Data stream outlier detection approach based on frequent pattern mining technique

Said, Aiman Moyaid; Dominic, P. D. D.; Faye, Ibrahima

doi:10.1504/ijbis.2015.070892

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The similarity provides the basis for applying outlier detection algorithm in intrusion detection. So far, the following outlier detection algorithms have been introduced to intrusion detection, including frequent pattern-based algorithm [19], density-based algorithm [20], and depth-based algorithm [21]. The previous studies have confirmed that both outlier detection and intrusion detection focus on the following aspects: data processing, model construction, method application, and technology selection.…”

Section: Literature Reviewmentioning

confidence: 99%

A Neighbor Propagation Clustering Algorithm for Intrusion Detection

Li¹

2020

RIA

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Outlier detection is an important research direction in data mining. This paper mainly applies outlier detection algorithm in intrusion detection, and proposes a novel outlier detection algorithm based on neighbor propagation clustering. The proposed algorithm first clusters the original dataset, then calculates the outlier degree, and finally mines the intrusion behaviors. To verify its effectiveness, our algorithm was tested on public dataset on network intrusions. The results prove that our algorithm can detect intrusions with a high accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

A Neighbor Propagation Clustering Algorithm for Intrusion Detection

Li¹

2020

RIA

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“…There have been many efforts on improving the efficiency of such methods, which can be categorized into: (a) parametric (statistical based) methods and (b) nonparametric methods [20]. The former [21][22][23] assumes a stochastic distribution for observations. It labels the observations as outliers if there is a significant difference between the observations and the model assumptions.…”

Section: Related Work Smentioning

confidence: 99%

Temporal and spatial outlier detection in wireless sensor networks

Nguyen

Thai

2019

ETRI Journal

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Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph‐based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state‐of‐the‐art methods in terms of accuracy and work well with various data types.

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