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 531–540 of 800 papers

TitleStatusHype
Intrusion Detection in Internet of Things using Convolutional Neural Networks—0
Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset—0
Intrusion detection in IoT using artificial neural networks on UNSW-15 dataset—0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification—0
Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model—0
Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning—0
Intrusion Detection Systems Using Adaptive Regression Splines—0
Intrusion detection systems using classical machine learning techniques versus integrated unsupervised feature learning and deep neural network—0
Intrusion Detection Systems Using Support Vector Machines on the KDDCUP'99 and NSL-KDD Datasets: A Comprehensive Survey—0
Intrusion Detection System with Machine Learning and Multiple Datasets—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