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 441–450 of 800 papers

TitleStatusHype
The Dendritic Cell Algorithm for Intrusion Detection—0
The Effective Methods for Intrusion Detection With Limited Network Attack Data: Multi-Task Learning and Oversampling—0
The Efficacy of Transformer-based Adversarial Attacks in Security Domains—0
The importance of the clustering model to detect new types of intrusion in data traffic—0
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey—0
Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System—0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
Time-Based CAN Intrusion Detection Benchmark—0
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data—0
Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks—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