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 51–75 of 800 papers

TitleStatusHype
Sketch-Based Anomaly Detection in Streaming GraphsCode1
MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of VehiclesCode1
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
Machine learning on knowledge graphs for context-aware security monitoringCode1
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
A flow-based IDS using Machine Learning in eBPFCode1
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural NetworkCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
Intrusion Detection for Cyber-Physical Systems using Generative Adversarial Networks in Fog EnvironmentCode1
MSTREAM: Fast Anomaly Detection in Multi-Aspect StreamsCode1
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection SystemsCode1
Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion DetectionCode1
Efficient Deep CNN-BiLSTM Model for Network Intrusion DetectionCode1
A Novel SDN Dataset for Intrusion Detection in IoT NetworksCode1
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference MeasureCode1
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion DetectorsCode1
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet DataCode1
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput ApplicationsCode1
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier DetectionCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
SparseIDS: Learning Packet Sampling with Reinforcement LearningCode1
Cyber Attack Detection thanks to Machine Learning AlgorithmsCode1
Explainability and Adversarial Robustness for RNNsCode1
Tree-based Intelligent Intrusion Detection System in Internet of VehiclesCode1
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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