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Found 8 papers, 2 papers with code
Online Self-Supervised Deep Learning for Intrusion Detection Systems
This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning.
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection
Cyberattacks are increasingly threatening networked systems, often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices.
Associated Random Neural Networks for Collective Classification of Nodes in Botnet Attacks
Thus this work introduces a collective Botnet attack classification technique that operates on traffic from an n-node IP network with a novel Associated Random Neural Network (ARNN) that identifies the nodes which are compromised.
Random Quantum Neural Networks (RQNN) for Noisy Image Recognition
We have extensively validated our proposed RQNN model, relying on hybrid classical-quantum algorithms via the PennyLane Quantum simulator with a limited number of \emph{qubits}.
Accurate and Energy-Efficient Classification with Spiking Random Neural Network: Corrected and Expanded Version
Artificial Neural Network (ANN) based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide.
Nonnegative autoencoder with simplified random neural network
This paper proposes new nonnegative (shallow and multi-layer) autoencoders by combining the spiking Random Neural Network (RNN) model, the network architecture typical used in deep-learning area and the training technique inspired from nonnegative matrix factorization (NMF).
Deep Learning in Multi-Layer Architectures of Dense Nuclei
We assume that, within the dense clusters of neurons that can be found in nuclei, cells may interconnect via soma-to-soma interactions, in addition to conventional synaptic connections.
Towards a Cognitive Routing Engine for Software Defined Networks
Most Software Defined Networks (SDN) traffic engineering applications use excessive and frequent global monitoring in order to find the optimal Quality-of-Service (QoS) paths for the current state of the network.
