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 526–550 of 800 papers

TitleStatusHype
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions—0
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection—0
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
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
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
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
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
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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