AungMoonLord_bert-log-anomaly-detection

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library_name: transformers metrics:

• accuracy
• precision
• recall
• f1 license: apache-2.0 base_model:
• google-bert/bert-base-uncased pipeline_tag: text-classification

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Model Card for Model ID

Model Summary

1. bert-log-anomaly-detection is a BERT-based NLP model fine-tuned for single SQL transaction log anomaly detection.

2. The model classifies each database transaction log as either Normal or Anomaly, with the goal of supporting AI-powered fraud detection and cybersecurity monitoring systems.

3. This model was developed as part of the Samsung × KBTG Digital Fraud Cybersecurity Hackathon (Thailand) under the AI-Powered Fraud Detection & Prevention track.

Model Description

This model analyzes individual SQL database transaction logs and detects abnormal patterns that may indicate fraudulent, malicious, or suspicious behavior.

Demo: Hackathon prototype

• Developed by: Aungruk Vanichanai, Napat Wanitwatthakorn, Thanakrit Sriphiphattana
• Shared by: Aungruk Vanichanai
• Model type: Transformer-based binary text classifier
• Language(s) (NLP): English (SQL logs in text format)
• License: Apache 2.0
• Finetuned from model: google-bert/bert-base-uncased

Model Sources

• GitHub Repository: https://github.com/AungMoonLord/AI-Cybersecurity-Hackathon/tree/main/New%20Finetune%20Hackathon

How to Get Started with the Model

Step 1 (Setup)

import torch
from transformers import BertForSequenceClassification, BertTokenizer

MODEL_PATH = "AungMoonLord/bert-log-anomaly-detection"

model = BertForSequenceClassification.from_pretrained(MODEL_PATH)
tokenizer = BertTokenizer.from_pretrained(MODEL_PATH)

model.eval()

Step 2 (Clean and Label Logs) — Optional, but may slightly improve accuracy, recall, and F1-score

# Perfom log preprocessing
def add_prefix_token(text): # log data must pass this code before training/inferencing
    # clean log
    text = text.replace("\t", " ")
    text = text.strip()
    # add token
    if text[0].isalpha() or text[3].isalpha():
        return "[SQL]\n" + text
    else:
        return "[LOG]\n" + text

Step 3 (Create the Function for Log Classification)

def predict_log(log_text):
    log_text = add_prefix_token(log_text)
    inputs = tokenizer(
        log_text,
        return_tensors="pt",
        truncation=True,
        padding=True, # for cases when the inference contains more than 1 log, i.e., batch size > 1
        max_length=128
    )

    with torch.no_grad():
        logits = model(**inputs).logits
        pred = torch.argmax(logits, dim=1).item()
        prob = torch.softmax(logits, dim=-1).tolist()[0]

    return "Normal" if pred == 1 else "Anomaly", prob

Step 4 (Samples of Inferences)

# Example 1
text1 = "SELECT * FROM users WHERE id = 1 OR 1=1"
print(predict_log(text1))

# Example 2
text2 = "2025-01-06 14:23:45 | User: anonymous | IP: 203.154.89.102 | Duration: 0.05s SELECT * FROM users WHERE username = 'admin' OR '1'='1' -- ' AND password = 'x'"
print(predict_log(text2))

# Example 3
text3 = "3051-06-22T07:20:02.296945Z 3 Query select e3mJKDCCY from 7Q8SpG8LLEWhrfpe4s5 where ph4d = 'a1S9hQa92uC1EAyJf2Y';"
print(predict_log(text3))

Application in Hackathon Project

• Developed by Waris Sripatoomrak, this model integrates with an n8n workflow to automate fraud detection within financial transaction logs.

Out-of-Scope Use

• Multi-log sequence anomaly detection

• Non-textual anomaly detection

Training Data

• SQL database transaction logs (1,611 samples) synthetically generated by ChatGPT, Qwen, DeepSeek, Grok, Gemini, and Claude

• Each log labeled as either Normal or Anomaly

• Data prepared for single-log classification

Evaluation

Metrics

- Training Set

Metric	Value
Accuracy	0.8950
Precision	0.8580
Recall	0.9026
F1-score	0.8797
Validation Loss	0.3279

- Test Set (Baseline — No Step 2 Preprocessing)

Metric	Value
Accuracy	0.6950
Precision	0.6639
Recall	0.7900
F1-score	0.7215
Validation Loss	0.6251

- Test Set (Full Pipeline — With Step 2 Preprocessing)

Metric	Value
Accuracy	0.7000
Precision	0.6613
Recall	0.8200
F1-score	0.7321
Validation Loss	0.6344

Summary

The model demonstrates strong anomaly detection capability with high recall, making it suitable for fraud detection and cybersecurity use cases.