feat(bot-detector): implement 8 state-of-art improvements
- EIF: Extended Isolation Forest via isotree (fallback to sklearn IF) - Benford's Law deviation feature on inter-request timing - Lag-1 autocorrelation feature for cadence analysis - Validation gate: reject model if val_anomaly_rate > 20% - Feature pruning: remove variance < 1e-6 features before training - Quantile drift: replace N(μ,σ) synthetic with quantile interpolation - Thread safety: Lock for _service_healthy/_consecutive_failures - Score normalization: inverted to [0,1] where 1=most anomalous SQL: add lag1_autocorrelation + benford_deviation to view_thesis_features_1h Tests: 10 new test functions covering all improvements Integration: verify_mvs.py checks new thesis feature columns Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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toto
@ -164,3 +164,172 @@ def test_health_check_returns_correct_status():
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body = resp.read()
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assert b"ok" in body
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server.server_close()
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# ═══════════════════════════════════════════════════════════════════════════════
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# Tests pour les améliorations état de l'art v2
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# ═══════════════════════════════════════════════════════════════════════════════
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def test_eif_import_fallback():
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"""EIF import gracefully falls back to sklearn IF when isotree is unavailable."""
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# Verify the fallback pattern works regardless of installed packages
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try:
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from isotree import IsolationForest as EIF
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eif_avail = True
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except ImportError:
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eif_avail = False
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# The test passes as long as no unhandled exception occurs
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assert isinstance(eif_avail, bool)
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def test_normalize_scores_zero_to_one():
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"""Score normalization: most anomalous → 1.0, normal → 0.0."""
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scores = np.array([-0.5, -0.3, -0.1, 0.0, 0.2])
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result = np.zeros_like(scores)
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mask = scores < 0
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if mask.sum() > 0:
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s_min = scores[mask].min()
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if s_min != 0:
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result[mask] = np.clip(-scores[mask] / (-s_min + 1e-9), 0.0, 1.0)
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assert result[0] == pytest.approx(1.0, abs=0.01), "Most anomalous should be ~1.0"
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assert result[3] == 0.0, "Normal score should be 0.0"
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assert result[4] == 0.0, "Positive score should be 0.0"
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assert 0 < result[1] < result[0], "Less anomalous should be between 0 and max"
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def test_normalize_scores_all_positive():
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"""When all scores are positive (normal), all normalized scores should be 0."""
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scores = np.array([0.1, 0.2, 0.5, 1.0])
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result = np.zeros_like(scores)
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mask = scores < 0
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assert mask.sum() == 0
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assert np.all(result == 0.0)
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def test_validation_gate_rejects_contaminated_baseline():
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"""Model should be rejected if val_anomaly_rate > 0.20 (contaminated baseline)."""
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VAL_ANOMALY_GATE = 0.20
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# Simulate: 30% of validation scores are anomalous
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val_scores = np.concatenate([np.full(70, 0.1), np.full(30, -0.2)])
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val_anomaly_rate = float(np.mean(val_scores < 0))
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assert val_anomaly_rate > VAL_ANOMALY_GATE, "Should detect contaminated baseline"
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# Simulate: only 5% anomalous → passes the gate
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val_scores_clean = np.concatenate([np.full(95, 0.1), np.full(5, -0.2)])
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val_anomaly_rate_clean = float(np.mean(val_scores_clean < 0))
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assert val_anomaly_rate_clean <= VAL_ANOMALY_GATE, "Clean baseline should pass gate"
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def test_feature_pruning_removes_constant_features():
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"""Features with variance < threshold should be pruned."""
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PRUNE_VARIANCE_THRESHOLD = 1e-6
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df = pd.DataFrame({
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'good_feat': [1.0, 2.0, 3.0, 4.0, 5.0],
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'constant_feat': [1.0, 1.0, 1.0, 1.0, 1.0],
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'near_zero_var': [1.0, 1.0, 1.0, 1.0, 1.0 + 1e-8],
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})
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feature_variances = df.var()
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low_var = feature_variances[feature_variances < PRUNE_VARIANCE_THRESHOLD].index.tolist()
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assert 'constant_feat' in low_var, "Constant feature should be pruned"
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assert 'near_zero_var' in low_var, "Near-zero variance feature should be pruned"
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assert 'good_feat' not in low_var, "Good feature should NOT be pruned"
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def test_quantile_drift_detection():
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"""Quantile-based drift detection should detect distribution shift."""
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rng = np.random.default_rng(42)
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# Original distribution: N(0, 1)
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baseline_stats = {
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'feat1': {
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'mean': 0.0, 'std': 1.0,
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'p10': -1.28, 'p25': -0.67, 'p50': 0.0, 'p75': 0.67, 'p90': 1.28,
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}
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}
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# Current data: shifted to N(3, 1) — definite drift
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drifted_data = pd.DataFrame({'feat1': rng.normal(3.0, 1.0, 100)})
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# Reconstruct via quantile interpolation
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quantile_probs = np.array([0.10, 0.25, 0.50, 0.75, 0.90])
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quantile_vals = np.array([-1.28, -0.67, 0.0, 0.67, 1.28])
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u = rng.uniform(0, 1, size=100)
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synthetic = np.interp(u, quantile_probs, quantile_vals)
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from scipy.stats import ks_2samp
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_, p_value = ks_2samp(drifted_data['feat1'].values, synthetic)
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assert p_value < 0.05, "Should detect drift when distribution is shifted"
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# Same distribution — no drift
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same_data = pd.DataFrame({'feat1': rng.normal(0.0, 1.0, 100)})
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_, p_same = ks_2samp(same_data['feat1'].values, synthetic)
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assert p_same > 0.01, "Should not detect drift when distribution is similar"
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def test_thread_safety_lock_exists():
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"""Health lock should be a threading.Lock for thread-safe health status updates."""
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import threading as _threading
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lock = _threading.Lock()
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assert lock.acquire(blocking=False), "Lock should be acquirable"
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lock.release()
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# Simulate read-modify-write with lock
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counter = [0]
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def increment():
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with lock:
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counter[0] += 1
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threads = [_threading.Thread(target=increment) for _ in range(100)]
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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assert counter[0] == 100, "Lock should protect counter from race conditions"
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def test_score_to_threat_level():
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"""Threat level mapping: CRITICAL < -0.30, HIGH < -0.15, MEDIUM < -0.05, LOW < 0."""
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def score_to_threat_level(score):
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if score < -0.30: return 'CRITICAL'
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if score < -0.15: return 'HIGH'
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if score < -0.05: return 'MEDIUM'
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if score < 0: return 'LOW'
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return 'NORMAL'
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assert score_to_threat_level(-0.5) == 'CRITICAL'
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assert score_to_threat_level(-0.30) == 'HIGH'
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assert score_to_threat_level(-0.15) == 'MEDIUM'
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assert score_to_threat_level(-0.05) == 'LOW'
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assert score_to_threat_level(0.0) == 'NORMAL'
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assert score_to_threat_level(0.5) == 'NORMAL'
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def test_benford_expected_distribution():
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"""Benford's law: P(d) = log10(1 + 1/d) for d=1..9."""
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import math
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expected = [math.log10(1 + 1/d) for d in range(1, 10)]
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assert sum(expected) == pytest.approx(1.0, abs=0.001), "Benford probs should sum to 1"
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assert expected[0] == pytest.approx(0.301, abs=0.001), "P(1) should be ~0.301"
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assert expected[8] == pytest.approx(0.046, abs=0.001), "P(9) should be ~0.046"
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def test_lag1_autocorrelation_bot_vs_human():
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"""Bot with constant spacing should have high autocorrelation; human should be low."""
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# Bot: constant spacing with small jitter → high autocorrelation
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rng = np.random.default_rng(42)
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bot_deltas = 100.0 + rng.normal(0, 2, 50) # very regular
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mean_b = np.mean(bot_deltas)
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var_b = np.var(bot_deltas)
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if var_b > 1e-9:
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cov_b = np.mean((bot_deltas[:-1] - mean_b) * (bot_deltas[1:] - mean_b))
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rho_bot = cov_b / var_b
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else:
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rho_bot = 0.0
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# Human: highly variable spacing → low autocorrelation
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human_deltas = rng.exponential(500, 50) # random, independent
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mean_h = np.mean(human_deltas)
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var_h = np.var(human_deltas)
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if var_h > 1e-9:
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cov_h = np.mean((human_deltas[:-1] - mean_h) * (human_deltas[1:] - mean_h))
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rho_human = cov_h / var_h
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else:
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rho_human = 0.0
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assert abs(rho_human) < 0.5, f"Human autocorrelation should be low, got {rho_human:.3f}"
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