Intrusion Detection
100 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
Latest papers
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network
renj-xu/negsc
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To the best of our knowledge, it is the first GNN-based self-supervised method for the multiclass classification of network flows in NIDS.
On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection
The results show nearly perfect classification performance when the models are trained and tested on the same dataset.
Deep Learning Applications for Intrusion Detection in Network Traffic
The CNN-BiLSTM neural network is synthesized to assess the applicability of deep learning methods for intrusion detection.
Improving Transferability of Network Intrusion Detection in a Federated Learning Setup
Network Intrusion Detection Systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device.
A Study on Transferability of Deep Learning Models for Network Intrusion Detection
In this paper, we explore transferability in learning between different attack classes in a network intrusion detection setup.
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
While AD is typically treated as an unsupervised learning task due to the high cost of label annotation, it is more practical to assume access to a small set of labeled anomaly samples from domain experts, as is the case for semi-supervised anomaly detection.
LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection
Through experiments, we prove that LiPar has great detection performance, running efficiency, and lightweight model size, which can be well adapted to the in-vehicle environment practically and protect the in-vehicle CAN bus security.
Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning on CAN Messages
In this paper, we propose StatGraph: an Effective Multi-view Statistical Graph Learning Intrusion Detection to implement the fine-grained intrusion detection.
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.
PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference
We show that by using polynomial building blocks, we can achieve the same accuracy using considerably fewer layers of soft logic than by using linear functions, leading to significant latency and area improvements.
