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 211–220 of 800 papers

TitleStatusHype
Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—0
Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies—0
Conformalized density- and distance-based anomaly detection in time-series data—0
A survey on deep packet inspection for intrusion detection systems—0
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems—0
A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection—0
An Adversarial Approach for Explainable AI in Intrusion Detection Systems—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