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 # README: Anomaly Detection in Graph Data Using Isolation Forest
 
 This documentation supports the Python implementation of a machine learning-based anomaly detection system, particularly tailored for graph streams. Our approach  addresses the detection of Advanced Persistent Threats (APTs) by harnessing both structural and temporal characteristics of graph-based data, as described in our recent publication in Applied Intelligence (Megherbi et al., 2024). This method leverages hashing for compact data representation and a dynamic learning model, enabling efficient and incremental anomaly detection with minimal memory usage.
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 ## Licensing
 
-This project and its contents are provided under the MIT License, details of which can be found in the accompanying LICENSE file.
+This project and its contents are provided under the MIT License.