Dimensionality Reduction for Network Anomaly Detection
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Solution Overview
Problem
Managed Detection and Response (MDR) services face challenges in efficiently detecting cyberattacks due to the manual analysis of large volumes of data, lack of labeled data for anomaly detection, high dimensionality of network behavior data, and the need for rapid adaptation to evolving attack signals.
Innovation Solution
A machine learning anomaly detection system that implements a pipeline with pre-processing, dimensionality reduction, and unsupervised machine learning models to identify outliers in hunt data, reducing the need for manual labeling and enhancing the speed and accuracy of threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data analysis is used in MDR services, then security experts can detect cyberattacks, but the process becomes extremely slow and tedious
Solution Approach 1:
The patent replaces manual mechanical analysis by security experts with automated machine learning systems. The system uses unsupervised learning models to automatically analyze network metadata and detect anomalies, substituting the mechanical human analysis process with computational algorithms that operate faster and without fatigue.
Solution Approach 2:
The system enables self-service anomaly detection through unsupervised learning models that automatically identify patterns and outliers in data without requiring continuous human intervention or labeled training data. The models autonomously process metadata and generate detection results, making the system self-sufficient in the detection task.
2Adaptability or versatility
If human analysts manually examine large volumes of data, then they can identify compromise signals, but they cannot easily identify events associated with novel types of attacks
Solution Approach 1:
The unsupervised learning models continuously adapt to new attack patterns by autonomously learning from incoming data streams without requiring retraining on labeled examples. The systems self-adjust their detection parameters and patterns, enabling them to identify novel attack types while maintaining detection sensitivity through automatic pattern recognition.
3Measurement precision
If high-dimensional network behavior data is analyzed, then comprehensive attack detection is possible, but the complexity of the data processing increases
Solution Approach 1:
The system extracts and focuses on the most relevant features and patterns from high-dimensional network metadata using unsupervised learning techniques. By automatically identifying and extracting salient features that indicate anomalies, the system reduces processing complexity while maintaining comprehensive detection capability across multiple dimensions of network behavior.
4Measurement precision
If more labeled data is used for training anomaly detection models, then detection accuracy improves, but the requirement for manual labeling increases
Solution Approach 1:
The system uses unsupervised learning models that do not require manually labeled training data. The models autonomously learn normal behavior patterns and identify anomalies by detecting deviations from these patterns, eliminating the need for time-consuming manual labeling while maintaining detection accuracy through self-directed learning from raw metadata.
Data Source
AI summary
An anomaly detection system is disclosed capable of reporting anomalous processes or hosts in a computer network using machine learning models trained using unsupervised training techniques. In embodiments, the system assigns observed processes to a set of process categories based on the file system path of the program executed by the process. The system extracts a feature vector for each process or host from the observation records and applies the machine learning models to the feature vectors to determine an outlier metric each process or host. The processes or hosts with the highest outlier metrics are reported as detected anomalies to be further examined by security analysts. In embodiments, the machine learnings models may be periodically retrained based on new observation records using unsupervised machine learning techniques. Accordingly, the system allows the models to learn from newly observed data without requiring the new data to be manually labeled by humans.