A survey on mobile edge computing for video streaming: Opportunities and challenges

MA Khan, E Baccour, Z Chkirbene, A Erbad… - IEEE …, 2022 - ieeexplore.ieee.org
5G communication brings substantial improvements in the quality of service provided to
various applications by achieving higher throughput and lower latency. However, interactive …

A comprehensive survey on client selections in federated learning

A Gouissem, Z Chkirbene, R Hamila - arXiv preprint arXiv:2311.06801, 2023 - arxiv.org
Federated Learning (FL) is a rapidly growing field in machine learning that allows data to be
trained across multiple decentralized devices. The selection of clients to participate in the …

Sliding principal component and dynamic reward reinforcement learning based IIoT attack detection

V Ellappan, A Mahendran, M Subramanian… - Scientific Reports, 2023 - nature.com
Abstract The Internet of Things (IoT) involves the gathering of all those devices that connect
to the Internet with the purpose of collecting and sharing data. The application of IoT in the …

An enhancement method in few-shot scenarios for intrusion detection in smart home environments

Y Chen, J Wang, T Yang, Q Li, NA Nijhum - Electronics, 2023 - mdpi.com
Different devices in the smart home environment are subject to different levels of attack.
Devices with lower attack frequencies confront difficulties in collecting attack data, which …

Amplification methods to promote the attacks against machine learning-based intrusion detection systems

S Zhang, Y Xu, X Zhang, X Xie - Applied Intelligence, 2024 - Springer
The security of machine learning attracts increasing attention in both academia and industry
due to its vulnerability to adversarial examples. However, the research on adversarial …

Effective Industrial Internet of Things Vulnerability Detection Using Machine Learning

CI Nwakanma, LAC Ahakonye, JN Njoku… - 2022 5th Information …, 2022 - ieeexplore.ieee.org
Protecting the industrial internet of things (IIoT) devices through vulnerability detection is
critical as the consequences of attacks can be devastating. Machine learning (ML) has …

Effects of hybrid non-linear feature extraction method on different data sampling techniques for liver disease prediction

R Yasmin, R Amin, M Reza - Journal of Future Sustainability, 2022 - growingscience.com
Liver disease indicates inflammatory condition of the liver, liver cirrhosis, cancer, or an
overload of toxic substances. A liver transplant may reinstate and extend life if a patient has …

Secure medical data sharing for healthcare system

Z Chkirbene, R Hamila, A Erbad - 2022 IEEE 33rd Annual …, 2022 - ieeexplore.ieee.org
A new generation of advanced information technologies are used nowadays by healthcare
systems to provide access to affordable and high-quality healthcare services. However, such …

Reinforcement Learning Based Active Attack Detection and Blockchain Technique to Protect the Data from the Passive Attack in the Autonomous Mobile Network

C Sivasankar, T Kumanan - Wireless Personal Communications, 2023 - Springer
Nowadays, autonomous mobile network (AMN) nodes are utilized in widespread
distribution; thus, security issues are a large apprehension. AMN is still necessary for further …

A new random forest and support vector machine-based intrusion detection model in networks

P Dey, D Bhakta - National Academy Science Letters, 2023 - Springer
There exist many intrusion detection systems (IDSs) to provide privacy and security to user
data in networks. However, these models are prone to generate high false alarms due to …