Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

S Messinis, N Temenos, NE Protonotarios… - Computers in Biology …, 2024 - Elsevier
Over the past five years, interest in the literature regarding the security of the Internet of
Medical Things (IoMT) has increased. Due to the enhanced interconnectedness of IoMT …

[HTML][HTML] Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things

S Wang, W Xu, Y Liu - Computer Networks, 2023 - Elsevier
Abstract The Internet of Things (IoT), as the information carrier of the Internet and
telecommunications networks, is a new network technology comprising physical entities …

[HTML][HTML] Research on adaptive 1DCNN network intrusion detection technology based on BSGM mixed sampling

W Ma, C Gou, Y Hou - Sensors, 2023 - mdpi.com
The development of internet technology has brought us benefits, but at the same time, there
has been a surge in network attack incidents, posing a serious threat to network security. In …

Privacy-preserving collaborative intrusion detection in edge of internet of things: A robust and efficient deep generative learning approach

W Yao, H Zhao, H Shi - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
The swift expansion of the Internet of Things (IoT) has brought about convenient services,
but it has also increased cyber threats. An intrusion detection system (IDS) is an effective …

[HTML][HTML] Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data

K Psychogyios, A Papadakis, S Bourou, N Nikolaou… - Future Internet, 2024 - mdpi.com
The advent of computer networks and the internet has drastically altered the means by
which we share information and interact with each other. However, this technological …

[HTML][HTML] Efficient Internet-of-Things Cyberattack Depletion Using Blockchain-Enabled Software-Defined Networking and 6G Network Technology

A Razaque, J Yoo, G Bektemyssova, M Alshammari… - Sensors, 2023 - mdpi.com
Low-speed internet can negatively impact incident response by causing delayed detection,
ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow …

A Comprehensive Analysis of the Machine Learning Algorithms in IoT IDS Systems

E Ozdogan - IEEE Access, 2024 - ieeexplore.ieee.org
In this study, machine learning algorithms in IoT IDS (Internet of Things Intrusion Detection
System) systems are comprehensively compared from various aspects. Accuracy, precision …

Balancing Data Privacy and 5G VNFs Security Monitoring: Federated Learning with CNN+ BiLSTM+ LSTM Model

AA Maiga, E Ataro, S Githinji - Journal of Electrical and …, 2024 - Wiley Online Library
The cloudification of telecommunication network functions with 5G is a novelty that offers
higher performance than that of previous generations. However, these virtual network …

Zeekflow+: A Deep LSTM Autoencoder with Integrated Random Forest Classifier for Binary and Multi-class Classification in Network Traffic Data

E Arapidis, N Temenos, D Giagkos, I Rallis… - Proceedings of the 17th …, 2024 - dl.acm.org
This work proposes Zeekflow+, a Deep LSTM Autoencoder (AE) architecture with integrated
Random Forest (RF) classifier for effective binary & multi-class classification of network traffic …

Fs-Tgan: An Enhanced Approach for Internet of Things (Iot) Intrusion Detection System Based on Feature Selection and Tabular Generative Adversarial Network

M CHEMMAKHA, A Chehri, O Habibi, M Lazaar… - Available at SSRN … - papers.ssrn.com
Abstract The Internet of Things (IoT) refers to a system of interconnected physical objects
that are capable of gathering and sharing data. The need for enhancing IoT security …