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 351–375 of 800 papers

TitleStatusHype
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection—0
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces—0
Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning—0
Exploring Edge TPU for Network Intrusion Detection in IoT—0
Exploring Global and Local Information for Anomaly Detection with Normal Samples—0
Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN—0
Exploring Information Centrality for Intrusion Detection in Large Networks—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means—0
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning—0
Extreme bandits—0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
Fast Feature Reduction in intrusion detection datasets—0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors—0
Feasibility of Non-Line-of-Sight Integrated Sensing and Communication at mmWave—0
Feature Analysis for Machine Learning-based IoT Intrusion Detection—0
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection—0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection—0
Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems—0
Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification—0
Feature selection for intrusion detection systems—0
Feature Selection for Network Intrusion Detection—0
Feature Selection using the concept of Peafowl Mating in IDS—0
Efficient Network Representation for GNN-based 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