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Embedding Tree-Based Intrusion Detection System in Smart Thermostats for Enhanced IoT Security.
Abbas Javed1, Muhammad Naeem Awais1, Ayyaz-Ul-Haq Qureshi2
1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan.
Sensors (Basel, Switzerland)
|November 27, 2024
Summary
Resource-constrained IoT devices can be secured with embedded intrusion detection systems (IDS). A novel dataset and tree-based IDS achieved high accuracy in detecting denial of service (DoS) and man-in-the-middle (MITM) attacks on smart thermostats.
Area of Science:
- Cybersecurity
- Internet of Things (IoT)
- Embedded Systems
Background:
- IoT devices with limited resources are vulnerable to attacks like Denial of Service (DoS) and Man-in-the-Middle (MITM), especially without gateways.
- Traditional Intrusion Detection Systems (IDS) are often deployed at the edge or cloud, but require on-device deployment for gateway-less IoT environments.
- Existing datasets lack features from microcontroller-based IoT devices, hindering the development of effective embedded IDS.
Purpose of the Study:
- To develop a unique dataset (Intrusion Detection in the Smart Homes - IDSH) with features retrievable from microcontroller-based IoT devices.
- To embed a Tree-based IDS within a smart thermostat for real-time intrusion detection.
- To evaluate the performance of the embedded IDS in detecting common IoT attacks without external infrastructure.
Main Methods:
- Creation of the Intrusion Detection in the Smart Homes (IDSH) dataset using microcontroller-based IoT device features.
- Implementation of a Tree-based Intrusion Detection System (IDS) directly onto a smart thermostat.
- Real-time testing and performance evaluation of the embedded IDS against DoS and MITM attacks.
Main Results:
- The embedded Tree-based IDS achieved 98.71% accuracy for binary classification and 97.51% for multi-classification.
- The system demonstrated a fast inference time, with 276 microseconds for binary and 273 microseconds for multi-classification.
- Real-time tests confirmed the smart thermostat's capability to detect DoS and MITM attacks autonomously.
Conclusions:
- Embedded IDS on resource-constrained IoT devices are feasible and effective for real-time threat detection.
- The developed IDSH dataset provides valuable features for training IDS on microcontroller-level IoT devices.
- This approach enhances IoT security by enabling on-device attack detection without reliance on gateways or cloud infrastructure.
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