Sequential fraud detection for prepaid cards using hidden Markov model divergence

Robinson, William; Aria, Andrea

doi:10.1016/j.eswa.2017.08.043

Cited by 57 publications

(25 citation statements)

References 23 publications

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“…HMM depends on two main properties, which are: − The observation at time t is produced by a process whose state Ht is hidden from the observer. − The state of the hidden process represents the Markov chain [16][17][18][19][20][21][22][23].…”

Section: Hmmmentioning

confidence: 99%

Viterbi optimization for crime detection and identification

2020

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In this paper, we introduce two types of hybridization. The first contribution is the hybridization between the Viterbi algorithm and Baum Welch in order to predict crime locations. While the second contribution considers the optimization based on decision tree (DT) in combination with the Viterbi algorithm for criminal identification using Iraq and India crime dataset. This work is based on our previous work [1]. The main goal is to enhance the results of the model in both consuming times and to get a more accurate model. The obtained results proved the achievement of both goals in an efficient way.

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Section: Hmmmentioning

confidence: 99%

Viterbi optimization for crime detection and identification

2020

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“…One of the important applications of intrusion detection is detecting fraud in credit card transactions [1,43,69,86,93]. In these cases, the goal is to identify abnormal credit card behavior which can be classified as fraud.…”

Section: Credit Card Fraud Detectionmentioning

confidence: 99%

“…One example of the sequence of transactions can be shown as llmhmlhm. The types of purchases such [13], [27], [43], [86], [93], [101], [107] [2], [34], [39], [50], [59], [113], [118] [30], [41], [77], [83] [4], [8], [17], [36], [38], [42] [6], [7], [18], [48], [74], [92] [28], [33], [40], [47], [53], [67], [71], [97], [105], [114], [116], [119] [54], [55], [112] [15], [70], [77], [95], [99], [102] [10], [44], [66] [41], [53], [56], [74],…”

Section: Credit Card Fraud Detectionmentioning

confidence: 99%

A systematic review on intrusion detection based on the Hidden Markov Model

Ramaki

Rasoolzadegan

Jafari

2018

Statistical Analysis

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Apart from using traditional security solutions in software systems such as firewalls and access control mechanisms, utilizing intrusion detection systems are also necessary. Intrusion detection is a process in which a set of methods are used to detect malicious activities against the victims. Many techniques for detecting potential intrusions in software systems have already been introduced. One of the most important techniques for intrusion detection based on machine learning is using Hidden Markov Models (HMM). In recent decades, many research communities have been working toward HMM-based intrusion detection. Therefore, a large volume of research works has been published and hence, various research areas have emerged in this field. However, until now, there has been no systematic and up-to-date review of research works within the field. This paper aims to survey the research in this field and provide open problems and challenges based on the analysis of advantages, limitations, types of architectural models, and applications of current techniques. Six various architecture models for intrusion detection purposes are proposed in the literature. We compare these models based on performance criteria in order to select an appropriate type for a specific application. The results show that HMM-based intrusion detection techniques have 6 main advantages-precise intrusion detection, ability to detect new and unknown intrusions, prediction of the intruder's potential next steps, usage in real-time applications by processing data streams on-the-fly, usage of heterogeneous data sources as input, and visual representation of acquired knowledge relative to the other techniques of machine learning. KEYWORDSHidden Markov Model, intrusion detection, intrusion detection system, statistical learning, system and network security

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“…Also, the system had been measurable to manage enormous volumes of transactions. William N. Robinson et.al [33] have been suggested an sequential fraud finding system utilizing HMM divergence. .…”

Section: Saravanan Et Al International Journal Of Emerging Researchmentioning

confidence: 99%

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

Saravanan

Babu²

2017

IJERMT

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In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

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Sequential fraud detection for prepaid cards using hidden Markov model divergence

Cited by 57 publications

References 23 publications

Viterbi optimization for crime detection and identification

Viterbi optimization for crime detection and identification

A systematic review on intrusion detection based on the Hidden Markov Model

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

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