Self-adaptive statistical process control for anomaly detection in time series

Zheng, Dequan; Li, Fenghuan; Zhao, Tiejun

doi:10.1016/j.eswa.2016.03.029

Cited by 32 publications

(11 citation statements)

References 33 publications

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“…Dumicic and Zmuk use the I-MR control chart to examine the quality of stock trading in Croatia. The results show similarities with both previous studies [6]. The similarity of those three studies is the using the I-MR control chart.…”

Section: Introductionsupporting

confidence: 88%

Evaluation of Research Standards at Ministry of Research, Technology and Higher Education with I-MR Map Control Analysis

Dimyati

Fauzy

2018

TELKOMNIKA

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Section: Introductionsupporting

confidence: 88%

Evaluation of Research Standards at Ministry of Research, Technology and Higher Education with I-MR Map Control Analysis

Dimyati

Fauzy

2018

TELKOMNIKA

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“…Zheng et al [23] assume that anomaly detection is a statistical hypothesis testing, based on which they developed a novel model based on a synergistic combination of statistical and fuzzy set-based technique. They also adopted an intensive fuzzification process to determine the parameters, so users do not have to input the parameters.…”

Section: Related Workmentioning

confidence: 99%

Generalized Pareto Model Based on Particle Swarm Optimization for Anomaly Detection

Huang

Chen

et al. 2019

IEEE Access

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Anomaly detection of time series has been widely used in various fields. Most detection methods depend either on assumptions about data distribution or manual threshold setting. If the assumption is incorrect, the effectiveness of detection technology will be greatly reduced. To deal with this problem, we propose a maximum likelihood estimation method based on particle swarm optimization for generalized Pareto model to detect outliers of time series, which can be called Generalized Pareto Model Based on Particle Swarm Optimization (GPMPSO). Because the generalized Pareto model is multidimensional, we introduce a comprehensive learning strategy to improve search ability of particle swarm algorithm. Due to the multiple peaks of the log-likelihood function of generalized Pareto model, we apply dynamic neighbors to reduce the possibility of particle swarm optimization falling into local optimum. Moreover, we propose a new processing model Big Drift Streaming Peak Over Threshold (BDSPOT) to enhance the capability of the data stream processor. Our algorithm is tested on various real-world datasets which demonstrate its very competitive performance. INDEX TERMSAnomaly detection, generalized pareto distribution, particle swarm optimization, time series. JIAN CHEN received the Ph.D. degree from Zhejiang University, Hangzhou, China. He is currently a Research Professor with the Third Institute of Oceanography, Ministry of Natural Resources, China. He have undertaken many marine geology investigation projects, such as Multiple Dis-

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“…This happens when it is important to characterize the involved elements on the basis of the time factor [22]. The information extracted from the time series can be exploited in order to perform different tasks, such as those related to the risk analysis (e.g., Credit Scoring [23] and Stock Forecasting [24]) and Information Security (e.g., Fraud Detection [25] and Intrusion Detection [26]) ones.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the benefits of using proactive transformed-domain-based techniques in fraud detection tasks

Saia

Carta

2019

Future Generation Computer Systems

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The exponential growth in the number of E-commerce transactions indicates a radical change in the way people buy and sell goods and services, a new opportunity offered by a huge global market, where they may choose sellers or buyers on the basis of multiple criteria (e.g., economic, logistical, ethical, sustainability, etc.), without being forced to use the traditional brick-and-mortar criterion. If, on the one hand, such a scenario offers an enormous control to people, both at private and corporate level, allowing them to filter their needs by adopting a large range of criteria, on the other hand, it has contributed to the growth of fraud cases related to the involved electronic instruments of payment, such as credit cards. The Big Data Information Security for Sustainability is a research branch aimed to face these issues in relation to the potential implications in the field of sustainability, proposing effective solutions to design safe environments in which the people can operate and by exploiting the benefits related to new technologies. The fraud detection systems are a significant example of such solutions, although the techniques adopted by them are typically based on retroactive strategies, which are incapable of preventing fraudulent events. In this perspective, this paper aims to investigate the benefits related to the adoption of proactive fraud detection strategies, instead of the canonical retroactive ones, theorizing those solutions that can lead toward practical effective implementations We evaluate two previously experimented novel proactive strategies, one based on the Fourier transform, and one based on the Wavelet transform, which are used in order to move the data (i.e., financial transactions) into a new domain, where they are analyzed and an evaluation model is defined. Such strategies allow a fraud detection system to operate by using a proactive approach, since they do not exploit previous fraudulent transactions, overcoming some important problems that reduce the effectiveness of the canonical retroactive state-ofthe-art solutions. Potential benefits and limitations of the proposed proactive approach have been evaluated in a real-world credit card fraud detection scenario, by comparing its performance to that of one of the most used and performing retroactive state-of-theart approaches (i.e. Random Forests).

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Self-adaptive statistical process control for anomaly detection in time series

Cited by 32 publications

References 33 publications

Evaluation of Research Standards at Ministry of Research, Technology and Higher Education with I-MR Map Control Analysis

Evaluation of Research Standards at Ministry of Research, Technology and Higher Education with I-MR Map Control Analysis

Generalized Pareto Model Based on Particle Swarm Optimization for Anomaly Detection

Evaluating the benefits of using proactive transformed-domain-based techniques in fraud detection tasks

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