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
Most implemented papers
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
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.
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives
Applying the SMOTE oversampling technique and random undersampling, we create a balanced version of NSL-KDD and prove that skewed target classes in KDD-99 and NSL-KDD hamper the efficacy of classifiers on minority classes (U2R and R2L), leading to possible security risks.
Sparse Bayesian approach for metric learning in latent space
Also, the present work is extended for learning in the feature space induced by an RKHS kernel.
LuNet: A Deep Neural Network for Network Intrusion Detection
mhwong2007/LuNet
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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.
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors
dongtsi/TrafficManipulator
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Many adversarial attacks have been proposed to evaluate the robustness of ML-based NIDSs.
Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection
Pattern matching methods usually have a high False Positive Rates whereas the AI/ML based method, relies on finding metric/feature or correlation between set of metrics/features to predict the possibility of an attack.
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion Detection
Fully Connected Neural Networks (FCNNs) have been the core of most state-of-the-art Machine Learning (ML) applications in recent years and also have been widely used for Intrusion Detection Systems (IDSs).
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems
Our evaluation conducted on a dataset with a variety of network attacks shows denoising autoencoders can improve detection of malicious traffic by up to 29% in a normal setting and by up to 45% in an adversarial setting compared to other recently proposed anomaly detectors.
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion Detection
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.
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs
yuweisunn/segmented-FL
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International Joint Conference on Neural Networks (IJCNN) 2020
In this research, a segmented federated learning is proposed, different from a collaborative learning based on single global model in a traditional federated learning model, it keeps multiple global models which allow each segment of participants to conduct collaborative learning separately and rearranges the segmentation of participants dynamically as well.
