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 151–160 of 800 papers

TitleStatusHype
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security—0
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks—0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks—0
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach—0
AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks—0
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection—0
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System—0
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis—0
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks—0
A Hybrid Deep Learning Anomaly Detection Framework for 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