A survey of deep learning methods for cyber security
DS Berman, AL Buczak, JS Chavis, CL Corbett - Information, 2019 - mdpi.com
This survey paper describes a literature review of deep learning (DL) methods for cyber
security applications. A short tutorial-style description of each DL method is provided …
security applications. A short tutorial-style description of each DL method is provided …
Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects
Abstract In modern Smart Grids (SGs) ruled by advanced computing and networking
technologies, condition monitoring relies on secure cyberphysical connectivity. Due to this …
technologies, condition monitoring relies on secure cyberphysical connectivity. Due to this …
A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing
The brain is capable of massively parallel information processing while consuming only∼ 1–
100 fJ per synaptic event,. Inspired by the efficiency of the brain, CMOS-based neural …
100 fJ per synaptic event,. Inspired by the efficiency of the brain, CMOS-based neural …
Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches
Wheat is the most important staple crop grown in Australia, and Australia is one of the top
wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is …
wheat exporting countries globally. Timely and reliable wheat yield prediction in Australia is …
Li-ion synaptic transistor for low power analog computing
Nonvolatile redox transistors (NVRTs) based upon Li-ion battery materials are demonstrated
as memory elements for neuromorphic computer architectures with multi-level analog …
as memory elements for neuromorphic computer architectures with multi-level analog …
Non‐Volatile Electrolyte‐Gated Transistors Based on Graphdiyne/MoS2 with Robust Stability for Low‐Power Neuromorphic Computing and Logic‐In‐Memory
BW Yao, J Li, XD Chen, MX Yu… - Advanced Functional …, 2021 - Wiley Online Library
Artificial synapses are the key building blocks for low‐power neuromorphic computing that
can go beyond the constraints of von Neumann architecture. In comparison with two …
can go beyond the constraints of von Neumann architecture. In comparison with two …
Resistive memory device requirements for a neural algorithm accelerator
Resistive memories enable dramatic energy reductions for neural algorithms. We propose a
general purpose neural architecture that can accelerate many different algorithms and …
general purpose neural architecture that can accelerate many different algorithms and …
Controllable digital and analog resistive switching behavior of 2D layered WSe 2 nanosheets for neuromorphic computing
S Cheng, L Zhong, J Yin, H Duan, Q Xie, W Luo, W Jie - Nanoscale, 2023 - pubs.rsc.org
Memristors with controllable resistive switching (RS) behavior have been considered as
promising candidates for synaptic devices in next-generation neuromorphic computing. In …
promising candidates for synaptic devices in next-generation neuromorphic computing. In …
Computational intelligence enabled cybersecurity for the internet of things
The computational intelligence (CI) based technologies play key roles in campaigning
cybersecurity challenges in complex systems such as the Internet of Things (IoT), cyber …
cybersecurity challenges in complex systems such as the Internet of Things (IoT), cyber …
Mimicking pain-perceptual sensitization and pattern recognition based on capacitance-and conductance-regulated neuroplasticity in neural network
Neuromorphic computing, inspired by the biological neuronal system, is a high potential
approach to substantially alleviate the cost of computational latency and energy for massive …
approach to substantially alleviate the cost of computational latency and energy for massive …