# 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.
