Quick comparison and when to use which
At a glance
-
Linear regression → target is a number
Example security use → predict remediation days, expected bandwidth, 0 —> 100 risk score -
Logistic regression → target is a class
Example security use → spam vs not spam, phish vs not phish, DGA vs benign -
Decision trees → human readable if —> then rules, non linear patterns
Example security use → explainable email triage rules, login anomaly flags -
Naive Bayes → text and tokens at scale, high dimensional sparse features
Example security use → spam and phishing from word or character n grams, DGA detection -
SVMs → strong margins, works well in high dimensional spaces
Example security use → text and URL classification with linear kernel, dense feature cases with RBF
Comparison table
| Model | Best for | Strengths | Watch outs | Typical security uses |
|---|---|---|---|---|
| Linear regression | Predicting a continuous value | Simple, fast, interpretable coefficients | Assumes linearity and stable residuals, sensitive to outliers and multicollinearity | Risk scoring 0 —> 100, remediation time, bandwidth baseline |
| Logistic regression | Binary or one vs rest classification with linear log odds | Fast, explainable, good baselines, easy to calibrate | Needs feature engineering for non linear patterns | Spam vs not spam, phish vs not phish, DGA vs benign, suspicious login |
| Decision trees | Mixed feature types, need explanations and non linear rules | Human readable paths, handles interactions, few data assumptions | Overfits if too deep, unstable to small data changes unless pruned | Email triage rules, login anomaly rules, initial policy mining |
| Naive Bayes | Text and tokens, high dimensional sparse inputs | Very fast, strong baseline for text and URLs, low data needs | Independence assumption, probabilities often overconfident, vocabulary sensitive | Spam and phishing from tokens, DGA n gram models, quick URL checks |
| SVMs | High dimensional data, clear margin separation | Strong decision boundaries, robust with good C and gamma, linear kernel great for text | Needs scaling, tuning C and gamma, raw scores not probabilities without calibration | Text and URL classification linear, malware triage with dense features RBF |
How to choose in practice
-
Your target decides first
- Number → Linear regression
- Class → Logistic regression, Naive Bayes, Decision tree, SVM
-
Your features decide next
- Mostly text or character tokens high dimensional and sparse → start Naive Bayes and Linear SVM then compare Logistic regression
- Few dense numeric signals with curved boundaries → try Decision tree and RBF SVM
- Need clear explanations for analysts and audits → Decision tree or Logistic regression with feature importance
- Need a fast baseline with limited data → Naive Bayes for text, Logistic regression for tabular
-
Operational reality
- Tight latency and easy maintenance → Logistic regression or Naive Bayes
- Strong accuracy on tricky boundaries and you can afford tuning → SVM
- Policy style rules you can show on a ticket → Decision tree
-
Imbalance and thresholds
- Any classifier → tune threshold to match Precision —> Recall and SOC capacity
- Use PR AUC and calibration checks when classes are rare
Simple starting playbook
- Email and URL tasks → start Naive Bayes and Linear SVM → add Logistic regression with n grams → pick by PR AUC and maintenance ease
- Auth anomaly with a handful of features → start Decision tree for explainability → compare Logistic regression → if needed, RBF SVM
- Scoring problems like risk or time → Linear regression first → if residuals misbehave, engineer features or consider tree based regressors
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