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 301310 of 800 papers

TitleStatusHype
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning0
Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS0
Federated Deep Learning for Intrusion Detection in IoT Networks0
Exploring Global and Local Information for Anomaly Detection with Normal Samples0
REGARD: Rules of EngaGement for Automated cybeR Defense to aid in Intrusion Response0
Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generation0
Anomaly Detection Dataset for Industrial Control Systems0
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion DetectionCode1
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour0
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection SystemsCode1
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Benchmark Results

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