Network Intrusion Detection
47 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
Efficient Network Representation for GNN-based Intrusion Detection
The last decades have seen a growth in the number of cyber-attacks with severe economic and privacy damages, which reveals the need for network intrusion detection approaches to assist in preventing cyber-attacks and reducing their risks.
Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification
The evolution of Internet and its related communication technologies have consistently increased the risk of cyber-attacks.
Multidomain transformer-based deep learning for early detection of network intrusion
Timely response of Network Intrusion Detection Systems (NIDS) is constrained by the flow generation process which requires accumulation of network packets.
Assessing Cyclostationary Malware Detection via Feature Selection and Classification
These features are extracted using algorithms such as Boruta and Principal Component Analysis (PCA), and then categorized to find the most significant cyclostationary patterns.
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection
Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world application.
Real-time Regular Expression Matching
The article supports the solution with theorems on correctness and complexity.
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection
Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed.
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks
As a defence method, Adversarial Training is used to increase the robustness of the NIDS model.
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction
However, the feature extraction method is much more reliable than its selection counterpart, particularly when K is very small, such as K = 4.
Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier
Performance of XGBoost, which represents conventional ML, is evaluated.
