RNN Anomaly Detection for System Call Sequence Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing anomaly detection systems for computing system resources are limited in capturing long-term system call dependencies and phrase-level features, making it difficult to detect complex and unknown system attacks, as they rely on fixed window sizes and fail to evaluate interactions between system calls and their arguments effectively.
Innovation Solution
A recurrent neural network (RNN) with long short-term memory (LSTM) or gated recurrent unit (GRU) cells is trained to predict system call sequences, incorporating a temporal control layer to account for elapsed time intervals, allowing it to detect anomalies by learning the semantics of individual system calls, their arguments, and interactions across arbitrary sequence lengths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed window sizes are used for anomaly detection, then device complexity is reduced, but the ability to capture long-term system call dependencies deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from fixed window sizes to dynamic sequence processing using RNNs and LSTMs. The system adapts its analysis window dynamically based on the sequence length requirements of different system calls, allowing it to capture long-term dependencies without being constrained by predetermined fixed windows. This dynamic approach enables the model to automatically adjust the temporal scope of analysis for each detection task.
Solution Approach 2:
The patent introduces a new dimensional perspective by transforming the traditional fixed-window temporal analysis into a sequential processing dimension using RNN architecture. Instead of analyzing fixed-size windows in one dimension, the system processes system call sequences in a temporal dimension that can extend arbitrarily long, capturing dependencies across the entire sequence rather than being limited to fixed segments.
2Ease of operation
If traditional anomaly detection methods are used, then ease of operation is maintained, but the ability to detect complex and unknown system attacks deteriorates
Solution Approach 1:
The patent replaces traditional mechanical anomaly detection methods (rule-based systems, statistical thresholds) with a neural network-based semantic understanding system. The RNN and LSTM models learn the semantic meanings of system call sequences automatically from training data, substituting manual rule configuration with automated pattern recognition. This substitution maintains ease of operation while dramatically improving detection accuracy for complex and unknown attacks.
Solution Approach 2:
The patent changes the fundamental parameters of anomaly detection from static thresholds and fixed rules to dynamic, learned representations. The system transforms system calls into vector embeddings and uses learned parameters from training to determine anomaly boundaries, allowing the detection criteria to adapt to new attack patterns while maintaining operational simplicity through automated learning.
3Speed
If system calls are processed individually, then processing speed is improved, but the ability to evaluate interactions between system calls and their arguments deteriorates
Solution Approach 1:
The patent merges individual system call processing with contextual interaction analysis by combining multiple system calls and their arguments into unified vector representations. The RNN architecture processes sequences of system calls while maintaining their interrelationships, and the attention mechanism further merges relevant information from different parts of the sequence to evaluate interactions comprehensively without sacrificing processing efficiency.
Data Source
AI summary
Mechanisms are provided for detecting abnormal system call sequences in a monitored computing environment. The mechanisms receive, from a computing system resource of the monitored computing environment, a system call of an observed system call sequence for evaluation. A trained recurrent neural network (RNN), trained to predict system call sequences, processes the system call to generate a prediction of a subsequent system call in a predicted system call sequence. Abnormal call sequence logic compares the subsequent system call in the predicted system call sequence to an observed system call in the observed system call sequence and identifies a difference between the predicted system call sequence and the observed system call sequence based on results of the comparing. The abnormal call sequence logic generates an alert notification in response to identifying the difference.