GNN vs Traditional ML for Security: Why Graphs Win

Traditional machine learning for security typically uses one of these approaches:

Signature-Based: Not really ML, but still common. Matches known patterns. Fast but misses anything novel.

Statistical Anomaly Detection: Establishes baselines for metrics (login counts, data volumes, connection frequencies) and alerts on deviations. Simple but generates many false positives.

Supervised Classification: Trains models on labeled examples of "good" and "bad" events. Effective for known attack patterns but struggles with novel techniques.

Sequence Models (RNNs, LSTMs): Analyzes event sequences over time. Better context than single-event analysis but treats events independently.

The Common Limitation: All these approaches process events as independent data points or simple sequences. They might consider "user A accessed file B at time T" but struggle with "user A accessed file B on server C which connects to database D in network segment E."

Relationships get flattened into features, losing critical information.

Graph Neural Networks represent your infrastructure as it actually is: a connected system.

Nodes: Users, devices, applications, files, IP addresses—any entity in your environment

Edges: Relationships between entities—logins, data flows, network connections, process executions

Node Features: Attributes of each entity—OS type, user role, file sensitivity, historical behavior

Edge Features: Attributes of relationships—data volume, protocol, timestamp, duration

GNNs learn by passing information between connected nodes. Each node builds understanding not just of itself but of its neighborhood. After several rounds of message passing, the model understands multi-hop relationships—exactly what's needed to detect sophisticated attacks.

Let's compare how traditional ML and GNNs would handle a lateral movement attack:

The Attack: An attacker compromises a workstation, harvests credentials, authenticates to a server, and pivots to a database.

Traditional ML View:
• Login from workstation: Normal user behavior ✓
• Authentication to server: User has access ✓
• Database query: Valid database user ✓

Each event passes individual inspection. The attack succeeds.

GNN View:
• Path exists: Workstation → Server → Database
• This path never occurred before for this user
• Similar paths were created by other compromised accounts
• Pattern matches known lateral movement techniques

The attack is detected because GNNs see the chain, not just the links.

Research and real-world deployments consistently show GNN advantages:

Detection Accuracy:
• Traditional ML: 70-85% detection rates on benchmark datasets
• GNNs: 90-99% detection rates on same datasets
• The gap widens for sophisticated attacks involving multiple systems

False Positive Rates:
• Traditional ML: High false positive rates from lack of context
• GNNs: 60-80% fewer false positives due to relationship understanding

Zero-Day Detection:
• Traditional ML: Poor—relies on known patterns
• GNNs: Good—detects anomalous relationships even for novel techniques

Multi-Stage Attacks:
• Traditional ML: Often misses connections between stages
• GNNs: Native capability to see attack chains

Security is fundamentally about relationships:

Authentication: Who is connecting to what?
Authorization: What relationships are permitted?
Lateral Movement: How do attackers traverse the network?
Data Exfiltration: What paths does data take?
Command and Control: What external relationships exist?

Traditional ML must encode relationships as features—losing fidelity and requiring manual engineering. GNNs learn relationship patterns automatically, capturing nuances that feature engineering misses.

This isn't theoretical. Attackers explicitly think in terms of relationships. Red team kill chains, MITRE ATT&CK techniques, and adversary playbooks all describe sequences of connected actions. Defending with relationship-blind tools is fighting with one eye closed.

GNNs aren't universally superior. Traditional ML remains appropriate for:

Single-System Analysis: Malware behavior on an individual endpoint. File classification. URL reputation.

Simple Anomaly Detection: Alerting on unusual login times or data volumes when relationship context isn't needed.

Resource-Constrained Environments: GNNs require more compute than simpler models. For edge devices or real-time processing at extreme scale, traditional approaches may be necessary.

Highly Interpretable Requirements: When you need to explain exactly why something was flagged, simpler models may be preferable for regulatory or legal contexts.

The best security architectures often combine both: traditional ML for single-entity analysis, GNNs for relationship-aware detection.

Moving from traditional ML to GNNs involves several factors:

Data Preparation: Traditional ML needs features extracted from events. GNNs need events structured as graphs—different pipelines, different schemas.

Model Complexity: GNN architectures are more complex than traditional classifiers. Training requires more expertise.

Computational Requirements: Graph operations scale with edges. Large networks require efficient implementations or sampling strategies.

Interpretability: GNN decisions can be explained through graph visualization—showing why entities are suspicious based on their connections—but this requires tooling.

These challenges are why purpose-built platforms like Hypergraph exist: we've solved the hard problems so you get GNN benefits without building infrastructure from scratch.