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 601610 of 800 papers

TitleStatusHype
Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation0
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference MeasureCode1
Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review0
A cognitive based Intrusion detection system0
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion DetectorsCode1
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System0
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet DataCode1
Adversarial Machine Learning in Network Intrusion Detection Systems0
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm0
Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks0
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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