Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review
Globally, the external internet is increasingly being connected to industrial control systems.
As a result, there is an immediate need to protect these networks from a variety of threats …
As a result, there is an immediate need to protect these networks from a variety of threats …
[PDF][PDF] Deep transfer learning applications in intrusion detection systems: A comprehensive review
Globally, the external Internet is increasingly being connected to the contemporary industrial
control system. As a result, there is an immediate need to protect the network from several …
control system. As a result, there is an immediate need to protect the network from several …
The evolution of federated learning-based intrusion detection and mitigation: a survey
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 …
Machine Learning (ML). FL does not share local data but ML models, offering applications in …
Transfer learning for raw network traffic detection
Traditional machine learning models used for network intrusion detection systems rely on
vast amounts of network traffic data with expertly engineered features. The abundance of …
vast amounts of network traffic data with expertly engineered features. The abundance of …
SEHIDS: Self evolving host-based intrusion detection system for IoT networks
M Baz - Sensors, 2022 - mdpi.com
The Internet of Things (IoT) offers unprecedented opportunities to access anything from
anywhere and at any time. It is, therefore, not surprising that the IoT acts as a paramount …
anywhere and at any time. It is, therefore, not surprising that the IoT acts as a paramount …
Generative ai-enabled blockchain networks: Fundamentals, applications, and case study
Generative Artificial Intelligence (GAI) has recently emerged as a promising solution to
address critical challenges of blockchain technology, including scalability, security, privacy …
address critical challenges of blockchain technology, including scalability, security, privacy …
ADCL: toward an adaptive network intrusion detection system using collaborative learning in IoT networks
With the widespread of cyber attacks, network intrusion detection system (NIDS) is becoming
an important and essential tool to protect Internet of Things (IoT) environments. However, it …
an important and essential tool to protect Internet of Things (IoT) environments. However, it …
Federated transfer learning for attack detection for Internet of Medical Things
AA Alharbi - International Journal of Information Security, 2024 - Springer
In the healthcare sector, cyberattack detection systems are crucial for ensuring the privacy of
patient data and building trust in the increasingly connected world of medical devices and …
patient data and building trust in the increasingly connected world of medical devices and …
Ensemble transfer learning for botnet detection in the Internet of Things
Botnet attacks are just one security scalability problem that nearly comes as a default with
each and every new IoT system launched into the real world. IoT devices, in particular, are …
each and every new IoT system launched into the real world. IoT devices, in particular, are …
[HTML][HTML] The cybersecurity mesh: A comprehensive survey of involved artificial intelligence methods, cryptographic protocols and challenges for future research
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 …
ever-evolving threats, adopting advanced models like cybersecurity mesh is necessary to …