Introducing Five Machine Learning Benchmark Datasets from BhinnekaAI

BhinnekaAI published five tabular machine learning benchmark datasets on Hugging Face for unsupervised anomaly detection: FRD (credit card fraud, TSB-Fraud-Tabular), NET (network intrusion logs, MNT-Log-Traffic), MED (patient biomarker screening, CLB-Patient-Screening), IOT (sensor telemetry, IMC-Sensor-Telemetry) and LOG (supply chain inventory audit, ESCL-Logistics-Audit). Each dataset hides anomalies within normal feature ranges but violating deeper multivariate relationships — broken correlations between transaction amounts and balances, attacks placed in narrow gaps between normal clusters, shifts in covariance orientation, phase shifts in harmonic telemetry, and discrepancies among irrelevant noise. BhinnekaAI said the datasets are intended for experimenting with unsupervised anomaly detection, comparing baseline algorithms and studying the limits of different scoring approaches.

Hi everyone I’ve published five tabular datasets under BhinnekaAI on Hugging Face, covering different domains and anomaly detection challenges. Rather than focusing only on obvious outliers, these datasets are designed around more subtle patterns where anomalous samples may remain within normal feature ranges but violate deeper relationships in the data. 1. FRD — Credit Card Fraud Simulator Explore the dataset https://huggingface.co/datasets/BhinnekaAI/TSB-Fraud-Tabular Simulates credit card transactions with fraudulent samples hidden within normal feature ranges. The challenge is identifying broken multivariate relationships between transaction amounts, account balances, and daily activity. 2. NET — Network Intrusion Logs Explore the dataset https://huggingface.co/datasets/BhinnekaAI/MNT-Log-Traffic Represents network traffic with multiple clusters of normal activity. Simulated cyberattacks are placed near narrow gaps between normal clusters, challenging models to detect subtle spatial and local-density anomalies. 3. MED — Patient Biomarker Screening Explore the dataset https://huggingface.co/datasets/BhinnekaAI/CLB-Patient-Screening Models multivariate blood biomarker profiles with normal covariance structures. Anomalies are represented through changes in the orientation of the underlying multivariate structure. 4. IOT — Sensor Anomaly Explore the dataset https://huggingface.co/datasets/BhinnekaAI/IMC-Sensor-Telemetry Simulates industrial machine telemetry with nonlinear harmonic patterns. Mechanical anomalies are introduced through phase shifts while keeping individual feature values within their normal amplitude ranges. 5. LOG — Supply Chain Inventory Audit Explore the dataset https://huggingface.co/datasets/BhinnekaAI/ESCL-Logistics-Audit Represents high-dimensional logistics records with irrelevant noise. Hidden audit discrepancies, such as inventory inconsistencies and cost inflation, challenge models to identify meaningful anomalies among distracting features. The goal is to explore how machine learning models handle different types of subtle anomalies across financial, cybersecurity, biological, industrial, and logistical data. These datasets may be useful for experimenting with unsupervised anomaly detection, comparing baseline algorithms, and studying the limitations of different scoring approaches. I’d appreciate feedback from the community: Which dataset presents the most interesting anomaly detection challenge? Which unsupervised methods would you recommend benchmarking against these datasets? What additional evaluation scenarios would make this collection more useful for research? Explore the full collection: BhinnekaAI BhinnekaAI https://huggingface.co/BhinnekaAI Thanks for checking them out Feedback and contributions are welcome. MachineLearning AnomalyDetection Datasets https://discuss.huggingface.co/c/datasets/10 OpenScience