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 51–100 of 800 papers

TitleStatusHype
TOD: GPU-accelerated Outlier Detection via Tensor OperationsCode1
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph LearningCode1
A flow-based IDS using Machine Learning in eBPFCode1
A new Deep Learning Based Intrusion Detection System for Cloud SecurityCode1
Real-time Event Recognition of Long-distance Distributed Vibration Sensing with Knowledge Distillation and Hardware AccelerationCode1
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural NetworksCode1
PolyLUT-Add: FPGA-based LUT Inference with Wide InputsCode1
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet DataCode1
Machine learning on knowledge graphs for context-aware security monitoringCode1
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput ApplicationsCode1
Cyber Attack Detection thanks to Machine Learning AlgorithmsCode1
Intrusion Detection for Cyber-Physical Systems using Generative Adversarial Networks in Fog EnvironmentCode1
Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT AlgorithmsCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
T-DFNN: An Incremental Learning Algorithm for Intrusion Detection SystemsCode1
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion DetectionCode1
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural NetworkCode1
STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph LearningCode1
Enabling AutoML for Zero-Touch Network Security: Use-Case Driven AnalysisCode1
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
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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