A Survey of Outlier Detection Methods in Network Anomaly Identification

Gogoi, Prasanta; Bhattacharyya, Dhruba K.; Borah, Bhogeswar; Kalita, Jugal

doi:10.1093/comjnl/bxr026

Cited by 209 publications

(116 citation statements)

References 66 publications

(62 reference statements)

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“…Window based method [22,23]. The time series is divided into several fixed size series (window), and then locate the outliers in each series.…”

Section: Outlier Detectionmentioning

confidence: 99%

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

Wang

2017

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Abstract:In order to detect outliers in temperature time series data for improving data quality and decision-making quality related to design and operation, we proposed an algorithm based on sliding window prediction. Firstly, the time series are segmented based on the sliding window. Then, the prediction model is established based on the history data to predict the future value. If the difference between a predicted value and a measured value is larger than the preset threshold value, the sequence point will be judged to be an outlier and then corrected. In this paper, the sliding window and parameter settings of the algorithm are discussed and the algorithm is verified on actual data. This method does not need to pre classify the abnormal points and perform fast, and can handle large scale data. The experimental results show that the proposed algorithm can not only effectively detect outliers in the time series of meteorological data but also improves the correction efficiency notoriously.

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“…Window based method [22,23]. The time series is divided into several fixed size series (window), and then locate the outliers in each series.…”

Section: Outlier Detectionmentioning

confidence: 99%

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

Wang

2017

Information

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“…A time series is a collection of temporal data objects, such as large data size, high dimensionality, and continuous updating (Fu, 2011). Outlier detection refers to the problem of finding patterns in data that are very different from the rest of the data, based on appropriate metrics (Gogoi et al, 2011). Silva et al (2013) proposed big data mining system for IoT, focusing on the integration with devices and data mining technologies supported by cloud computing IoT.…”

Section: Data Miningmentioning

confidence: 99%

The internet of things sensors technologies and their applications for complex engineering projects: a digital construction site framework

Miranda

Bezerra

et al. 2017

BJO&PM

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Purpose: The paper aims to present constructs related to Internet of Things, which are expected to be integrated to, and improve the implantation phase of Complex Engineering Projects. The traditional implantation methods for Complex Engineering Projects are neither effective nor efficient to deal with the massive data generated by their productive processes. It usually causes high levels of rework, schedule delays and financial losses. Design/methodology: In this paper a systematic literature review on IoT architecture, sensor technologies and possible applications was presented. These constructs were identified and selected from a lexical analysis of a reseach basis carried out with NVivo software. Findings: We have been in the emerging 4 th Industrial Revolution and the Internet of Things (IoT) has a valuable role to make an easier way to success for Complex Project implementations. Therefore, a correlation scheme of the constructs and their applications identified in the bibliography was proposed. Originality/value: A concept and a framework for a "Digital Construction Site" (DCS) from the application of IoT Sensors (IoTS) in the shipbuilding industry. It is valuable for readers who want to understand alternatives to improve the results of typical processes on implementation phase of Complex Engineering Projects, with open source technologies.

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“…[7]). The first category can be further split into parametric [8] and non-parametric [9] methods according to how the statistical model is built.…”

Section: Outliers Detection and Accommodationmentioning

confidence: 99%

A Multi-Agent System for Outliers Accommodation in Wireless Sensor Networks

Gil¹,

Palma²,

Santos

et al. 2014

IJCEE

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Abstract:In monitoring applications the accuracy of data is paramount. When considering wireless sensor networks the quality of readings taken from the environment may be hampered by outliers in raw data collected from transmitters attached to nodes' analogue-to-digital converter ports. To improve the data quality sent to the base-station, a real-time data analysis should be implemented at nodes' level, while taking into account their computing power and storage limitations. This paper deals with the problem of outliers detection and accommodation in raw data. The proposed approach relies on univariate statistics within an hierarchical multi-agent framework. Results from experiments on a real monitoring scenario, at a major oil refinery plant, show the relevance of the proposed approach.

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A Survey of Outlier Detection Methods in Network Anomaly Identification

Cited by 209 publications

References 66 publications

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

The internet of things sensors technologies and their applications for complex engineering projects: a digital construction site framework

A Multi-Agent System for Outliers Accommodation in Wireless Sensor Networks

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