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 161–170 of 800 papers

TitleStatusHype
Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection—0
A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks—0
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection—0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System—0
Anomaly Detection via Federated Learning—0
Anomaly detection optimization using big data and deep learning to reduce false-positive—0
A Heterogeneous Graph Learning Model for Cyber-Attack Detection—0
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection—0
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms—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