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 76–100 of 800 papers

TitleStatusHype
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks—0
AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks—0
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection—0
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection—0
A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks—0
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System—0
A Heterogeneous Graph Learning Model for Cyber-Attack Detection—0
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection—0
A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection—0
A Cutting-Edge Deep Learning Method For Enhancing IoT Security—0
CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid—0
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
Active Learning for Wireless IoT Intrusion Detection—0
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs—0
Active Learning for Network Intrusion Detection—0
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
Adversarial Training for Deep Learning-based Intrusion Detection Systems—0
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection—0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection—0
Show:102550
← PrevPage 4 of 32Next →
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