Detecting Abnormal Behavior in Social Network Websites by Using a Process Mining Technique

Sahlabadi, Mahdi; Muniyandi, Ravie Chandren; Shukur, Zarina

doi:10.3844/jcssp.2014.393.402

Cited by 30 publications

(10 citation statements)

References 12 publications

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“…The maximum fitness measurement value is used as an assessment criterion, i.e., f = 1. The following event logs have been used for evaluation (ProM, 2010) (Sahlabadi et al, 2014;2017).…”

Section: Resultsmentioning

confidence: 99%

Distributed Learning Automata Approach for Workflow Mining: Discovering Process Model Using Condensate Drops Method

Naderifar

Shukur

Sahran

2019

Journal of Computer Science

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An information system is a process of collecting, processing, storing and distributing information, which leads to efficient decisionmaking and control in organizations. Examples of information systems include classical management systems, systems for workflow management, systems for case handling and middleware. Information systems collect information concerning important people, locations and other important matters in an organization and store relevant events in some form of structure. Based on event logs, from information systems, the discovery of process models can be made automatically by process mining techniques, without having an a priori model. By learning from the event logs, process mining aims to discover, monitor and improve processes. This paper proposes a method to discover a process model based on distributed learning automata and the condensate approach. In this proposed method, each event in the log is called a drop, which had its condensate and can be combined with other condensates. Each drop is connected to other drops and become a larger drop. All of those drops would obtain reward if it represents sequence of an event log. The evaluation results demonstrated that the proposed method could detect various patterns in the event log and discover a more efficient process model in terms of fitness, total node and total path of the mined process model.

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“…The maximum fitness measurement value is used as an assessment criterion, i.e., f = 1. The following event logs have been used for evaluation (ProM, 2010) (Sahlabadi et al, 2014;2017).…”

Section: Resultsmentioning

confidence: 99%

Distributed Learning Automata Approach for Workflow Mining: Discovering Process Model Using Condensate Drops Method

Naderifar

Shukur

Sahran

2019

Journal of Computer Science

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“…Committed to. There will be a lot of false news for most of the day [2]. This requires tracking all false news and keeping the database for future reference [10], [12].…”

Section: Proposed Methodologymentioning

confidence: 99%

Detecting and Analyzing the Malicious Social Bots by using Data Mining and Naïve Bayesian Classifier

M¹

2020

IJETER

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With the huge increase in the number, speed, and variety of customer information (such as customer-generated information) in online interpersonal organizations, people have worked hard to construct better methods to collect and inspect such large information. For example, social robots have been used to perform scientific management of robots and provide customers with improved management attributes. Even so, harmful social robots are used to spread false data (for example, false news), which may bring real results. Therefore, identifying and evacuating harmful social robots in online informal communities is crucial. The latest discovery technology of resentful social robots undermines the quantitative focus of its behavior. These highlights are easily imitated by social robots. This leads to a reduction in the accuracy of the investigation. This article describes an epic strategy for identifying malicious social robots, which includes two key options, depending on the possibility of snapshot stream grouping and semi-hosted binding changes. The technology not only investigates the possibility of changes in the click stream of customer behavior, but also takes into account the temporal highlights of the behavior.

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“…Depending on the scenario, different are the points of view that could be used to discover a process, such as the activities executed, persons involved, the resources used, the location where the actions occur, etc. The versatility of process mining techniques has brought about its application to several scenarios (Dakic et al 2018), healthcare (Mans et al 2009;Rozinat et al 2009;Perimal-Lewis et al 2016) and IT (Sahlabadi et al 2014;Mȃruşter and van Beest 2009;Pérez-´A lvarez et al 2018;Fernández-Cerero et al 2019) being the most active areas.…”

Section: Application Of Process Mining In Different Contextsmentioning

confidence: 99%

Discovering configuration workflows from existing logs using process mining

et al. 2021

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Variability models are used to build configurators, for guiding users through the configuration process to reach the desired setting that fulfils user requirements. The same variability model can be used to design different configurators employing different techniques. One of the design options that can change in a configurator is the configuration workflow, i.e., the order and sequence in which the different configuration elements are presented to the configuration stakeholders. When developing a configurator, a challenge is to decide the configuration workflow that better suits stakeholders according to previous configurations. For example, when configuring a Linux distribution the configuration process starts by choosing the network or the graphic card and then, other packages concerning a given sequence. In this paper, we present COnfiguration workfLOw proceSS mIning (COLOSSI), a framework that can automatically assist determining the configuration workflow that better fits the configuration logs generated by user activities given a set of logs of previous configurations and a variability model. COLOSSI is based on process discovery, commonly used in the process mining area, with an adaptation to configuration contexts. Derived from the possible complexity of both logs and the discovered processes, often, it is necessary to divide the traces into small ones. This provides an easier configuration workflow to be understood and followed by the user during the configuration process. In this paper, we apply and compare four different techniques for the traces clustering: greedy, backtracking, genetic and hierarchical algorithms. Our proposal is validated in three different scenarios, to show its feasibility, an ERP configuration, a Smart Farming, and a Computer Configuration. Furthermore, we open the door to new applications of process mining techniques in different areas of software product line engineering along with the necessity to apply clustering techniques for the trace preparation in the context of configuration workflows.

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Detecting Abnormal Behavior in Social Network Websites by Using a Process Mining Technique

Cited by 30 publications

References 12 publications

Distributed Learning Automata Approach for Workflow Mining: Discovering Process Model Using Condensate Drops Method

Distributed Learning Automata Approach for Workflow Mining: Discovering Process Model Using Condensate Drops Method

Detecting and Analyzing the Malicious Social Bots by using Data Mining and Naïve Bayesian Classifier

Discovering configuration workflows from existing logs using process mining

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