Automated Traffic Variance Finder for Attack Pattern Detection
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Solution Overview
Problem
Online service providers face challenges in detecting and identifying computing attacks, such as HTTP smuggling and DDoS attacks, due to the complexity of malicious network traffic logs and the difficulty in determining the scale and sources of these attacks, which can lead to increased risk and fraud.
Innovation Solution
A method is introduced to generate a log signature based on variance and spread analysis of malicious network traffic logs, allowing for the identification of similar logs from the same or different sources, using a weighted average of variance and spread values for each data field, and employing machine learning models to adjust weights for improved detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If service providers manually analyze malicious network traffic logs to identify attack patterns, then detection accuracy may be maintained, but the time and resources required increase significantly
Solution Approach 1:
The system enables automated self-analysis of network traffic logs through machine learning models that automatically identify attack patterns, calculate variance metrics, and generate log signatures without requiring manual intervention, thereby maintaining detection accuracy while dramatically reducing analysis time
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational systems including machine learning models (random forest, gradient boosting, neural networks) that process log data, calculate variance and spread metrics, and generate signatures algorithmically, eliminating the time-consuming manual review process
2Reliability
If service providers implement comprehensive monitoring of all network traffic logs to identify attack sources, then the scale and sources of attacks can be identified, but the complexity of processing and analyzing all logs increases
Solution Approach 1:
The system extracts only the most relevant features from comprehensive log data including IP address variance, geographic location spread, and temporal patterns, rather than processing all log attributes equally. This extraction of key discriminative features maintains attack source identification reliability while reducing processing complexity
Solution Approach 2:
The patent applies different analysis methods and weighting to different log fields based on their local characteristics - for example, using variance analysis for IP addresses, geographic clustering for location data, and temporal analysis for timing patterns. This localized approach to data quality assessment improves identification reliability without requiring uniform complex processing of all fields
3Measurement precision
If service providers use traditional signature-based detection methods, then known attack patterns can be identified, but new or variant attacks may go undetected
Solution Approach 1:
The system dynamically adapts its detection approach by using machine learning models that can identify both known attack patterns through traditional signature matching and novel attacks through anomaly detection based on variance metrics. The model weights and thresholds are dynamically adjusted based on learned patterns, enabling flexibility to detect both established and emerging threats
Solution Approach 2:
The patent creates a universal log signature generation system that works across multiple attack types (DDoS, HTTP smuggling, fraud) and multiple machine learning algorithms (random forest, gradient boosting, neural networks). This multi-functional approach allows the same core methodology to detect diverse attack patterns, enhancing both precision and adaptability
4Measurement precision
If service providers analyze detailed variance and spread metrics for each log field, then more accurate attack identification is achieved, but the computational resources and processing time increase
Solution Approach 1:
The system performs variance and spread analysis selectively on the most critical log fields (IP address, geographic location, user agent) rather than uniformly analyzing all fields. This partial action approach focuses computational resources on high-impact metrics that provide the greatest discrimination power, achieving accurate attack identification with reduced resource consumption
Solution Approach 2:
The patent transforms detailed continuous variance and spread metrics into discrete log signature components with optimized weightings. By changing the parameter representation from fine-grained continuous values to weighted categorical signatures, the system maintains identification accuracy while reducing the computational burden of storing and comparing detailed metrics
Data Source
AI summary
There are provided systems and methods for identifying patterns in computing attacks through an automated traffic variance finder. A service provider, such as an electronic transaction processor for digital transactions, may determine network traffic logs caused or generated by malicious web traffic and network communications, such as during a computing attack by a bad actor. The service provider may generate a log signature for the network traffic log based on a variance or uniqueness of the network traffic logs IP address from other network traffic logs for each field in the network traffic log over a time period, and a spread in the commonality of the network traffic log with other network traffic logs. An aggregate score for each field may be determined based on the variance and the spread. Once determined, the log signature may be used to identify other network traffic logs through a search function.