Network Intrusion Detection
46 papers with code • 5 benchmarks • 12 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
CIC IoT Dataset 2022
IoT Network Intrusion Dataset
IoT Benign and Attack Traces
IoT ENVIRONMENT DATASET
Latest papers with no code
Advancing Network Intrusion Detection: Integrating Graph Neural Networks with Scattering Transform and Node2Vec for Enhanced Anomaly Detection
In this paper, we present two novel methods in Network Intrusion Detection Systems (NIDS) using Graph Neural Networks (GNNs).
An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks
It can detect known and unknown attacks.
Dealing with Imbalanced Classes in Bot-IoT Dataset
To evaluate the robustness of the NIDS in the IoT network, the existing work proposed a realistic botnet dataset in the IoT network (Bot-IoT dataset) and applied it to machine learning-based anomaly detection.
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning
As the number of IoT devices increases, security concerns become more prominent.
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks
Machine Learning (ML) algorithms have become increasingly popular for supporting Network Intrusion Detection Systems (NIDS).
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats
Within this second tier, we also embed a multi-classification mechanism coupled with a clustering algorithm.
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection
As cyber-attacks become more sophisticated, improving the robustness of Machine Learning (ML) models must be a priority for enterprises of all sizes.
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction
Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities.
Real-time Network Intrusion Detection via Decision Transformers
Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e. g., network intrusion detection from a sequence of arriving packets.
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture
Deep Learning (DL) based methods have shown great promise in network intrusion detection by identifying malicious network traffic behavior patterns with high accuracy, but their applications to real-time, packet-level detections in high-speed communication networks are challenging due to the high computation time and resource requirements of Deep Neural Networks (DNNs), as well as lack of explainability.
