Faster, More Accurate IoT Security: A Quantitative Analysis of the CUMAD Framework

Tested on the N-BaIoT dataset, CUMAD reduces false positives by 7x compared to autoencoders alone and detects threats faster than window-based methods.

Table of Links

Abstract and 1. Introduction

2. Related Work

3. Background on Autoencoder and SPRT and 3.1. Autoencoder

3.2. Sequential Probability Ratio Test

4. Design of CUMAD and 4.1. Network Model

4.2. CUMAD: Cumulative Anomaly Detection

5. Evaluation Studies and 5.1. Dataset, Features, and CUMAD System Setup

5.2. Performance Results

6. Conclusions and References

5. Evaluation Studies

In this section we perform evaluation studies to investigate the performance of CUMAD using the publicdomain N-BaIoT dataset [8]. In order to better understand the evaluation studies, we will first describe the dataset, in particular, the features of the data points contained in the dataset. We will also compare the performance of CUMAD with that of the N-BaIoT scheme ...