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Intrusion Detection

18 papers with code · Miscellaneous

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A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

10 Sep 2017AFAgarap/cnn-svm

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.

 SOTA for Intrusion Detection on 20NEWS (using extra training data)

INTRUSION DETECTION SPEECH RECOGNITION TEXT CLASSIFICATION

Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

25 Feb 2018ymirsky/KitNET-py

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.

NETWORK INTRUSION DETECTION

A Taxonomy and Survey of Intrusion Detection System Design Techniques, Network Threats and Datasets

9 Jun 2018AbertayMachineLearningGroup/network-threats-taxonomy

This manuscript aims to provide researchers with a taxonomy and survey of current dataset composition and current Intrusion Detection Systems (IDS) capabilities and assets.

INTRUSION DETECTION

Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems

4 Aug 2018antonpuz/DeROL

Second, we develop the first open-source software for practical artificially intelligent one-shot classification systems with limited resources for the benefit of researchers in related fields.

INTRUSION DETECTION OMNIGLOT ONE-SHOT LEARNING

Hybrid Isolation Forest - Application to Intrusion Detection

10 May 2017pfmarteau/HIF

From the identification of a drawback in the Isolation Forest (IF) algorithm that limits its use in the scope of anomaly detection, we propose two extensions that allow to firstly overcome the previously mention limitation and secondly to provide it with some supervised learning capability.

ANOMALY DETECTION NETWORK INTRUSION DETECTION

Cyber Attack Detection thanks to Machine Learning Algorithms

17 Jan 2020antoinedelplace/Cyberattack-Detection

The Random Forest Classifier succeeds in detecting more than 95% of the botnets in 8 out of 13 scenarios and more than 55% in the most difficult datasets.

CYBER ATTACK DETECTION FEATURE SELECTION INTRUSION DETECTION

eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys

27 Feb 2017MJafarMashhadi/Haplophysh

For years security machine learning research has promised to obviate the need for signature based detection by automatically learning to detect indicators of attack.

INTRUSION DETECTION

Learning Neural Representations for Network Anomaly Detection

IEEE Transactions on Cybernetics 2019 vanloicao/SAEDVAE

Our approach is to introduce new regularizers to a classical autoencoder (AE) and a variational AE, which force normal data into a very tight area centered at the origin in the nonsaturating area of the bottleneck unit activations.

INTRUSION DETECTION MODEL SELECTION UNSUPERVISED ANOMALY DETECTION

CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data

6 Jun 2019etas/SynCAN

For reproducibility of the method, our synthetic data is publicly available.

INTRUSION DETECTION