Antigravity Agent
149 lines
5.1 KiB
Markdown
149 lines
5.1 KiB
Markdown
---
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id: wiki-2026-0508-anomaly-detection
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title: Anomaly Detection
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Outlier Detection, Novelty Detection, 이상 탐지]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [security, ml, monitoring, observability]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: applied
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tech_stack:
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language: Python
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framework: scikit-learn/PyOD/Prometheus
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---
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# Anomaly Detection
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## 매 한 줄
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> **"매 normal 의 boundary 를 학습하고 그 밖을 flag 한다."**. Anomaly detection 은 fraud, intrusion, equipment failure, log spike 등을 unsupervised 로 발견하는 매 core observability/security primitive. 2026 의 standard 는 Isolation Forest + LSTM-AE + transformer-based time-series (PatchTST, TimesNet).
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## 매 핵심
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### 매 Anomaly Type 3가지
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- **Point anomaly**: 매 single observation 이 outlier — credit card 단일 거래.
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- **Contextual anomaly**: 매 context 에서만 anomaly — 여름의 영하 온도.
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- **Collective anomaly**: 매 group 으로만 anomaly — DDoS 의 packet sequence.
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### 매 Algorithm Family
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- **Statistical**: z-score, MAD, Grubbs, EWMA — 매 univariate baseline.
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- **Distance-based**: kNN, LOF — 매 density 차이로 detect.
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- **Tree-based**: Isolation Forest, Extended IF — 매 high-dim 잘 작동.
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- **Reconstruction**: Autoencoder, VAE — 매 reconstruction error = anomaly score.
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- **Time-series DL**: LSTM-AE, Transformer (PatchTST 2024, TimesNet) — 매 SOTA 2026.
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- **One-class**: One-Class SVM, Deep SVDD — 매 normal-only training.
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### 매 응용
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1. **Fraud detection**: payment, account takeover.
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2. **Intrusion detection (IDS)**: network traffic anomaly.
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3. **Predictive maintenance**: vibration sensor, temp.
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4. **APM**: latency/error rate spike — Datadog Watchdog, New Relic.
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5. **Log anomaly**: unseen log template — DeepLog, LogBERT.
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## 💻 패턴
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### Isolation Forest baseline
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```python
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from sklearn.ensemble import IsolationForest
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import numpy as np
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# 매 contamination = expected anomaly fraction
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clf = IsolationForest(contamination=0.01, n_estimators=200, random_state=42)
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clf.fit(X_train)
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scores = -clf.score_samples(X_test) # 매 high score = more anomalous
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preds = clf.predict(X_test) # -1=anomaly, 1=normal
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```
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### LOF for density anomaly
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```python
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from sklearn.neighbors import LocalOutlierFactor
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lof = LocalOutlierFactor(n_neighbors=20, contamination=0.01, novelty=True)
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lof.fit(X_train)
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anomaly_score = -lof.score_samples(X_test)
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```
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### Autoencoder reconstruction error (PyTorch)
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```python
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import torch.nn as nn
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class AE(nn.Module):
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def __init__(self, d=64):
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super().__init__()
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self.enc = nn.Sequential(nn.Linear(d,32), nn.ReLU(), nn.Linear(32,8))
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self.dec = nn.Sequential(nn.Linear(8,32), nn.ReLU(), nn.Linear(32,d))
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def forward(self, x): return self.dec(self.enc(x))
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# 매 train on normal only — anomaly = high reconstruction error
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recon = model(x)
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score = ((x - recon) ** 2).mean(dim=1)
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```
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### EWMA streaming detector
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```python
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class EWMA:
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def __init__(self, alpha=0.1, k=3.0):
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self.alpha, self.k = alpha, k
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self.mu = self.var = None
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def step(self, x):
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if self.mu is None: self.mu, self.var = x, 1.0; return False
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z = abs(x - self.mu) / (self.var ** 0.5 + 1e-9)
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self.mu = self.alpha * x + (1 - self.alpha) * self.mu
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self.var = self.alpha * (x - self.mu)**2 + (1 - self.alpha) * self.var
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return z > self.k
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```
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### PyOD ensemble
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```python
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from pyod.models.iforest import IForest
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from pyod.models.lof import LOF
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from pyod.models.combination import average
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scores = np.column_stack([
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IForest().fit(X).decision_function(X),
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LOF().fit(X).decision_function(X),
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])
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ensemble_score = average(scores)
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```
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## 매 결정 기준
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| 상황 | Algorithm |
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|---|---|
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| Tabular, low-dim | Isolation Forest |
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| Tabular, density 중요 | LOF |
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| Time-series univariate | EWMA / Prophet |
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| Time-series multivariate | LSTM-AE / PatchTST |
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| Image | PaDiM / PatchCore |
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| Log sequence | LogBERT / DeepLog |
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**기본값**: 매 Isolation Forest baseline → 부족시 deep model.
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## 🔗 Graph
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- 부모: [[Statistics & Data Analysis]]
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- 변형: [[Inferential-Statistics]]
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- 응용: [[Malware-Analysis]] · [[Deepfake-Detection]] · [[Logging_and_Error_Handling]]
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- Adjacent: [[경고 피로 (Alert Fatigue)]]
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## 🤖 LLM 활용
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**언제**: log template 추출, anomaly explanation generation, false-positive triage.
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**언제 X**: 매 high-frequency stream 의 inner-loop scoring (use specialized model).
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## ❌ 안티패턴
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- **Threshold hard-coding**: 매 environment drift 시 무용지물 — adaptive threshold 사용.
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- **Class imbalance 무시**: 매 anomaly 0.1% 일 때 accuracy 99.9% 무의미 — PR-AUC.
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- **Train on contaminated data**: 매 anomaly 가 train set 에 섞이면 mask 됨.
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- **Alert fatigue**: 매 raw score 그대로 alert 면 dev 가 무시.
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## 🧪 검증 / 중복
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- Verified: Liu et al. 2008 (Isolation Forest); PyOD docs; Nie et al. 2023 (PatchTST).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — algorithm taxonomy + PyOD/AE patterns |
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