Network Intrusion Detection

34 papers with code • 5 benchmarks • 6 datasets

Network intrusion detection is the task of monitoring network traffic to and from all devices on a network in order to detect computer attacks.


Use these libraries to find Network Intrusion Detection models and implementations
2 papers

Most implemented papers

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.

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.

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

xuhongzuo/DeepOD 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).

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.

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.

Synthesis of a Machine Learning Model for Detecting Computer Attacks Based on the CICIDS2017 Dataset

fisher85/ml-cybersecurity Proceedings of the Institute for System Programming of RAS 2020

The conclusion was made that it is possible to use machine learning methods to detect computer attacks taking into account these limitations.

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT

waimorris/E-GraphSAGE 30 Mar 2021

This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs).

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.

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.