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
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
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
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
DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture—0
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—0
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection—0
The Effective Methods for Intrusion Detection With Limited Network Attack Data: Multi-Task Learning and Oversampling—0
Interpretable Sequence Classification via Discrete OptimizationCode0
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed NetworksCode0
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
Graph-Based Intrusion Detection System for Controller Area Networks—0
Experimental Review of Neural-based approaches for Network Intrusion Management—0
Intrusion Detection for Cyber-Physical Systems using Generative Adversarial Networks in Fog EnvironmentCode1
MSTREAM: Fast Anomaly Detection in Multi-Aspect StreamsCode1
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
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection SystemsCode1
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—0
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
Fragments-Expert: A Graphical User Interface MATLAB Toolbox for Classification of File Fragments—0
NERD: Neural Network for Edict of Risky Data Streams—0
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—0
Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion DetectionCode1
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
Efficient Deep CNN-BiLSTM Model for Network Intrusion DetectionCode1
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
A Novel SDN Dataset for Intrusion Detection in IoT NetworksCode1
Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks—0
Learning With Differential Privacy—0
G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System—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