Why Accuracy Can Be Misleading in Cybersecurity
When evaluating machine learning (ML) model performance, Accuracy often commands attention. But in cybersecurity, that metric can be misleading. A recent evaluation using a real-world dataset from one of our customers illustrates why.
A Real-World Test Across Three Platforms
We tested three models trained on the same dataset using:
- AWS SageMaker
- Azure ML Studio
- NovaceneAI Platform
At first glance, the traditional platforms appear to lead:
| Azure ML Studio | AWS SageMaker | NovaceneAI Platform |
|---|---|---|
| 96.3% | 95.3% | 82.6% |
Here’s the catch: the dataset is highly imbalanced — roughly 15:1 — and the cost of misclassification isn’t symmetrical. False positives are inconvenient. False negatives carry real risk.
When Finding the Threat Matters Most
So what happens when we evaluate using Recall — the metric that measures how many true threats are correctly identified?
| Azure ML Studio | AWS SageMaker | NovaceneAI Platform |
|---|---|---|
| 50.4% | 66.8% | 88.2% |
Despite having lower overall accuracy, the model trained on the NovaceneAI Platform detects far more real threats. While this may increase false positives, most security teams consider that a worthwhile trade-off. In high-stakes environments, missing a real threat is far more damaging than investigating a few extra alerts.
Flexibility to Match Your Risk Tolerance
This isn’t a one-size-fits-all scenario. That’s why the NovaceneAI Platform lets users tune performance based on their operational priorities:
- Want fewer false alarms? Optimize for Precision
- Need to catch every threat? Optimize for Recall
- Looking for balance? Use F1 Score
Performance improves as more data becomes available. This evaluation reflects just one snapshot — but the trend is clear: when the cost of a miss is high, Accuracy alone isn’t enough.
Analyst Input Drives Ongoing Model Optimization
With NovaceneAI, security analysts can contribute directly to model improvement — no data science expertise required. When a misclassification is spotted, analysts can flag and correct it within the platform. These corrections don’t just sit in a log — they’re used to retrain the model, driving ongoing improvement with every interaction. The more your team engages with the system, the smarter it becomes.
Curious how your models stack up when it matters most? Let’s talk.
