@Article{csse.2023.039984,
AUTHOR = {Okba Taouali, Sawcen Bacha, Khaoula Ben Abdellafou, Ahamed Aljuhani, Kamel Zidi, Rehab Alanazi, Mohamed Faouzi Harkat},
TITLE = {Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {47},
YEAR = {2023},
NUMBER = {2},
PAGES = {1593--1609},
URL = {http://www.techscience.com/csse/v47n2/53681},
ISSN = {},
ABSTRACT = {Introducing IoT devices to healthcare fields has made it possible
to remotely monitor patients’ information and provide a proper diagnosis
as needed, resulting in the Internet of Medical Things (IoMT). However,
obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge. However, due to
the computational resources being limited, an edge device may struggle to
handle heavy detection tasks such as complex machine learning algorithms.
Therefore, designing and developing a lightweight detection mechanism is
crucial. To address the aforementioned challenges, a new lightweight IDS
approach is developed to effectively combat a diverse range of cyberattacks
in IoMT networks. The proposed anomaly-based IDS is divided into three
steps: pre-processing, feature selection, and decision. In the pre-processing
phase, data cleaning and normalization are performed. In the feature selection
step, the proposed approach uses two data-driven kernel techniques: kernel
principal component analysis and kernel partial least square techniques to
reduce the dimension of extracted features and to ameliorate the detection
results. Therefore, in decision step, in order to classify whether the traffic
flow is normal or malicious the kernel extreme learning machine is used.
To check the efficiency of the developed detection scheme, a modern IoMT
dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss
the achieved results. The proposed method achieved 99.9% accuracy, 99.8%
specificity, 100% Sensitivity, 99.9 F-score.},
DOI = {10.32604/csse.2023.039984}
}