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 751–760 of 800 papers

TitleStatusHype
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection—0
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks—0
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis—0
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach—0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks—0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security—0
A Novel Online Incremental Learning Intrusion Prevention System—0
A Novel Resampling Technique for Imbalanced Dataset Optimization—0
AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networks—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