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 151–200 of 800 papers

TitleStatusHype
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelCode0
Intrusion Detection Using Mouse DynamicsCode0
A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security—0
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks—0
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks—0
A Novel Approach To Network Intrusion Detection System Using Deep Learning For Sdn: Futuristic Approach—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
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis—0
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks—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
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection—0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System—0
Anomaly Detection via Federated Learning—0
Anomaly detection optimization using big data and deep learning to reduce false-positive—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
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms—0
Anomaly Detection in Intra-Vehicle Networks—0
Cybersecurity Anomaly Detection in Adversarial Environments—0
Anomaly Detection Framework Using Rule Extraction for Efficient 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
Anomaly Detection Dataset for Industrial Control Systems—0
Anomaly based network intrusion detection for IoT attacks using deep learning technique—0
Active Learning for Wireless IoT Intrusion Detection—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
An Interpretable Federated Learning-based Network Intrusion Detection Framework—0
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems—0
Active Learning for Network Intrusion Detection—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices—0
An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks—0
Adversarial Training for Deep Learning-based Intrusion Detection Systems—0
An Identification System Using Eye Detection Based On Wavelets And Neural Networks—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
CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model—0
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks—0
CADeSH: Collaborative Anomaly Detection for Smart Homes—0
ByteStack-ID: Integrated Stacked Model Leveraging Payload Byte Frequency for Grayscale Image-based Network Intrusion Detection—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—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