RNN Contextualizes Sparse Log Vectors for Anomaly Detection
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Solution Overview
Problem
Current network security systems face challenges in detecting malicious activities due to the limitations of rule- or signature-based techniques, which struggle with unknown threats and require significant human expertise, and existing log analysis tools fail to effectively correlate log messages for anomaly detection, leading to false positives and loss of contextual information.
Innovation Solution
The implementation of a predictive recurrent neural network (RNN) that contextualizes sparse feature vectors from log messages into dense vectors, enabling the detection of anomalous network flows by analyzing sequence patterns and improving feature embedding through graph embedding techniques, thereby enhancing the detection of malicious activities.
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 threats and requires significant human expertise
Solution Approach 1:
The system uses unsupervised autoencoder neural networks that automatically learn normal network traffic patterns without human-labeled training data. The model self-adjusts to detect anomalies representing unknown threats, eliminating the need for continuous manual rule updates while maintaining high detection accuracy
Solution Approach 2:
The patent transforms the detection approach by changing from fixed rule-based parameters to dynamic learned representations. The autoencoder learns optimal feature representations of normal traffic during training, and detection sensitivity is adjusted by monitoring reconstruction error thresholds, enabling adaptation to both known and unknown threats
2Loss of information
If manual log analysis is performed, then contextual information can be examined, but the process is time-consuming and prone to human error
Solution Approach 1:
The patent replaces manual mechanical log analysis with an automated neural network system. The autoencoder mechanically processes log data through learned transformations, detecting anomalies based on reconstruction errors without human intervention, thus retaining contextual information while eliminating time loss and human error
Solution Approach 2:
The autoencoder acts as an intermediary between raw log data and security analysts. It transforms high-dimensional log data into compressed representations and highlights only the anomalous portions, preserving contextual information while reducing the time required for analysis by filtering out normal traffic patterns
3Device complexity
If features are extracted from individual log messages, then processing is simplified, but inter-relationships between log messages are ignored and contextual information is lost
Solution Approach 1:
The patent merges multiple individual log message features into a unified sequence input for the autoencoder. The neural network processes logs as sequential data, combining features from multiple messages while preserving their inter-relationships and contextual information through learned representations
4Adaptability or versatility
If more log data is analyzed to improve detection accuracy, then unknown threats can be detected, but the volume of data makes manual analysis futile
Solution Approach 1:
The patent replaces manual mechanical analysis of large log volumes with automated neural network processing. The autoencoder efficiently handles high-dimensional sequential log data through parallel computation, detecting unknown threats while maintaining high productivity that is impossible for human analysts
Data Source
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.