Solution Approach 1:
The system changes the 'color' or characteristics of traffic analysis by examining specific HTTP response patterns (headers, status codes, timing) rather than treating all traffic uniformly. This allows differentiation between attack and legitimate traffic based on their distinct response signatures, reducing false positives while maintaining attack mitigation.
Solution Approach 2:
The system applies different analysis rules to different parts of the traffic flow. Instead of uniform blocking thresholds, it examines specific local characteristics of HTTP responses (headers, body patterns, timing sequences) to make granular decisions about each traffic flow, allowing legitimate traffic to pass while blocking attacks.
2Measurement precision
If traffic analysis examines detailed HTTP response characteristics, then accuracy in differentiating attack from legitimate traffic improves, but system complexity increases
Solution Approach 1:
The system segments the HTTP response analysis into distinct components: header examination, body pattern matching, timing analysis, and sequence detection. Each segment handles a specific aspect of traffic characterization, making the overall complex task manageable through modular processing stages that can be independently optimized.
Solution Approach 2:
The system introduces intermediary processing layers that translate raw HTTP responses into standardized traffic profiles. These intermediaries (pattern matchers, timing analyzers, sequence detectors) convert complex raw data into simplified characteristics that can be compared against known attack signatures, reducing the complexity of direct analysis.
3Object-affected harmful factors
If the system blocks traffic during suspected attacks, then network protection improves, but service availability for legitimate users may be disrupted
Solution Approach 1:
The system dynamically adjusts its blocking behavior based on real-time traffic pattern analysis. Rather than static blocking rules, it continuously monitors HTTP response characteristics and adapts its mitigation strategy, blocking only when attack patterns are detected and allowing normal traffic to pass during legitimate service conditions.
Solution Approach 2:
The system implements feedback mechanisms where blocked and permitted traffic outcomes are monitored and used to refine future blocking decisions. By analyzing the effectiveness of previous blocking actions and the characteristics of subsequent traffic, the system learns to improve its differentiation between attack and legitimate traffic, reducing false positives while maintaining protection.
Data Source
AI summary
A computer system and process for mitigating a Distributed Denial of Service (DDoS) attack to one or more protected computer networks by determining keywords and/or patterns in HyperText Transfer Protocol (HTTP) responses. Stored HTTP responses are analyzed to extract one or more HTTP characteristics for each stored HTTP response. One or more patterns having one or more keywords in each stored HTTP response is determined utilizing the extracted one or more HTTP characteristics for each stored HTTP response. A hash value is determined for each determined pattern, which is preferably stored in a hash structure accompanied by its respective determined HTTP characteristics. Each hash value accompanied by its respective determined HTTP characteristics is stored as a mitigation filter candidate if the hash value contains a determined pattern consisting of at least a predetermined percentage of all determined patterns stored in the hash structure. A determination is then made as to whether if each stored filter candidate contains an acceptable pattern, whereby filter candidates having acceptable patterns are removed from being a mitigation filter candidate. A regular expression is then generated for each stored filter candidate adaptable to be utilized as keywords in a DDOS mitigation filter.