Journal of Ubiquitous Computing and Communication Technologies
IRO Journals
Volume 7 — Issue 1 — March 2025

A Framework for Detecting Multiple Cyberattacks in IoT Environments

https://doi.org/10.36548/jucct.2025.1.003
Yonas Mekonnen
Department of Computer Science, Natural and Computational Science, Addis Ababa University, Addis Ababa, Ethiopia
Mesfin Kifle
Assistance Professor, Department of Computer Science, Natural and Computational Science, Addis Ababa University, Addis Ababa, Ethiopia
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Mekonnen, Yonas, and Mesfin Kifle. 2025. “A Framework for Detecting Multiple Cyberattacks in IoT Environments”. Journal of Ubiquitous Computing and Communication Technologies 7 (1): 36-60. https://doi.org/10.36548/jucct.2025.1.003.
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Keywords

— IoT Environments
— Cyberattacks
— Multiple Attack Detection
— CNN
— LSTM
— FFNN
Published: 10-04-2025

Abstract

The Internet of Things refers to the growing trend of embedding ubiquitous and pervasive computing capabilities through sensor networks and internet connectivity. The growth and expansion of newly evolved cyberattacks, network patterns and heterogenous nature of cyberattacks trend become the warfare across the globe and challenges to apply single layer cyberattacks detection techniques to the Internet of Things. This research work identified the lack of cyberattacks detection framework as the major gap for detection of multiple cyberattacks such as denial of services, distributed denial of services, and multiple attacks while it includes multiple parameters at the same time The proposed framework contains three primary modules; the first module is responsible for capturing and pre-processing the captured data and construction of the model, then the core engine moule orchestrates the detection of cyberattacks. The third module, notifies and displays the results in a dashboard. This research study used multiple parameters including multiple attack classes, network packet patterns, and three scalar types namely no scaler, MinMax, and Standard. Regardless of the defined parameters used minmax scaler followed by standard scaler gives better detection performance than models trained with no scaler. The proposed framework is trained and evaluated with different models including Convolutional Neural Network (CNN), Hybrid, Feed Forward Neural Networks (FFNN), and Long Short-Term Memory (LSTM) that provides a result of 91.42%, 82.75%, 78.38%, and,74.83% detection accuracy respectively where it is observed that CNN model outperforms the optimal results among followed by hybrid and FFNN.

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