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 181–190 of 800 papers

TitleStatusHype
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks—0
Bidirectional RNN for Medical Event Detection in Electronic Health Records—0
Big data analysis and distributed deep learning for next-generation intrusion detection system optimization—0
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models—0
Blockchain Large Language Models—0
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data—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