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 511–520 of 800 papers

TitleStatusHype
HTTP2vec: Embedding of HTTP Requests for Detection of Anomalous Traffic—0
Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges—0
Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks—0
Decision-forest voting scheme for classification of rare classes in network intrusion detection—0
Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks—0
Segmented Federated Learning for Adaptive Intrusion Detection System—0
Reinforcement Learning for Feedback-Enabled Cyber Resilience—0
Precise Feature Selection and Case Study of Intrusion Detection in an Industrial Control System (ICS) Environment—0
Feature selection for intrusion detection systems—0
Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach—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