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 761–770 of 800 papers

TitleStatusHype
A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid—0
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning—0
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
Are Embedding Spaces Interpretable? Results of an Intrusion Detection Evaluation on a Large French Corpus—0
Are Trees Really Green? A Detection Approach of IoT Malware Attacks—0
A review of Federated Learning in Intrusion Detection Systems for IoT—0
A Review of Machine Learning based Anomaly Detection Techniques—0
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges—0
Are We There Yet? Unraveling the State-of-the-Art Graph Network Intrusion Detection Systems—0
ARLIF-IDS -- Attention augmented Real-Time Isolation Forest 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