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 481–490 of 800 papers

TitleStatusHype
Jasmine: A New Active Learning Approach to Combat Cybercrime—0
Intrusion Detection In Computer Networks Using Machine Learning AlgorithmsCode0
SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks—0
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
Unveiling the potential of Graph Neural Networks for robust Intrusion DetectionCode1
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
Reinforcement Learning for Feedback-Enabled Cyber Resilience—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