Machine learning and deep learning methods for intrusion detection systems: recent developments and challenges
G Kocher, G Kumar - Soft Computing, 2021 - Springer
Deep learning (DL) is gaining significant prevalence in every field of study due to its
domination in training large data sets. However, several applications are utilizing machine …
domination in training large data sets. However, several applications are utilizing machine …
Edge intelligence: Architectures, challenges, and applications
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …
caching, processing, and analysis in locations close to where data is captured based on …
Edge intelligence: Empowering intelligence to the edge of network
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …
caching, processing, and analysis proximity to where data are captured based on artificial …
Internet of Things: A survey on machine learning-based intrusion detection approaches
In the world scenario, concerns with security and privacy regarding computer networks are
always increasing. Computer security has become a necessity due to the proliferation of …
always increasing. Computer security has become a necessity due to the proliferation of …
Machine learning in real-time Internet of Things (IoT) systems: A survey
Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …
significantly evolved and been employed in diverse applications, such as computer vision …
Deep learning for compressive sensing: a ubiquitous systems perspective
AL Machidon, V Pejović - Artificial Intelligence Review, 2023 - Springer
Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor
sampling rate, potentially bringing context-awareness to a wider range of devices …
sampling rate, potentially bringing context-awareness to a wider range of devices …
An efficient image encryption using deep neural network and chaotic map
SR Maniyath, V Thanikaiselvan - Microprocessors and Microsystems, 2020 - Elsevier
Inspite of progressive growth of cryptography, encrypting sensitive information of an image is
still a computationally complex task. After reviewing existing literature, it is now known that …
still a computationally complex task. After reviewing existing literature, it is now known that …
Unsupervised pre-trained filter learning approach for efficient convolution neural network
Abstract The concept of Convolution Neural Network (ConvNet or CNN) is evaluated from
the animal visual cortex. Since humans can learn through experience, similarly, ConvNet …
the animal visual cortex. Since humans can learn through experience, similarly, ConvNet …
Artificial intelligence techniques for cognitive sensing in future IoT: State-of-the-Art, potentials, and challenges
Smart, secure and energy-efficient data collection (DC) processes are key to the realization
of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges …
of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges …
[PDF][PDF] A survey on edge intelligence
Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …
caching, processing, and analysis in locations close to where data is captured based on …