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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Prediction Models to Effectively Detect Malware Patterns in the IoT Systems

Author 1: Rawabi Nazal Alhamad
Author 2: Faeiz M. Alserhani

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130744

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 7, 2022.

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Abstract: The Widespread use of the Internet of Things (IoT) has influenced many domains including smart cities, cameras, wearables, smart industrial equipment, and other aspects of our daily lives. On the other hand, the IoT environment deals with a massive volume of data that needs to be kept secure from tampering or theft. Detection of security attacks against IoT context requires intelligent techniques rather than relying on signature matching. Machine learning (ML) and Deep Learning (DL) approaches are efficient to detect these attacks and predicting intrusion behavior based on unknown patterns. This study proposes the application of five deep and ML techniques for identifying malware in network traffic based on the IoT-23 dataset. Random Forest, Catboost, XGBoost, Convolutional Neural Network, and Long Short-Term Memory (LSTM) models are among the classifiers utilized. These algorithms have been selected to provide lightweight security systems to be deployed in the IoT devices rather than a centralized approach. The dataset was preprocessed to remove unnecessary or missing data, and then the most significant features were extracted using a feature engineering technique. The highest overall accuracy achieved was 96% by applying all classifiers except LSTM which recorded a lower accuracy.

Keywords: Internet of Things (IoT); malware deletion; random forest; Catboost; convolutional neural network; long short-term memory (LSTM); XGBoost

Rawabi Nazal Alhamad and Faeiz M. Alserhani, “Prediction Models to Effectively Detect Malware Patterns in the IoT Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 13(7), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130744

@article{Alhamad2022,
title = {Prediction Models to Effectively Detect Malware Patterns in the IoT Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130744},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130744},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {7},
author = {Rawabi Nazal Alhamad and Faeiz M. Alserhani}
}


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