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 401–410 of 800 papers

TitleStatusHype
VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT—0
Anomaly Detection via Federated Learning—0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—0
Network Intrusion Detection System in a Light Bulb—0
Effective Metaheuristic Based Classifiers for Multiclass Intrusion Detection—0
LGTBIDS: Layer-wise Graph Theory Based Intrusion Detection System in Beyond 5G—0
FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
Anomaly detection optimization using big data and deep learning to reduce false-positive—0
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique—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