# A performance study of anomaly detection using entropy method

## TL;DR

This study evaluates the entropy method for anomaly detection in sensor networks, demonstrating its superior ability to identify outliers compared to elliptical methods, especially when sensor data are uncorrelated.

## Contribution

The paper provides an empirical comparison showing that the entropy method outperforms elliptical methods in detecting anomalies in uncorrelated sensor data.

## Key findings

- Entropy method detects more outliers than elliptical method.
- Entropy approach performs well with uncorrelated sensor data.
- Sensor independence is crucial for the effectiveness of the entropy method.

## Abstract

An experiment to study the entropy method for an anomaly detection system has been performed. The study has been conducted using real data generated from the distributed sensor networks at the Intel Berkeley Research Laboratory. The experimental results were compared with the elliptical method and has been analyzed in two dimensional data sets acquired from temperature and humidity sensors across 52 micro controllers. Using the binary classification to determine the upper and lower boundaries for each series of sensors, it has been shown that the entropy method are able to detect more number of out ranging sensor nodes than the elliptical methods. It can be argued that the better result was mainly due to the lack of elliptical approach which is requiring certain correlation between two sensor series, while in the entropy approach each sensor series is treated independently. This is very important in the current case where both sensor series are not correlated each other.