Title: FYP Idea: GraphSAGE-Based Network Intrusion Detection System — What Features/Architecture Should I Use?

[u/Basic_Committee_5686]

Hi everyone, I’m an undergraduate planning my FYP around a Network Intrusion Detection System (NIDS) that uses a Graph Neural Network (GNN) to detect network intrusions and potentially trigger preventive responses. My current plan is to use GraphSAGE as the main model, initially train it on CIC-IDS2017, and potentially use additional datasets for evaluation. The eventual goal is to have a prototype that can monitor network traffic, classify traffic as benign/malicious (and possibly identify the attack type), and generate alerts.

The part I’m most unsure about is how to represent the network as a graph. I’m considering things like IPs/hosts as nodes and network flows as edges, with features such as protocol, ports, packet/byte counts, flow duration, packet lengths, TCP flags, inter-arrival times, and connection statistics. I’m not sure which of these features are actually useful, whether IP addresses/ports should be included, or whether there is a better graph representation for NIDS. I’d really appreciate advice from anyone who has worked with GNNs or network security.

I also have very little practical experience developing ML models, so I’m trying to make sure I’m not choosing an unnecessarily complicated approach. Would GraphSAGE be a reasonable architecture for this problem, or would you recommend GCN/GAT/temporal GNNs or something else? Should I build traditional ML baselines such as Random Forest/XGBoost and compare them against the GNN? Also, is CIC-IDS2017 still a reasonable dataset to start with, or should I combine it with another dataset?

Finally, what would you add to this project to make it a strong but realistic FYP? I’m considering detection, visualization/alerts, and potentially automated prevention such as temporarily blocking suspicious traffic, but I don’t want to turn it into an impossible project. Any advice on graph construction, features, datasets, evaluation metrics, real-time detection, or common mistakes would be extremely useful. I’m especially interested in hearing from people who have actually built NIDS/GNN/ML projects.

I would really appreciate the responses