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

Showing 6170 of 800 papers

TitleStatusHype
MSTREAM: Fast Anomaly Detection in Multi-Aspect StreamsCode1
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection SystemsCode1
Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion DetectionCode1
Efficient Deep CNN-BiLSTM Model for Network Intrusion DetectionCode1
A Novel SDN Dataset for Intrusion Detection in IoT NetworksCode1
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference MeasureCode1
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion DetectorsCode1
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet DataCode1
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput ApplicationsCode1
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13Unverified
2K-Nearest NeighborsAccuracy (%)98.07Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9Unverified