Demystifying in-vehicle intrusion detection systems: A survey of surveys and a meta-taxonomy

G Karopoulos, G Kambourakis, E Chatzoglou… - Electronics, 2022 - mdpi.com
Breaches in the cyberspace due to cyber-physical attacks can harm the physical space, and
any type of vehicle is an alluring target for wrongdoers for an assortment of reasons …

Security of Internet of Things (IoT) using federated learning and deep learning—Recent advancements, issues and prospects

V Gugueoth, S Safavat, S Shetty - ICT Express, 2023 - Elsevier
There is a great demand for an efficient security framework which can secure IoT systems
from potential adversarial attacks. However, it is challenging to design a suitable security …

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: A survey

S Ali, Q Li, A Yousafzai - Ad Hoc Networks, 2024 - Elsevier
The industrial internet of things (IIoT) is an evolutionary extension of the traditional Internet of
Things (IoT) into processes and machines for applications in the industrial sector. The IIoT …

Adversarial attacks on deep neural network: developing robust models against evasion technique

GS Nadella, H Gonaygunta, K Meduri… - Transactions on Latest …, 2023 - ijsdcs.com
In the fast-paced field of machine learning, it is important to build agile models that can
correctly classify data in the face of enemy attacks. This paper explores the field of …

Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach

G de Carvalho Bertoli, LAP Junior, O Saotome… - Computers & …, 2023 - Elsevier
The constantly evolving digital transformation imposes new requirements on our society.
Aspects relating to reliance on the networking domain and the difficulty of achieving security …

Federated learning for IoMT applications: A standardization and benchmarking framework of intrusion detection systems

A Alamleh, OS Albahri, AA Zaidan… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Efficient evaluation for machine learning (ML)-based intrusion detection systems (IDSs) for
federated learning (FL) in the Internet of Medical Things (IoMTs) environment falls under the …

The evolution of federated learning-based intrusion detection and mitigation: a survey

L Lavaur, MO Pahl, Y Busnel… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In 2016, Google introduced the concept of Federated Learning (FL), enabling collaborative
Machine Learning (ML). FL does not share local data but ML models, offering applications in …

Design and testing novel one-class classifier based on polynomial interpolation with application to networking security

P Dini, A Begni, S Ciavarella, E De Paoli… - IEEE …, 2022 - ieeexplore.ieee.org
This work exploits the concept of one-class classifier applied to the problem of anomaly
detection in communication networks. The article presents the design of an innovative …

Comparative review of the intrusion detection systems based on federated learning: Advantages and open challenges

E Fedorchenko, E Novikova, A Shulepov - Algorithms, 2022 - mdpi.com
In order to provide an accurate and timely response to different types of the attacks, intrusion
and anomaly detection systems collect and analyze a lot of data that may include personal …

DeepFedWT: A federated deep learning framework for fault detection of wind turbines

G Jiang, WP Fan, W Li, L Wang, Q He, P Xie, X Li - Measurement, 2022 - Elsevier
Data-driven fault detection of wind turbines has gained increasingly attention. Currently,
most existing methods require sufficient labeled data to train a reliable model in a …