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 426–450 of 800 papers

TitleStatusHype
LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach—0
Machine Learning Applications in Misuse and Anomaly Detection—0
Machine Learning Approach for Detection of nonTor Traffic—0
Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security—0
Machine Learning-based Android Intrusion Detection System—0
Machine Learning based Anomaly Detection for 5G Networks—0
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction—0
Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions—0
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction—0
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things—0
Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats—0
Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments—0
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations—0
Machine Learning Techniques for Intrusion Detection—0
Manifold regularization based on Nyström type subsampling—0
Man-in-the-Middle Intrusion Detection Based on CNN-LSTM Model—0
Many Field Packet Classification with Decomposition and Reinforcement Learning—0
Mapping the Landscape of Generative AI in Network Monitoring and Management—0
Mimic Learning to Generate a Shareable Network Intrusion Detection Model—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs—0
Model Selection for Anomaly Detection—0
Modern Cybersecurity Solution using Supervised Machine Learning—0
Modern Problems Require Modern Solutions: Hybrid Concepts for Industrial Intrusion Detection—0
More Efficient Topic Modelling Through a Noun Only Approach—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