Statistical Traffic Pattern Detection for DDoS Mitigation
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Solution Overview
Problem
Current DDoS attack mitigation methods are inadequate as they fail to distinguish between legitimate and malicious traffic, do not allow for flexible policy configuration, and can degrade system performance, making them ineffective in handling large-scale attacks.
Innovation Solution
A method and system that uses statistical analysis to detect patterns in network traffic by generating value combinations from packet headers, storing them in a results table with counters, and sorting to identify frequent patterns, allowing for real-time differentiation between legitimate and malicious traffic without degrading system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard mitigation policies discard all packets destined to victim server, then attack traffic is blocked, but legitimate user traffic is also blocked and system performance degrades
Solution Approach 1:
The patent segments traffic analysis into two distinct paths: a fast path for legitimate traffic that requires minimal processing, and a slow path for suspicious traffic that undergoes comprehensive statistical analysis. This segmentation allows the system to maintain high performance for normal operations while applying thorough mitigation only when necessary, resolving the contradiction between reliable attack blocking and system performance.
Solution Approach 2:
The patent introduces statistical analysis mechanisms and pattern detection algorithms as intermediaries between incoming traffic and the mitigation action. These intermediaries analyze traffic patterns, generate statistical profiles, and determine whether packets are malicious before blocking, thereby preventing premature discarding of legitimate traffic while still achieving effective attack mitigation.
2Measurement precision
If comprehensive packet analysis is performed to distinguish legitimate from malicious traffic, then accurate detection is achieved, but system resources are consumed and performance degrades
Solution Approach 1:
The patent applies partial analysis to most traffic by maintaining statistical counters for all packets and using pre-computed patterns for rapid matching. Comprehensive analysis is performed only on packets that exhibit suspicious characteristics or fail initial pattern matching. This partial action approach achieves accurate differentiation while minimizing overall resource consumption by avoiding excessive analysis of legitimate traffic.
Solution Approach 2:
The patent performs preliminary statistical analysis by maintaining running counters of packet characteristics (source IP, destination IP, protocol, ports, etc.) and pre-computing traffic patterns before attacks occur. This preliminary action creates a baseline of normal traffic behavior that enables rapid classification during attacks without requiring resource-intensive real-time analysis of every packet, thus achieving accurate differentiation with minimal resource consumption.
3Ease of manufacture
If fixed mitigation policies are used, then implementation is simple, but flexibility to handle varied attack patterns is limited
Solution Approach 1:
The patent implements dynamic mitigation policies that automatically adapt to varying attack patterns through statistical analysis. The system continuously monitors traffic characteristics, updates statistical counters, and adjusts mitigation rules based on detected patterns. This dynamic approach maintains implementation simplicity through automated rule generation while achieving high flexibility in handling diverse attack types including SYN floods, UDP floods, and application-layer attacks.
4Measurement precision
If real-time traffic analysis is performed to detect DDoS attacks, then accurate detection is achieved, but control plane CPU is overloaded
Solution Approach 1:
The patent segments the processing architecture into data plane and control plane functions. The data plane handles high-speed packet forwarding and maintains statistical counters with minimal CPU involvement. The control plane performs higher-level pattern matching and policy decision-making based on aggregated statistics. This segmentation enables accurate attack detection while preventing control plane CPU overload by keeping the bulk of processing in the data plane.
Solution Approach 2:
The patent uses copied and cached statistical data structures that can be rapidly replicated and distributed across processing elements. Traffic patterns and statistical counters are maintained in optimized data structures that enable fast copying and comparison without requiring intensive control plane computation, thereby achieving accurate detection while preserving control plane processing capacity.
Data Source
AI summary
A method for detecting patterns using statistical analysis is provided. The method includes receiving a subset of structured data having a plurality of fields. A plurality of value combinations is generated for the plurality of fields using a statistical combination function. Each combination of the generated plurality of value combinations is stored as a separate entry in a results table. The entry in the results table includes a counter associated with the stored combination. A value of the counter is incremented for every occurrence of the stored combination in the generated plurality of value combinations. The results table is sorted based on the counters' values and based on a number of fields in each combination. One or more entries having highest counter values are identified in the results table.