Context-Aware Log Embedding for Unknown Anomaly Detection
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Solution Overview
Problem
Existing network security systems struggle with detecting unknown malicious activities in network flows due to the reliance on rule- or signature-based techniques, which are difficult to generalize and require human expertise, and existing log analysis tools lack contextual understanding and correlation capabilities, leading to inefficiencies and false positives.
Innovation Solution
A predictive recurrent neural network (RNN) is used to contextually encode sparse feature vectors from network packets and log messages into dense vectors, leveraging graph embedding and unsupervised training to detect anomalies by analyzing sequence patterns and relationships within log traces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule- or signature-based techniques are used for malicious activity detection, then known malicious activities can be detected, but the system cannot detect unknown malicious activities and requires significant human expertise
Solution Approach 1:
The patent replaces rule-based mechanical detection systems with a neural network-based learning system. The neural network automatically learns detection patterns from training data, eliminating the need for manual rule creation and enabling detection of both known and unknown malicious activities through learned representations rather than predefined signatures
Solution Approach 2:
The system performs self-training by automatically learning from log data without requiring continuous human intervention for rule updates. The neural network autonomously improves its detection capabilities by processing training examples and adjusting its internal parameters, reducing dependency on human security experts for system maintenance
2Ease of operation
If features are extracted from individual log messages, then processing is simplified, but contextual information and inter-relationships between log messages are lost
Solution Approach 1:
The patent combines multiple individual log message features into unified contextual representations. The neural network processes sequences of log messages together, merging their features while preserving temporal and contextual relationships, thereby maintaining both processing efficiency and contextual information through integrated feature representations
3Measurement precision
If manual log analysis is performed, then expert knowledge can be applied, but the process becomes unmanageable with large volumes of log data
Solution Approach 1:
The patent replaces manual log analysis with an automated neural network system that processes log data at machine speed. The neural network maintains expert-level detection accuracy by learning from training data while achieving high throughput by automatically processing large volumes of logs without human intervention
Solution Approach 2:
The system creates learned representations (embeddings) of log messages that capture essential patterns. These copied representations can be efficiently processed and compared, enabling high-speed analysis that preserves the analytical capabilities of expert review while achieving automated scalability
4Ease of manufacture
If static detection tools are used, then implementation is straightforward, but the tools cannot adapt to new types of malicious behavior over time
Solution Approach 1:
The patent implements a dynamic detection system using neural networks that can adapt to new threats. The system transitions from static rule-based detection to dynamic learning-based detection, where the model continuously improves by processing new data and adjusting its parameters, enabling adaptation to evolving malicious behaviors while maintaining straightforward deployment through pre-trained models
Data Source
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AI summary
Techniques are provided herein for contextual embedding of features of operational logs or network traffic for anomaly detection based on sequence prediction. In an embodiment, a computer has a predictive recurrent neural network (RNN) that detects an anomalous network flow. In an embodiment, an RNN contextually transcodes sparse feature vectors that represent log messages into dense feature vectors that may be predictive or used to generate predictive vectors. In an embodiment, graph embedding improves feature embedding of log traces. In an embodiment, a computer detects and feature-encodes independent traces from related log messages. These techniques may detect malicious activity by anomaly analysis of context-aware feature embeddings of network packet flows, log messages, and/or log traces.