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 251–260 of 800 papers

TitleStatusHype
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks—0
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification—0
A New Clustering Approach for Anomaly Intrusion Detection—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
Adversarial Examples in Constrained Domains—0
Bidirectional RNN for Medical Event Detection in Electronic Health Records—0
Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks—0
A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm—0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets—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