About

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

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Greatest papers with code

Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion Detection

28 Aug 2020JustGlowing/minisom

In this paper, we propose a self-organizing map assisted deep autoencoding Gaussian mixture model (SOMDAGMM) supplemented with well-preserved input space topology for more accurate network intrusion detection.

NETWORK INTRUSION DETECTION

SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection

11 Mar 2020yzhao062/SUOD

How to accelerate the training and predicting with a large number of heterogeneous unsupervised OD models?

DIMENSIONALITY REDUCTION FRAUD DETECTION INTRUSION DETECTION OUTLIER ENSEMBLES

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.

 Ranked #1 on Intrusion Detection on 20NEWS (using extra training data)

IMAGE CLASSIFICATION 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 of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems

9 Jun 2018AbertayMachineLearningGroup/network-threats-taxonomy

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.

ANOMALY DETECTION NETWORK INTRUSION DETECTION

MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams

17 Sep 2020Stream-AD/MStream

Given a stream of entries in a multi-aspect data setting i. e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner?

GROUP ANOMALY DETECTION 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

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

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