Network Intrusion Detection
65 papers with code • 5 benchmarks • 13 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.
Benchmarks
These leaderboards are used to track progress in Network Intrusion Detection
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Libraries
Use these libraries to find Network Intrusion Detection models and implementationsDatasets
UNSW-NB15
CICIDS2017
SIDD-Image
IoT Network Intrusion Dataset
CIC IoT Dataset 2022
IoT Benign and Attack Traces
IoT ENVIRONMENT DATASET
Most implemented papers
Deep Anomaly Detection with Deviation Networks
GuansongPang/deviation-network
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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.
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).
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.
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
xuhongzuo/DeepOD
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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
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.
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs).
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.
Synthesis of a Machine Learning Model for Detecting Computer Attacks Based on the CICIDS2017 Dataset
The conclusion was made that it is possible to use machine learning methods to detect computer attacks taking into account these limitations.
IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection
In this paper we present an approach for modelling IoT network attacks that focuses on generalizability, yet also leads to better detection and performance.
Hybrid Isolation Forest - Application to Intrusion Detection
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.
