What is k Nearest Neighbors

k nearest neighbors is a simple method that predicts using the k most similar past examples. There is no training phase beyond storing data.

Why security teams like it

  • Extremely simple and intuitive
  • Good teaching baseline to sanity check other models
  • Can work for small feature sets where distance is meaningful

Core idea in one line

Similarity votes decide
new sample —> find k closest examples —> vote for class or average for a number

Small security example

Goal suspicious login or normal
Features hour bin, geo distance from last login, device novelty, failed attempt count
How it works look at the k most similar past logins and vote suspicious or normal.

Training workflow

  1. Engineer low count, well scaled features
  2. Scale features to comparable ranges
  3. Choose a distance metric typically Euclidean or cosine
  4. Tune k with time aware validation
  5. Pick threshold for action using neighbor vote proportion

Evaluation that matches reality

  • Use Precision, Recall, F1, PR AUC
  • Validate on future windows to reflect real use
  • Slice by user and tenant because identities can dominate neighbors

Overfitting and underfitting

  • k too small memorizes noise overfits
  • k too large oversmooths underfits
  • Fix by tuning k, scaling features, and limiting to a sensible feature set

Common hyperparameters

  • k number of neighbors
  • metric distance measure Euclidean, cosine, Manhattan
  • weights uniform or distance weighted votes
  • algorithm for nearest neighbor search kd tree, ball tree, brute

Security focused testing checklist

  • Ensure feature scaling at train and inference
  • Test monotonicity do reasonable changes move neighbors as expected
  • Evaluate sensitivity to k and metric choices
  • Guard memory and latency budgets nearest neighbor search can be heavy
  • Validate on future time slices to avoid optimistic scores

Threats and mitigations

  • Feature gaming attacker crafts inputs close to benign neighbors
    • Mitigate add robust features, require secondary checks for low margin votes
  • Data poisoning attackers insert crafted neighbors into the store
    • Mitigate curate the memory, age out data, verify labels
  • Concept drift old neighbors become stale
    • Mitigate rolling windows and periodic rebuilds

Takeaways

kNN is a simple, teachable baseline great for small, well scaled feature spaces. Use it to sanity check pipelines, but prefer forests or boosting for scale and robustness on complex security data.


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