Machine learning and blockchain technologies for cybersecurity in connected vehicles

J Ahmad, MU Zia, IH Naqvi, JN Chattha… - … reviews: data mining …, 2024 - Wiley Online Library
Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks
for their everyday functions on the road so that safety of passengers and vehicles can be …

Security issues in Internet of Vehicles (IoV): A comprehensive survey

H Taslimasa, S Dadkhah, ECP Neto, P **ong, S Ray… - Internet of Things, 2023 - Elsevier
Nowadays, connected vehicles have a major role in enhancing the driving experience.
Connected vehicles in the network share their knowledge with the help of the network …

A federated learning-based approach for improving intrusion detection in industrial internet of things networks

MM Rashid, SU Khan, F Eusufzai, MA Redwan… - Network, 2023 - mdpi.com
The Internet of Things (IoT) is a network of electrical devices that are connected to the
Internet wirelessly. This group of devices generates a large amount of data with information …

Federated learning-based misbehavior detection for the 5G-enabled Internet of Vehicles

P Rani, C Sharma, JVN Ramesh… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
The concept of federated learning (FL) is becoming increasingly popular as a method for
training collaborative models without loss the sensitive information. The term has become …

A novel chaos-based privacy-preserving deep learning model for cancer diagnosis

MU Rehman, A Shafique, YY Ghadi… - … on Network Science …, 2022 - ieeexplore.ieee.org
Early cancer identification is regarded as a challenging problem in cancer prevention for the
healthcare community. In addition, ensuring privacy-preserving healthcare data becomes …

Deep learning in the fast lane: A survey on advanced intrusion detection systems for intelligent vehicle networks

M Almehdhar, A Albaseer, MA Khan… - IEEE Open Journal …, 2024 - ieeexplore.ieee.org
The rapid evolution of modern automobiles into intelligent and interconnected entities
presents new challenges in cybersecurity, particularly in Intrusion Detection Systems (IDS) …

Review of cyberattack implementation, detection, and mitigation methods in cyber-physical systems

N Mtukushe, AK Onaolapo, A Aluko, DG Dorrell - Energies, 2023 - mdpi.com
With the rapid proliferation of cyber-physical systems (CPSs) in various sectors, including
critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing …

[HTML][HTML] A novel detection and multi-classification approach for IoT-malware using random forest voting of fine-tuning convolutional neural networks

SB Atitallah, M Driss, I Almomani - Sensors, 2022 - mdpi.com
The Internet of Things (IoT) is prone to malware assaults due to its simple installation and
autonomous operating qualities. IoT devices have become the most tempting targets of …

[HTML][HTML] The cybersecurity mesh: A comprehensive survey of involved artificial intelligence methods, cryptographic protocols and challenges for future research

B Ramos-Cruz, J Andreu-Perez, L Martínez - Neurocomputing, 2024 - Elsevier
In today's world, it is vital to have strong cybersecurity measures in place. To combat the
ever-evolving threats, adopting advanced models like cybersecurity mesh is necessary to …

FedMicro-IDA: A federated learning and microservices-based framework for IoT data analytics

SB Atitallah, M Driss, HB Ghezala - Internet of Things, 2023 - Elsevier
Abstract The Internet of Things (IoT) has recently proliferated in both size and complexity.
Using multi-source and heterogeneous IoT data aids in providing efficient data analytics for …