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Ensemble learning-based IDS for sensors telemetry data in IoT networks

Naz, Naila; Khan, Muazzam A; Alsuhibany, Suliman A.; Diyan, Muhammad; Tan, Zhiyuan; Khan, Muhammad Almas; Ahmad, Jawad


Naila Naz

Muazzam A Khan

Suliman A. Alsuhibany

Muhammad Diyan

Muhammad Almas Khan


The Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with other devices using different communication protocols such as CoAP, MQTT, DDS, etc. Study shows that these protocols are vulnerable to attack and prove a significant threat to IoT telemetry data. Within a network, IoT devices are interdependent, and the behaviour of one device depends on the data coming from another device. An intruder exploits vulnerabilities of a device's interdependent feature and can alter the telemetry data to indirectly control the behaviour of other dependent devices in a network. Therefore, securing IoT devices have become a significant concern in IoT networks. The research community often proposes intrusion Detection Systems (IDS) using different techniques. One of the most adopted techniques is machine learning (ML) based intrusion detection. This study suggests a stacking-based ensemble model makes IoT devices more intelligent for detecting unusual behaviour in IoT networks. The TON-IoT (2020) dataset is used to assess the effectiveness of the proposed model. The proposed model achieves significant improvements in accuracy and other evaluation measures in binary and multi-class classification scenarios for most of the sensors compared to traditional ML algorithms and other ensemble techniques.

Journal Article Type Article
Acceptance Date Jul 4, 2022
Online Publication Date Jul 25, 2022
Publication Date Jul 25, 2022
Deposit Date Aug 7, 2022
Publicly Available Date Aug 8, 2022
Journal Mathematical Biosciences and Engineering
Print ISSN 1547-1063
Publisher American Institute of Mathematical Sciences
Peer Reviewed Peer Reviewed
Volume 19
Issue 10
Pages 10550-10580
Keywords ensemble learning, intrusion detection, IoT, sensors security, ToN-IoT, bagging
Public URL


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