BhinnekaAI/MNT-Log-Traffic

Overview

MNT-Log-Traffic is a synthetic tabular anomaly detection benchmark developed by BhinnekaAI for evaluating algorithms that detect cyber intrusions, port scanning, and low-and-slow denial of service attacks in enterprise network logs.

Normal network behavior is characterized by consistent connection durations and standard packet byte distributions. Anomalies represent stealthy intrusion attempts or abnormal traffic spikes.

Dataset information

Property	Description
Dataset name	MNT-Log-Traffic
Full name	Multivariate Network Traffic Benchmark
Publisher	BhinnekaAI
Dataset Creator	Christopher Alberto Hamonangan
Domain	Cybersecurity and network intrusion detection
Data modality	Structured numerical tabular data
Feature count	5 numerical features
Data type	Synthetic
Task	Network anomaly and intrusion detection
Expected anomaly proportion	Approximately 10.0%
Label convention	0 = normal traffic; 1 = malicious intrusion

Dataset Structure

random-seed

• Directory: random-seed
• Files: 5 files, 5,000 observations each (dataset_5000_rows_seed{seed}.csv, seeds: 11, 22, 33, 44, 55).

seed42

• Directory: seed42
• Files: 5 files with sizes 100, 1,000, 10,000, 50,000, and 100,000 (dataset_{size}_rows_seed42.csv).

Feature Specification

Feature	Description	Data Type
duration_sec	Connection duration in seconds	Continuous (Exponential)
packet_bytes	Total packet size in bytes	Continuous (Log-Normal)
error_rate	Ratio of transmission errors	Continuous
dest_port_category	Categorical numerical representation of destination port	Discrete
tcp_flag_anomaly	TCP flag irregularity indicator	Binary

Mathematical Formulation

Normal durations follow an exponential decay process:

duration_sec ~ Exponential(λ = 2.0)

Anomalies introduce prolonged connection durations and massive packet byte transfers.

Loading the Dataset

import pandas as pd

df = pd.read_csv("seed42/dataset_10000_rows_seed42.csv")
X = df.iloc[:, :-1].values
y = df['is_intrusion'].values