Traffic Anomaly Detection Using K-Means Clustering

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A survey of network anomaly detection techniques (2016, methodology): “The approach used by Münz et al. (2007) to anomalous data is quite straightforward.”
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications (2018, methodology): “, one-class SVM [1, 7], clustering based methods [9] like K-Means [26] and GMM [21], KDE [27], and VAE [2] and VRNN [33].”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, methodology): “K-mean clustering method is usually used in anomaly detection [130]-[132] and Sybil attack detection [133], [134].”
A deep learning approach for detecting traffic accidents from social media data (2018, methodology): “The medians of the historical traffic flow values over the same occupancy group are defined as the traffic signature of the detector: 𝐹 (cid:147) = (𝑓 $ (cid:147) , 𝑓 & (cid:147) , ⋯ , 𝑓 (cid:132)(cid:147) ) where 𝑓 }(cid:147) is the median value of traffic flow given a range of occupancy 𝑜 in”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “detection by distinguishing normal behaviour from abnormal behaviour by feature similarity calculations [181], [182].”
Generalized Out-of-Distribution Detection: A Survey (2021, unknown): “3: Distance-based Methods [249], [250], [251]”