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Credit Card Fraud Detection Using Hidden Markov Models
Post: #1

I am astudent of one of the leading colleges in Nigeria, and in respect to this we were ask to write a good project, i saw this this topic and i am interested in it and need assistance and also would want to add my own idea in anyway to improve the topic.

i would be very gratefull if my situation is been considered and rendered quick reply.
Post: #2
The use of credit cards has dramatically increased due to development in the e- commerce technology. cases of Credit card fraud are also increasing.
Credit-card-based purchases can be categorized into two
types: 1) physical card
the cardholder presents his card physically
to a merchant for making a payment.
an attacker has to steal
the credit card to show fraud in this case.


2) virtual card.
Here, card number, expiration
date, secure code or similar information are required to make the payment.
The only way to detect
his kind of fraud is to analyze the spending patterns
.

HMM(Hidden markov model) BACKGROUND

An HMM is a double embedded stochastic process with two
hierarchy levels which can be used to model much more
complicated stochastic processes as compared to a traditional
Markov model. An HMM has a finite set of states governed by
a set of transition probabilities. In a given state, an
outcome can be generated according to an
associated probability distribution. only the outcome that is visible to an external observer and not the state . Fields like speech recognition, bioinformatics, and genomics use the HMM. HMM is also used in anomaly detection. HMM was used to model human behavior.
HMM can be characterized by the following:
N is the number of states in the model. the set of states is denoted as S={S1 ; S2 ; . . . SN}
; . . . ; N is an individual state. The state at time
instant t is denoted by qt .
M is the number of distinct observation symbols per
state. The observation symbols correspond to the
physical output of the system being modeled. We
denote the set of symbols V={V1 ; V2 ; . . . VM}

USE OF HMM FOR CREDIT CARD FRAUD DETECTION
A FDS(fraud detection system) runs at a credit card issuing bank. It is sent the card details and the value of purchase to verify
whether the transaction is genuine or not and tries to find any anomaly in the transaction. This calculation is based on spending profile of the cardholder, shipping address, and
billing address, etc.If found to be fraudulent, it raises an alarm, and the issuing bank denies the transaction.

HMM Model for Credit Card Transaction Processing
A credit cardholder makes different kinds of purchases of

different amounts over a period of time.The sequence of types of
purchase is more stable compared to the sequence of
transaction amounts. The set of all possible types of purchase and,
equivalently, the set of all possible lines of business of
merchants forms the set of hidden states of the HMM. the actual items purchased
in the transaction are not determined. After deciding the state and symbol representations, the
next step is to determine the probability matrices A, B, and
mu so that representation of the HMM is complete.

Dynamic Generation of Observation Symbols

we train and maintain an HMM for each cardholder the amount
that the cardholder spent in his transactions are determined from the bank database and K-means clustering algorithm to determine the clusters. The grouping is performed by minimizing the sum of
squares of distances between each data point and the
centroid of the cluster to which it belongs.

Spending profile of cardholders
suggests his normal spending behavior. These are are determined at the
end of the clustering step.

Fraud Detection

After the HMM parameters are learned, we take the
symbols from a cardholderâ„¢s training data and form an
initial sequence of symbols. We input
this sequence to the HMM and compute the probability of
acceptance by the HMM.We input this
new sequence to the HMM formed by dropping O and calculate the probability of
acceptance by the HMM. If O is malicious, the issuing bank
does not approve the transaction, but in other cases is added in the sequence perma-nently, and the new sequence is used as the base sequence for
determining the validity of the next transaction.

Fullseminar report available in the link:
ieeexplore.ieeeiel5/8858/4447479/04358713.pdf

you need IEEE subscription to access it . Your instituition must be having it probably.
Post: #3
please read http://project-seminars.com/Thread-credi...dels--5664 and http://project-seminars.com/Thread-fraud...rkov-model and http://project-seminars.com/Thread-credi...kov-models
and http://project-seminars.com/Thread-credi...rkov-model for getting detailed information of Credit Card Fraud Detection Using Hidden Markov Models Related information
Post: #4

.doc  CreditCardFraudDetectionUsingHiddenMarkovModel.doc (Size: 102.5 KB / Downloads: 603)
Credit Card Fraud Detection Using Hidden Markov Model

ABSTRACT

Purpose
The purpose of this document is to present a detailed description of the Credit-card- based purchases can be categorized into two types: 1) physical card and 2) virtual card. In a physical-card based purchase, the cardholder presents his card physically to a merchant for making a payment. To carry out fraudulent transactions in this kind of purchase, an attacker has to steal the credit card. If the cardholder does not realize the loss of card, it can lead to a substantial financial loss to the credit card company. In the second kind of purchase, only some important information about a card (card number, expiration date, secure code) is required to make the payment. Such purchases are normally done on the Internet or over the telephone. To commit fraud in these types of purchases, a fraudster simply needs to know the card details. Most of the time, the genuine cardholder is not aware that someone else has seen or stolen his card information. The only way to detect this kind of fraud is to analyze the spending patterns on every card and to figure out any inconsistency with respect to the “usual” spending patterns.

Post: #5


.doc  Final Document.doc (Size: 928 KB / Downloads: 438)

Credit Card Fraud Detection Using Hidden Markov Model

Abstract
:

Now a day the usage of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature
Post: #6

.doc  IDNS03.doc (Size: 1.49 MB / Downloads: 311)
Credit Card Fraud Detection Using
Hidden Markov Model

Abstract:

Now a day the usage of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature




Introduction
Credit-card-based purchases can be categorized into two types: 1) physical card and 2) virtual card. In a physical-card based purchase, the cardholder presents his card physically to a merchant for making a payment. To carry out fraudulent transactions in this kind of purchase, an attacker has to steal the credit card. If the cardholder does not realize the loss of card, it can lead to a substantial financial loss to the credit card company. In the second kind of purchase, only some important information about a card (card number, expiration date, secure code) is required to make the payment. Such purchases are normally done on the Internet or over the telephone. To commit fraud in these types of purchases, a fraudster simply needs to know the card details. Most of the time, the genuine cardholder is not aware that someone else has seen or stolen his card information. The only way to detect this kind of fraud is to analyze the spending patterns on every card and to figure out any inconsistency with respect to the “usual” spending patterns. Fraud detection based on the analysis of existing purchase data of cardholder is a promising way to reduce the rate of successful credit card frauds. Since humans tend to exhibit specific behaviorist profiles, every cardholder can be represented by a set of patterns containing information about the typical purchase category, the time since the last purchase, the amount of money spent, etc. Deviation from such patterns is a potential threat to the system.

http://project-seminars.com/Thread-credi...-using-hmm

http://project-seminars.com/Thread-credi...del--12225
Post: #7
This article is presented by:Erik L.L. Sonnhammer
Gunnar von Heijne
Anders Krogh
A hidden Markov model for predicting transmembrane helices in protein
sequences




Abstract
A novel method to model and predict the location and orientation of alpha helices in membrane- spanning proteins is presented. It is based on a hidden Markov model (HMM) with an architecture that corresponds closely to the biological system. The model is cyclic with 7 types of states for helix core, helix caps on either side, loop on the cytoplasmic side, two loops for the non-cytoplasmic side, and a globular domain state in the middle of each loop. The two loop paths on the non-cytoplasmic side are used to model short and long loops separately, which corresponds biologically to the two known different membrane insertions mechanisms. The close mapping between the biological and computational states allows us to infer which parts of the model architecture are important to capture the information that encodes the membrane topology, and to gain a better understanding of the mechanisms and constraints involved. Models were estimated both by maximum likelihood and a discriminative method, and a method for reassignment of the membrane helix boundaries were developed. In a cross validated test on single sequences, our transmembrane HMM, TMHMM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 83 proteins with known topology. The same accuracy was achieved on a larger dataset of 160 proteins. These results compare favourably with existing methods.
Introduction.
Prediction of membrane-spanning alpha helices in proteins is a frequent sequence analysis objective. A large portion of the proteins in a genome encode integral membrane proteins (Himmelreich et al. 1996; Frishman & Mewes 1997; Wallin & von Heijne 1998). Knowledge of the presence and exact location of the transmembrane helices is important for functional annotation and to direct functional analysis. Transmembrane helices are substantially easier to predict than helices in globular domains. Predicting 95% of the transmembrane helices in the ‘correct’ location is not unusual (Cserzo et al. 1997; Rost et al. 1995). By ‘correct’ is meant that the prediction overlaps the true location. The reason for this high accuracy is that most transmembrane alpha helices are encoded by an unusually long stretch of hydrophobic residues. This compositional bias is imposed by the constraint that residues buried in lipid membranes must be suitable for hydrophobic interactions with the lipids. The hydrophobic signal is so strong that a straightforward approach of calculating a propensity scale for residues in transmembrane helices and applying a sliding window with a cutoff already performs quite well. In addition to knowing the location of a transmembrane helix, knowledge of its orientation, i.e. whether it runs inwards or outwards, is also important for making functional inferences for different parts of the sequence. The orientations of the transmembrane helices give the overall topology of the protein. It is known that the positively charged residues arginine and lysine play a major role in determining the orientation as they are mainly found in non-transmembrane parts of the protein (‘loops’) on the cytoplasmic side (von Heijne 1986; Jones, Taylor, & Thornton 1994; Persson & Argos 1994; Wallin &von Heijne 1998), often referred to as the ‘positiveinside rule’. Since the rule also applies to proteins in the membrane of intracellular organelles (Gavel et al. 1991; Gavel & von Heijne 1992), we shall use the terms ‘cytoplasmic’ and ‘non-cytoplasmic’ for the two sides of a membrane. The difference in amino acid usage between cytoplasmic and non-cytoplasmic loops can be exploited to improve the prediction of transmembrane helices by validating potential transmembrane helices by the charge bias they would produce (von Heijne 1992). Despite this relatively consistent topogenic signal, correct prediction of the location and orientation of all transmembrane segments has proved to be a difficult problem. On a reasonably large dataset of single sequences, a topology accuracy of 77% has been reported (Jones, Taylor, & Thornton 1994), and aided with multiple alignments 86% (Rost, Fariselli, & Casadio 1996). The difficulty in predicting the topology seems to be partly caused by the fact that the positive-inside rule can be blurred by globular domains in loops on the non-cytoplasmic side that contain a substantial number of positively charged residues.
For more information about this article,please follow the link:
http://www.googleurl?sa=t&source=web&cd=...98-021.pdf&ei=uy-5TJCyI8jzcea2wM8M&usg=AFQjCNHvDbAtMmG7BvGBiYuKp3-E6-dL9w
Post: #8
PLEASE HELP ME IN THIS PROJECT !!!!
Post: #9

.ppt  Lecture5-hmm.ppt (Size: 1.21 MB / Downloads: 155)

Hidden Markov Models
Bonnie Dorr Christof Monz
CMSC 723: Introduction to Computational Linguistics

Hidden Markov Model (HMM)

HMMs allow you to estimate probabilities of unobserved events
Given plain text, which underlying parameters generated the surface
E.g., in speech recognition, the observed data is the acoustic signal and the words are the hidden parameters
Post: #10
Can u please send me the full report on this topic as soon as possible...It will be very helpfull.
Post: #11
hi pratik.bhatia007ia,

you can download the report from above post.
Post: #12
Speech Recognition with Hidden Markov Models



HMMs for Speech
Speech is the output of an HMM; problem is to find most likely model for a given speech observation sequence.

Speech is divided into sequence of 10-msec frames, one frame per state transition (faster processing). Assume speech can be recognized using 10-msec chunks.


HMMs for Speech
Each state can be associated with  sub-phoneme  phoneme
 sub-word

Usually, sub-phonemes or sub-words are used, to account for spectral dynamics (coarticulation).

One HMM corresponds to one phoneme or word

For each HMM, determine the probability of the best state sequence that results in the observed speech.

Choose HMM with best match (probability) to observed speech.

Given most likely HMM and state sequence, maybe determine the corresponding phoneme and word sequence.
7-state word model for “cat” with null states

Null states do not emit observations, and are entered and exited at the same time t. Theoretically, they are unnecessary. Practically, they can make implementation easier.

States don’t have to correspond directly to phonemes, but are commonly labeled using phonemes.


This permits several different models for each phoneme, depending on surrounding phonemes (context sensitive)

k-ae+t
p-ae+t
k-ae+p

Probability of “illegal” state sequence is zero (never used) sil-k+ae p-ae+t


Much larger number of states to train on… (50 vs. 125,000 for a full set of phonemes, 39 vs. 59,319 for reduced set).


For more information about this article,please follow the link:3
http://www.cslu.ogi.edu/people/hosom/cs5...speech.ppt
Post: #13
ieee pdf for this project
Post: #14
sorry , we don't have ieee pdf for this topic
Post: #15
Hi,
try this link for the pdf:
http://www.wiphalaresearch/proposal/pape..._model.pdf
 

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