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 771–780 of 800 papers

TitleStatusHype
A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms—0
Artificial Neural Network for Cybersecurity: A Comprehensive Review—0
A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles—0
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique—0
A short review on Applications of Deep learning for Cyber security—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors—0
Assessing Cyclostationary Malware Detection via Feature Selection and Classification—0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
A Survey for Deep Reinforcement Learning Based Network 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