Network Traffic Anomaly Detection With Dynamic Flow Scoring
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Solution Overview
Problem
Existing network traffic data analysis systems struggle with adaptability to dynamic changes in network behavior, leading to high false positives and negatives, require extensive manual configuration, and lack scalability and efficiency in processing large volumes of data, failing to detect emerging threats effectively.
Innovation Solution
A computer-implemented system utilizing a flow aggregator, flow feature generation, anomaly detection, dynamic score, and calibration subsystems, employing deep-learning models and time-series methods to analyze network traffic data, dynamically adjusting thresholds, and generating alerts for anomalous patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static threshold-based approaches are used for anomaly detection, then the system is simple to implement, but it fails to adapt to dynamic changes in network behavior leading to high false positives and negatives
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously learning normal network behavior patterns and adapting detection thresholds based on changing traffic conditions. The system uses rolling statistical calculations and machine learning models that automatically adjust to new network states, transforming the static detection mechanism into a dynamic one that evolves with network behavior.
Solution Approach 2:
The system performs self-learning and self-adjustment by automatically analyzing network traffic patterns and updating its detection parameters without manual intervention. The machine learning models continuously train on incoming data, enabling the system to autonomously adapt to new threats and changing network conditions, reducing reliance on manual configuration and maintenance.
2Ease of operation
If rule-based systems are used for anomaly detection, then the detection logic is clear and interpretable, but extensive manual configuration and maintenance is required making them cumbersome to manage
Solution Approach 1:
The system automatically generates detection rules by learning from network traffic data using machine learning algorithms. Instead of requiring manual rule creation and updates, the system autonomously identifies patterns, generates detection logic, and maintains itself through continuous learning, dramatically reducing the time and effort required for configuration and maintenance.
Solution Approach 2:
The patent replaces manual rule-based systems with automated machine learning-based detection. The mechanical process of manually creating, updating, and maintaining detection rules is substituted with an automated intelligent system that learns patterns from data and generates detection logic autonomously, freeing operators from tedious manual work.
3Productivity
If traditional analysis methods are used, then the system is computationally efficient, but it struggles to keep pace with the sheer volume and complexity of data generated by modern networks
Solution Approach 1:
The patent divides network traffic analysis into multiple stages and layers, processing different types of data through specialized algorithms. By segmenting the analysis pipeline into flow-level aggregation, feature extraction, anomaly scoring, and detailed inspection, the system efficiently handles large volumes of data by processing only relevant information at each stage, reducing overall computational burden.
Solution Approach 2:
The system applies anomaly detection selectively rather than analyzing every packet in detail. It uses sampling techniques and focuses computational resources on suspicious or anomalous traffic patterns, performing full analysis only when necessary. This partial action approach maintains high productivity while conserving computational energy by avoiding exhaustive analysis of all network data.
4Reliability
If existing outlier detection systems are used, then the system can detect anomalies based on reconstruction error, but it lacks the capability to analyze network traffic patterns for proactive security measures
Solution Approach 1:
The patent creates a multi-functional system that combines outlier detection with network traffic pattern analysis, flow feature generation, and security threat detection. The system performs multiple functions including reconstructing normal traffic patterns, identifying anomalies through error analysis, generating flow features for detailed inspection, and providing proactive security alerts, making it universally applicable to various network security challenges.
Solution Approach 2:
The system introduces flow feature generation and pattern analysis as intermediary steps between raw network traffic and anomaly detection. These intermediary components extract meaningful characteristics from traffic data, enabling the system to detect not just statistical outliers but also security-relevant patterns, thereby enhancing both detection accuracy and proactive security capabilities.
Data Source
AI summary
A system for detecting anomalous network patterns based on analyzing network traffic data and method thereof are disclosed. The system comprises a flow aggregator subsystem, a flow feature generation subsystem, an anomaly detection subsystem, a dynamic score subsystem, and a calibration subsystem. The system is configured to receive the network traffic data from one or more network endpoints for aggregating the network traffic data into network flow data. The system is configured to generate one or more flow features for each packet associated with the aggregated network traffic data based on a rolling window-based analysis of the one or more attributes. The system is configured to analyze the one or more flow features by utilizing one or more deep-learning models to detect the one or more anomalous network patterns.