Network Intrusion Detection

18 papers with code • 3 benchmarks • 1 datasets

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Greatest papers with code

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

JustGlowing/minisom 28 Aug 2020

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

Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

ymirsky/KitNET-py 25 Feb 2018

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

AbertayMachineLearningGroup/network-threats-taxonomy 9 Jun 2018

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

Deep Anomaly Detection with Deviation Networks

GuansongPang/deviation-network 19 Nov 2019

Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e. g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail.

Anomaly Detection Cyber Attack Detection +3

Hybrid Isolation Forest - Application to Intrusion Detection

pfmarteau/HIF 10 May 2017

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

Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

GuansongPang/deep-outlier-detection 13 Jun 2018

However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i. e., outliers).

Anomaly Detection Disease Prediction +3

AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks

haoyfan/AnomalyDAE 10 Feb 2020

In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings.

Anomaly Detection Network Intrusion Detection +1

LuNet: A Deep Neural Network for Network Intrusion Detection

mhwong2007/LuNet 22 Sep 2019

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.

Network Intrusion Detection

Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection

lmunoz-gonzalez/Poisoning-Attacks-with-Back-gradient-Optimization 8 Feb 2018

We show empirically that the adversarial examples generated by these attack strategies are quite different from genuine points, as no detectability constrains are considered to craft the attack.

Anomaly Detection Data Poisoning +2