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 301–325 of 800 papers

TitleStatusHype
Efficient Intrusion Detection Using Evidence Theory—0
Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture—0
Efficient Network Representation for GNN-based Intrusion Detection—0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection—0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
A Review of Machine Learning based Anomaly Detection Techniques—0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.—0
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs—0
End-to-End Adversarial Learning for Intrusion Detection in Computer Networks—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector—0
Energy-based Models for Video Anomaly Detection—0
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance—0
Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning—0
Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks—0
Enhanced network anomaly detection based on deep neural networks—0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers—0
Utilizing Deep Learning for Enhancing Network Resilience in Finance—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—0
Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks—0
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems—0
Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems—0
DI-NIDS: Domain Invariant Network 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