SOTAVerified

Intrusion Detection

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.

Source: Machine Learning Techniques for Intrusion Detection

Papers

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Showing 551–600 of 800 papers

TitleStatusHype
Explaining Network Intrusion Detection System Using Explainable AI Framework—0
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack—0
ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection—0
Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers—0
Clustering Algorithm to Detect Adversaries in Federated Learning—0
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
TINKER: A framework for Open source Cyberthreat Intelligence—0
Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data—0
Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based NetworksCode0
DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware—0
Robust Attack Detection Approach for IIoT Using Ensemble Classifier—0
Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy—0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset—0
Multi-Source Data Fusion for Cyberattack Detection in Power Systems—0
Time-Based CAN Intrusion Detection Benchmark—0
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks—0
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks—0
Towards Network Traffic Monitoring Using Deep Transfer Learning—0
A Novel Resampling Technique for Imbalanced Dataset Optimization—0
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset—0
Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On AutoencoderCode0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
Fragments Expert A Graphical User Interface MATLAB Toolbox for Classification of File FragmentsCode0
RNNIDS: Enhancing Network Intrusion Detection Systems through Deep Learning—0
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape—0
Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach—0
Intrusion detection in computer systems by using artificial neural networks with Deep Learning approaches—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems—0
Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning—0
Training a quantum annealing based restricted Boltzmann machine on cybersecurity data—0
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack—0
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance—0
Adversarial Examples in Constrained Domains—0
Unsupervised Intrusion Detection System for Unmanned Aerial Vehicle with Less Labeling Effort—0
DualNet: Locate Then Detect Effective Payload with Deep Attention Network—0
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—0
DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture—0
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection—0
Interpretable Sequence Classification via Discrete OptimizationCode0
The Effective Methods for Intrusion Detection With Limited Network Attack Data: Multi-Task Learning and Oversampling—0
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed NetworksCode0
Graph-Based Intrusion Detection System for Controller Area Networks—0
Experimental Review of Neural-based approaches for Network Intrusion Management—0
Machine Learning Applications in Misuse and Anomaly Detection—0
PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection—0
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion DetectionCode0
Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection—0
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified