Unsupervised and Ensemble-based Anomaly Detection Method for Network Security

Yang, Donghun; Hwang, Myunggwon

doi:10.1109/kst53302.2022.9729061

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…For this reason, unsupervised outlier detection methods based on deep learning have gained wide popularity recently. For example, Autoencoder (AE) [18] performs outlier detection by examining its reconstruction loss. Yao [19] applied Variational Autoencoder (VAE) to extract valuable features for the unsupervised outlier detection tasks.…”

Section: Introductionmentioning

confidence: 99%

Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization

Zheng

Xie

et al. 2023

Mathematics

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As an emerging type of spatio-temporal big data based on positioning technology and navigation devices, vehicle-based crowdsourcing data has become a valuable trajectory data resource. However, crowdsourcing trajectory data has been collected by non-professionals and with multiple measurement terminals, resulting in certain errors in data collection. In these cases, to minimize the impact of outliers and obtain relatively accurate trajectory data, it is crucial to detect and clean outliers. This paper proposes an efficient crowdsourcing trajectory outlier detection (CTOD) method that detects outliers from the trajectory sequence data in both spatial view and temporal view. Specifically, we first use the adaptive spatial clustering algorithm based on the Delaunay triangulation (ASCDT) algorithm to remove the location offset points in the trajectory sequence. After that, based on the most basic attributes of the trajectory points, a 6-dimensional movement feature vector is constructed for each point as an input. The feature-rich trajectory sequence data is reconstructed using the proposed temporal convolutional network autoencoder (TCN-AE), and the Squeeze-and-Excitation (SE) channel attention mechanism is introduced. Finally, the effectiveness of the CTOD method is experimentally verified.

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Section: Introductionmentioning

confidence: 99%

Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization

Zheng

Xie

et al. 2023

Mathematics

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“…However, traffic detection is deemed to be a developing and challenging issue since it needs to deal with various difficulties coming from imbalanced data, increasing network traffic volume, evolving and sophisticated attacks, as well as dynamic and variable network environments [2][3][4][5]. Various techniques have been developed to detect and analyze such anomalies, ranging from statistical-based methods [6][7][8], time series analysis [9][10][11], machine-learning based methods [12][13][14], deep-learning based methods [15][16][17], ensemble methods [18][19][20], flow-based analysis [21][22][23], hybrid methods [24,25], to unsupervised clustering methods [26,27].…”

Section: Introductionmentioning

confidence: 99%

ODFM: Abnormal Traffic Detection Based on Optimization of Data Feature and Mining

2023

IJACSA

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The booming of computer networks and software applications has led to an explosive growth in the potential damage caused by network attacks. Efficient detection of abnormal traffic in networks is appealing for facilely mastering the traffic tracking and locating for network usage at low resource cost. High quality abnormal traffic detection of Internet becomes particularly relevant during the automated services of multiple application situations. This paper proposes a novel abnormal traffic detection algorithm called ODFM based on the optimization of data feature and mining. Specially, we develop a feature selection strategy to reduce the feature analysis dimension, and set a peer-to-peer (P2P) traffic identification module to filter and mine the related service traffic to reduce the amount of data detection and facilitate the abnormal traffic detection. Experimental results demonstrate that the proposed algorithm greatly improves the detection accuracy, which verifies its effectiveness and competitiveness in the general tasks of abnormal network traffic detection.

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Impact of Training Set Size on Resource Usage of Machine Learning Models for IoT Network Intrusion Detection

Asulba,

Schumacher,

Souto

et al. 2023

2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)

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Unsupervised and Ensemble-based Anomaly Detection Method for Network Security

Cited by 3 publications

References 19 publications

Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization

Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization

ODFM: Abnormal Traffic Detection Based on Optimization of Data Feature and Mining

Impact of Training Set Size on Resource Usage of Machine Learning Models for IoT Network Intrusion Detection

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