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 521–530 of 800 papers

TitleStatusHype
Integrating Sensing and Communication in Cellular Networks via NR Sidelink—0
Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security—0
Intelligent Green Efficiency for Intrusion Detection—0
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques—0
A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS—0
Intrusion Detection: A Deep Learning Approach—0
Intrusion Detection and Localization for Networked Embedded Control Systems—0
Intrusion Detection at Scale with the Assistance of a Command-line Language Model—0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems—0
Intrusion detection in computer systems by using artificial neural networks with Deep Learning approaches—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