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Ensemble learning based anomaly detection for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis

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posted on 2024-08-27, 04:08 authored by T Lai, F Farid, A Bello, Fariza SabrinaFariza Sabrina
The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods.

History

Volume

7

Issue

1

Start Page

1

End Page

18

Number of Pages

18

eISSN

2523-3246

ISSN

2096-4862

Publisher

Springer Science and Business Media LLC

Additional Rights

CC BY 4.0

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2024-04-02

Era Eligible

  • Yes

Journal

Cybersecurity

Article Number

1

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