Jason-42195/VNU-SQLi-Detection-Models

VNU SQLi Detection Models

Trained model artifacts for a 2-branch AI-based SQL Injection detection system (Branch 3 — session-level — is designed but not yet implemented; see the project repo for details).

Data used to train these models: Jason-42195/VNU-SQLi-Detection.

nhanh1_v1/ — Branch 1 (supervised multiclass)

TF-IDF (char_wb, 2-4gram) + Logistic Regression. Classifies a query into one of 5 classes: normal, union_based, error_based, boolean_blind, time_blind.

• F1-macro: 0.9822 on held-out test set (13,560 rows)
• p50 latency: ~0.5ms, size: ~3.9MB
• Files: vectorizer.joblib (TfidfVectorizer), model.joblib (LogisticRegression), metadata.json

import joblib
vectorizer = joblib.load("nhanh1_v1/vectorizer.joblib")
clf = joblib.load("nhanh1_v1/model.joblib")

X = vectorizer.transform(["1' OR '1'='1"])
clf.predict(X)  # -> array([3])  (3 = boolean_blind)

nhanh2_v1/ — Branch 2 (anomaly detection)

One-Class SVM trained on 100% benign traffic, using 4 structural features (length, special_char_ratio, sql_keyword_count, entropy) — not TF-IDF, so it can generalize to unseen attack syntax.

• AUC: 0.90, FPR: 0.3% (9/3000 benign), detection rate: 20.7% (5196/25065 anomalous)
• Files: model.joblib (wraps src.models.nhanh2_anomaly.AnomalyDetector), metadata.json

# From within the project repo (needs src.models.nhanh2_anomaly.AnomalyDetector):
from src.models.nhanh2_anomaly import AnomalyDetector
import numpy as np

detector = AnomalyDetector.load("nhanh2_v1")
X = np.array([[40, 0.05, 1, 3.6]])  # [length, special_char_ratio, sql_keyword_count, entropy]
detector.score(X)          # continuous anomaly score
detector.anomaly_flags(X)  # boolean flag

Limitations

• Branch 1's boolean_blind class has ~13% measured label noise (catch-all bucket for unmatched attack rows) — see data_contract.md in the project repo.
• Branch 2's detection rate (20.7%) reflects a diverse anomalous eval set (D3 CSIC2010, covering XSS/path-traversal/etc., not just SQLi) — not a pure SQLi zero-day benchmark.
• No adversarial/obfuscation robustness testing yet.
• License: mixed/unclear for underlying training data (see the dataset repo's card) — treat as research/course-project artifacts, not yet cleared for unrestricted reuse.

Project repo

Full source, training scripts, and documentation: see the VNU-Database2-Project repo (private/course project — ask the author for access).