Intrusion Detection
101 papers with code • 4 benchmarks • 7 datasets
Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.
Benchmarks
These leaderboards are used to track progress in Intrusion Detection
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Libraries
Use these libraries to find Intrusion Detection models and implementationsDatasets
UNSW-NB15
20NewsGroups
IoT Network Intrusion Dataset
IoT ENVIRONMENT DATASET
Most implemented papers
Learning Neural Representations for Network Anomaly Detection
vanloicao/SAEDVAE
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IEEE Transactions on Cybernetics 2019
Our approach is to introduce new regularizers to a classical autoencoder (AE) and a variational AE, which force normal data into a very tight area centered at the origin in the nonsaturating area of the bottleneck unit activations.
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.
Walling up Backdoors in Intrusion Detection Systems
Interest in poisoning attacks and backdoors recently resurfaced for Deep Learning (DL) applications.
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.
K-Metamodes: frequency- and ensemble-based distributed k-modes clustering for security analytics
Besides this, the existing feature discretisation method from the previous work is utilised in order to adapt k-modes for processing of mixed data sets.
Tree-based Intelligent Intrusion Detection System in Internet of Vehicles
The results from the implementation of the proposed intrusion detection system on standard data sets indicate that the system has the ability to identify various cyber-attacks in the AV networks.
Explainability and Adversarial Robustness for RNNs
CN-TU/adversarial-recurrent-ids
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Recurrent Neural Networks (RNNs) yield attractive properties for constructing Intrusion Detection Systems (IDSs) for network data.
SparseIDS: Learning Packet Sampling with Reinforcement Learning
CN-TU/adversarial-recurrent-ids
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To minimize the computational expenses of the RL-based sampling we show that a shared neural network can be used for both the classifier and the RL logic.
LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection
Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's Internet, disrupting the availability of essential services.
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
