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 501–550 of 800 papers

TitleStatusHype
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection—0
A new Deep Learning Based Intrusion Detection System for Cloud SecurityCode1
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services—0
A concise method for feature selection via normalized frequencies—0
Sketch-Based Anomaly Detection in Streaming GraphsCode1
FlexParser -- the adaptive log file parser for continuous results in a changing world—0
Network Activities Recognition and Analysis Based on Supervised Machine Learning Classification Methods Using J48 and Naïve Bayes Algorithm—0
MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of VehiclesCode1
Performance Analysis of a Foreground Segmentation Neural Network Model—0
Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model—0
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
Machine learning on knowledge graphs for context-aware security monitoringCode1
Cybersecurity Anomaly Detection in Adversarial Environments—0
ADASYN-Random Forest Based Intrusion Detection Model—0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means—0
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT—0
Robustness of ML-Enhanced IDS to Stealthy Adversaries—0
Adversarial Training for Deep Learning-based Intrusion Detection Systems—0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets—0
Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning—0
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection—0
A multiagent based framework secured with layered SVM-based IDS for remote healthcare systems—0
Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review—0
Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network—0
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Exploring Edge TPU for Network Intrusion Detection in IoT—0
Evaluating Document Coherence Modelling—0
Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss—0
Efficient Intrusion Detection Using Evidence Theory—0
Image Classifiers for Network Intrusions—0
Explaining Network Intrusion Detection System Using Explainable AI Framework—0
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack—0
ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection—0
Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers—0
Clustering Algorithm to Detect Adversaries in Federated Learning—0
A flow-based IDS using Machine Learning in eBPFCode1
How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?—0
TINKER: A framework for Open source Cyberthreat Intelligence—0
Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data—0
Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based NetworksCode0
Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural NetworkCode1
DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware—0
Robust Attack Detection Approach for IIoT Using Ensemble Classifier—0
Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy—0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset—0
Multi-Source Data Fusion for Cyberattack Detection in Power Systems—0
Time-Based CAN Intrusion Detection Benchmark—0
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks—0
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks—0
Towards Network Traffic Monitoring Using Deep Transfer Learning—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