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 476–500 of 800 papers

TitleStatusHype
NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capability—0
Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions—0
Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks—0
Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification—0
OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns—0
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack—0
Omni SCADA Intrusion Detection Using Deep Learning Algorithms—0
One-Class Classification for Intrusion Detection on Vehicular Networks—0
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks—0
One-Shot Learning on Attributed Sequences—0
On Generalisability of Machine Learning-based Network Intrusion Detection Systems—0
Onion-Peeling Outlier Detection in 2-D data Sets—0
Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems—0
Online Feature Ranking for Intrusion Detection Systems—0
Online Self-Supervised Deep Learning for Intrusion Detection Systems—0
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection—0
On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0—0
On the (Statistical) Detection of Adversarial Examples—0
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—0
Open Set Dandelion Network for IoT Intrusion Detection—0
Integrating Artificial Intelligence into Operating Systems: A Comprehensive Survey on Techniques, Applications, and Future Directions—0
Optimal In-Network Distribution of Learning Functions for a Secure-by-Design Programmable Data Plane of Next-Generation Networks—0
Optimized IoT Intrusion Detection using Machine Learning Technique—0
Optimizing cnn-Bigru performance: Mish activation and comparative analysis with Relu—0
Orthogonal variance-based feature selection for 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