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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–325 of 800 papers

TitleStatusHype
A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes—0
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
A Compendium on Network and Host based Intrusion Detection Systems—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
A survey on deep packet inspection for intrusion detection 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
Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning—0
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance—0
Energy-based Models for Video Anomaly Detection—0
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection—0
An Adversarial Approach for Explainable AI in Intrusion Detection Systems—0
Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks—0
Enhanced network anomaly detection based on deep neural networks—0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers—0
Utilizing Deep Learning for Enhancing Network Resilience in Finance—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—0
Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks—0
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems—0
Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—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