Anomaly Detection Mixture Model for File System Security
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Solution Overview
Problem
File systems are susceptible to attacks such as malware, malicious encryption, and data deletion, which compromises data integrity and system reliability.
Innovation Solution
The implementation of a mixture model generated using the BIRCH algorithm for clustering and the expectation-maximization process to identify anomalous events on a file system, allowing for real-time response actions to prevent or mitigate such attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional file management systems are used, then file management functionality is provided, but the system is susceptible to attacks such as malware, data deletion, and malicious encryption
Solution Approach 1:
The system performs preliminary clustering of data points into normal behavior patterns before evaluating new events. By pre-establishing what constitutes normal file system behavior through the BIRCH algorithm and expectation-maximization process, the system can quickly identify and respond to anomalous events that deviate from these pre-defined patterns, thereby preventing attacks before they cause harm.
Solution Approach 2:
The system continuously monitors file system events, compares them against the clustered normal behavior patterns, and executes response actions when anomalies are detected. This closed-loop feedback mechanism allows the system to adapt to new threats by updating its understanding of normal behavior while maintaining protection against known attack patterns, thus improving reliability without increasing susceptibility to attacks.
2Measurement precision
If all data points are processed for anomaly detection, then detection accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The system segments the large dataset into clusters of normal behavior patterns using the BIRCH algorithm, which efficiently groups similar data points together. This segmentation allows the system to represent complex normal behavior with a manageable number of clusters, maintaining detection accuracy while significantly reducing the computational burden of processing individual data points in real-time.
Solution Approach 2:
The system extracts only the essential characteristics of normal behavior by clustering data points and representing them as condensed patterns. By taking out and retaining only the fundamental behavior patterns needed for anomaly detection, the system achieves high detection accuracy without processing every single data point, thus reducing processing time and resource consumption while maintaining precision.
3Manufacturing precision
If complex clustering algorithms are used, then cluster optimization is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary clustering using the BIRCH algorithm, which is designed to efficiently handle large datasets by maintaining a compact representation of data clusters. This preliminary action creates an optimized structure that can be further refined using expectation-maximization, achieving high cluster optimization without requiring computationally intensive processing of the entire dataset from scratch.
Solution Approach 2:
The system uses the expectation-maximization process to dynamically refine cluster parameters based on the preliminary clustering results. This dynamic approach allows the system to optimize clusters iteratively, improving precision while avoiding the need for static, overly complex algorithms. The dynamic refinement process adapts to the data characteristics, achieving optimal clustering with manageable computational complexity.
Data Source
AI summary
According to examples, an apparatus may include a memory on which is stored instructions that when executed by a processor, cause the processor to cluster a set of data points into a hierarchy of a plurality of clusters, in which each level of the hierarchy includes a different number of the plurality of clusters. The processor may also select a number of the plurality of clusters to be used in modeling behaviors of the plurality of clusters and for each cluster of the selected number of the plurality of clusters, determine a distribution type of the cluster. The processor may further merge the distribution types of the clusters to generate a mixture model, identify an event, evaluate the event based on the generated mixture model to determine whether the event is likely anomalous, and based on a determination that the event is likely anomalous, execute a response action.