Outlier detection: A survey on techniques of WSNs involving event and error based outliers

Shukla, Deep Shikha; Pandey, Avinash Chandra; Kulhari, Ankur

doi:10.1109/cipech.2014.7019101

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…Spectral method aims to find the normal behavior mode in the data by using the principle component [1]. In the early step of outlier detection, spectral decomposition-based approach uses PCA to reduce dimensionality [55]. Similar to [55], Zolotukhin et al [56], use PCA to reduce dimensionality of feature vectors corresponding with web resources.…”

Section: Anomaly Detection Methodsmentioning

confidence: 99%

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Network anomaly detection research: a survey

Kurniabudi

Purnama

Sharipuddin

et al. 2019

IJEEI

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Data analysis to identifying attacks/anomalies is a crucial task in anomaly detection and network anomaly detection itself is an important issue in network security. Researchers have developed methods and algorithms for the improvement of the anomaly detection system. At the same time, survey papers on anomaly detection researches are available. Nevertheless, this paper attempts to analyze futher and to provide alternative taxonomy on anomaly detection researches focusing on methods, types of anomalies, data repositories, outlier identity and the most used data type. In addition, this paper summarizes information on application network categories of the existing studies.

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Section: Anomaly Detection Methodsmentioning

confidence: 99%

“…In the early step of outlier detection, spectral decomposition-based approach uses PCA to reduce dimensionality [55]. Similar to [55], Zolotukhin et al [56], use PCA to reduce dimensionality of feature vectors corresponding with web resources. PCA is the most common method used for analysis high-dimensionality data [40].…”

Section: Anomaly Detection Methodsmentioning

confidence: 99%

Network anomaly detection research: a survey

Kurniabudi

Purnama

Sharipuddin

et al. 2019

IJEEI

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“…This table reveals how each defined technique can be applied for outlier detection in WSN based on their characteristics, usability, and drawbacks. [88,190] Spectral The detection performance is highly depending on the choices of features and distance measure Robust to parameter perturbations and good performances with different anomaly scoring metrics [117,191] Gaussian Use of the spatial correlation to determine outlying sensors and event boundaries…”

Section: Spectral Decomposition-based Approachesmentioning

confidence: 99%

A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

et al. 2020

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A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

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“…Several approaches exist to tackle the issues of data quality in outliers [22], noise [18], inconsistency [23], incompleteness [24], redundancy [26], [27], amount of data [28]- [30], heterogeneity [14], and timeliness [25]. Nevertheless the results to date not consider resolve the issues in ensemble.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

“…In Table 2 we presented the approaches to fix the issues found in DQD phase from the review works [18], [22]- [24] and the research [25]. …”

Section: Clean Datamentioning

confidence: 99%

A Conceptual Framework for Data Quality in Knowledge Discovery Tasks (FDQ-KDT): A Proposal

Corrales¹,

Ledezma²,

Corrales³

2015

JCP

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Large Volume of Data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through data mining and data science methodologies. Nevertheless these not tackle the issues in data quality clearly, leaving out relevant activities. We proposed a conceptual framework for data quality in knowledge discovery tasks based on CRISP-DM, SEMMA and Data Science, considering the issues of ESE Taxonomy.

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Outlier detection: A survey on techniques of WSNs involving event and error based outliers

Cited by 7 publications

References 20 publications

Network anomaly detection research: a survey

Network anomaly detection research: a survey

A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

A Conceptual Framework for Data Quality in Knowledge Discovery Tasks (FDQ-KDT): A Proposal

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