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 601–650 of 800 papers

TitleStatusHype
Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data—0
Multi-agent Reinforcement Learning-based Network Intrusion Detection System—0
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications—0
Multibit Tries Packet Classification with Deep Reinforcement Learning—0
Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection—0
Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol—0
Multidomain transformer-based deep learning for early detection of network intrusion—0
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems—0
Multi-Source Data Fusion for Cyberattack Detection in Power Systems—0
Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study—0
Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks—0
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection—0
Multivariate Big Data Analysis for Intrusion Detection: 5 steps from the haystack to the needle—0
Nature Inspired Metaheuristic Effectiveness Used in Phishing Intrusion Detection Systems with Grey Wolf Algorithm Techniques—0
NERD: Neural Network for Edict of Risky Data Streams—0
NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks—0
Network Activities Recognition and Analysis Based on Supervised Machine Learning Classification Methods Using J48 and Naïve Bayes Algorithm—0
Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD—0
Network Intrusion Detection based on LSTM and Feature Embedding—0
Network Intrusion Detection System in a Light Bulb—0
Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection—0
Network Security Modelling with Distributional Data—0
Neuromorphic Mimicry Attacks Exploiting Brain-Inspired Computing for Covert Cyber Intrusions—0
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning—0
New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning—0
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