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 611620 of 800 papers

TitleStatusHype
Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation0
Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review0
A cognitive based Intrusion detection system0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System0
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
SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems0
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series0
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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
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1MSTREAM-AEAUC0.9Unverified