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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 176–200 of 800 papers

TitleStatusHype
CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid—0
Anomaly Detection Dataset for Industrial Control Systems—0
Anomaly based network intrusion detection for IoT attacks using deep learning technique—0
Active Learning for Wireless IoT Intrusion Detection—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
An Interpretable Federated Learning-based Network Intrusion Detection Framework—0
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems—0
Active Learning for Network Intrusion Detection—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices—0
An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks—0
Adversarial Training for Deep Learning-based Intrusion Detection Systems—0
An Identification System Using Eye Detection Based On Wavelets And Neural Networks—0
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection—0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection—0
CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model—0
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks—0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
Building an Effective Intrusion Detection System using Unsupervised Feature Selection in Multi-objective Optimization Framework—0
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion 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