Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of
IoT and IIoT Applications for Centralized and Federated Learning
Abstract
In this paper, we propose a new comprehensive realistic cyber security
dataset of IoT and IIoT applications, called Edge-IIoTset, which can be
used by machine learning-based intrusion detection systems in two
different modes, namely, centralized and federated learning.
Specifically, the proposed testbed is organized into seven layers,
including, Cloud Computing Layer, Network Functions Virtualization
Layer, Blockchain Network Layer, Fog Computing Layer, Software-Defined
Networking Layer, Edge Computing Layer, and IoT and IIoT Perception
Layer. In each layer, we use new emerging technologies that satisfy the
key requirements of IoT and IIoT applications, such as, ThingsBoard IoT
platform, OPNFV platform, Hyperledger Sawtooth, Digital twin, ONOS SDN
controller, Mosquitto MQTT brokers, Modbus TCP/IP, …etc. The IoT
data are generated from various IoT devices (more than 10 types) such as
Low-cost digital sensors for sensing temperature and humidity,
Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil
Moisture sensor, Heart Rate Sensor, Flame Sensor, …etc.).
Furthermore, we identify and analyze fourteen attacks related to IoT and
IIoT connectivity protocols, which are categorized into five threats,
including, DoS/DDoS attacks, Information gathering, Man in the middle
attacks, Injection attacks, and Malware attacks. In addition, we extract
features obtained from different sources, including alerts, system
resources, logs, network traffic, and propose new 61 features with high
correlations from 1176 found features. After processing and analyzing
the proposed realistic cyber security dataset, we provide a primary
exploratory data analysis and evaluate the performance of machine
learning approaches (i.e., traditional machine learning as well as deep
learning) in both centralized and federated learning modes.