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Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner.
A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
Conventionally, like most neural networks, both of the aforementioned RNN variants employ the Softmax function as its final output layer for its prediction, and the cross-entropy function for computing its loss.
Ranked #1 on
Intrusion Detection
on 20NewsGroups
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS).
Ranked #1 on
Network Intrusion Detection
on KDD
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems
This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats.
Deep Learning Applications for Intrusion Detection in Network Traffic
The CNN-BiLSTM neural network is synthesized to assess the applicability of deep learning methods for intrusion detection.
Ranked #4 on
Network Intrusion Detection
on CICIDS2017
(using extra training data)
An Intrusion Detection System based on Deep Belief Networks
The CICIDS2017 dataset was used to train and evaluate the performance of our proposed DBN approach.
Ranked #3 on
Network Intrusion Detection
on CICIDS2017
A Novel SDN Dataset for Intrusion Detection in IoT Networks
The number of Internet of Things (IoT) devices and the use cases they aim to support have increased sharply in the past decade with the rapid developments in wireless networking infrastructures.
LuNet: A Deep Neural Network for Network Intrusion Detection
Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems
Our evaluation conducted on a dataset with a variety of network attacks shows denoising autoencoders can improve detection of malicious traffic by up to 29% in a normal setting and by up to 45% in an adversarial setting compared to other recently proposed anomaly detectors.
Non-Intrusive Reduced-Order Modeling Using Uncertainty-Aware Deep Neural Networks and Proper Orthogonal Decomposition: Application to Flood Modeling
These insights on the unknown are also utilized for an uncertainty propagation task, allowing for flooded area predictions that are broader and safer than those made with a regular uncertainty-uninformed surrogate model.
Computational Physics Data Analysis, Statistics and Probability
