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 281–290 of 800 papers

TitleStatusHype
Automating Privilege Escalation with Deep Reinforcement Learning—0
AutoIDS: Auto-encoder Based Method for Intrusion Detection System—0
An AutoML-based approach for Network Intrusion Detection—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
Attribute Learning for Network Intrusion Detection—0
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models—0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—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