A mini-review of machine learning in big data analytics: Applications, challenges, and prospects
The availability of digital technology in the hands of every citizenry worldwide makes an
available unprecedented massive amount of data. The capability to process these gigantic …
available unprecedented massive amount of data. The capability to process these gigantic …
An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
The rapid advancements in technology have given rise to groundbreaking solutions and
practical applications in the field of the Industrial Internet of Things (IIoT). These …
practical applications in the field of the Industrial Internet of Things (IIoT). These …
STL-HDL: A new hybrid network intrusion detection system for imbalanced dataset on big data environment
The ability to process large amounts of data in real time using big data analytics tools brings
many advantages that can be used in intrusion detection systems. Deep learning …
many advantages that can be used in intrusion detection systems. Deep learning …
Deep learning in IoT intrusion detection
Abstract The Internet of Things (IoT) is the new paradigm of our times, where smart devices
and sensors from across the globe are interconnected in a global grid, and distributed …
and sensors from across the globe are interconnected in a global grid, and distributed …
Big data analytics: from leadership to firm performance
Purpose This paper aims to propose a research model with eight constructs, ie BDA
leadership, BDA talent quality, BDA security quality, BDA privacy quality, innovation …
leadership, BDA talent quality, BDA security quality, BDA privacy quality, innovation …
A two-layer fog-cloud intrusion detection model for IoT networks
S Roy, J Li, Y Bai - Internet of Things, 2022 - Elsevier
Abstract The Internet of Things (IoT) and its applications are becoming ubiquitous in our life.
However, the open deployment environment and the limited resources of IoT devices make …
However, the open deployment environment and the limited resources of IoT devices make …
Deep learning model transposition for network intrusion detection systems
Companies seek to promote a swift digitalization of their business processes and new
disruptive features to gain an advantage over their competitors. This often results in a wider …
disruptive features to gain an advantage over their competitors. This often results in a wider …
Variational restricted Boltzmann machines to automated anomaly detection
Data-driven methods are implemented using particularly complex scenarios that reflect in-
depth perennial knowledge and research. Hence, the available intelligent algorithms are …
depth perennial knowledge and research. Hence, the available intelligent algorithms are …
Hijacking of unmanned surface vehicles: A demonstration of attacks and countermeasures in the field
Driven by advances in information and communication technologies, an increasing number
of industries embrace unmanned and autonomous vehicles for services, such as public …
of industries embrace unmanned and autonomous vehicles for services, such as public …
Reducing the false negative rate in deep learning based network intrusion detection systems
J Mijalkovic, A Spognardi - Algorithms, 2022 - mdpi.com
Network Intrusion Detection Systems (NIDS) represent a crucial component in the security of
a system, and their role is to continuously monitor the network and alert the user of any …
a system, and their role is to continuously monitor the network and alert the user of any …