Anomaly detection

Anomalies within local network data traffic will be detected with ML methods, e.g. Graph Neural Networks (GNN).

An anomaly-based intrusion detection system (IDS) monitors various system events in order to detect security issues and classifying it as either normal or anomalous. For example, a Host IDS (HIDS) monitors Operating System events such as file access patterns to detect malicious activity while a Network IDS (NIDS) monitors network traffic to detect issues. Recent research in NIDS have mostly focused on anomaly-based systems, in particular on using various machine learning and deep learning techniques.

Anomalies within the local network data traffic will be detected with the usage of Graph Neural Networks (GNN).

Nearest neighbour:
Benchmark datasets:

Relationships with other web-repo artefacts
Improvement Classification
Metrics to evaluate AI/ML algorithms, Number of malicious attacks and faults detected
Randomness and security assessment process performance
Open source - Goals

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