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

Showing 361370 of 800 papers

TitleStatusHype
FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS0
Anomaly detection optimization using big data and deep learning to reduce false-positive0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization0
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique0
Intrusion Detection Systems Using Support Vector Machines on the KDDCUP'99 and NSL-KDD Datasets: A Comprehensive Survey0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis0
Nature Inspired Metaheuristic Effectiveness Used in Phishing Intrusion Detection Systems with Grey Wolf Algorithm Techniques0
Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces0
CAN bus intrusion detection based on auxiliary classifier GAN and out-of-distribution detectionCode1
RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13Unverified
2K-Nearest NeighborsAccuracy (%)98.07Unverified
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1MSTREAM-PCAAUC0.94Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9Unverified