fraud detection using hidden markov model
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10-02-2010, 07:07 PM

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10-02-2010, 08:11 PM

use this link to get ppt of fraud detection using hidden markov model

Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. Consumers rely on the Internet to shop, bank and invest online. Most online shoppers use credit cards to pay for their purchases. As credit card becomes the most popular mode of payment, cases of fraud associated with it are also increasing in both online as well as regular purchase,here a 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, try to ensure that genuine transactions are not rejected. and present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.

Proposed System:
In this system present a hidden morkov model(HMM) Which does not required fraud signatures and yet is able to detect frauds by considering a cardholder's spending habit. Card transaction processing sequence by the stochastic process of an HMM. The details of items purchased in individual transactions are usually not known to an FDS running at the bank that issues credit cards to the cardholder. Hence ,we feel that HMM is an ideal choice for addressing this problem. Another important advantage of the HMM based approach is a drastic reduction is the number reduction in the number of false positives transactions identified a malicious by an FDS although they are actually genuine . An FDS runs at a credit card issuing bank. Each incoming transaction is submitted to the FDS for verification. FDS receives the card details and the values if purchases to verify, whether the transaction is genuine or not. The types of goods that are bought in that transaction are not known to the FDS. It tries to find nay anomaly in the transaction based on the spending profile of the cardholder, shipping address , shipping address, and billing addresses.
1. The detection of the fraud use of the card is found much faster that existing system.
2. In case of the existing system even the original card holder is also checked for fraud detection. But in this system no need check the original user as maintain a log
3. The log which is maintained will also be a proof for the bank for the transaction made.
4. can find the most accurate detection using this technique.
5. This reduce the tedious work of an employee in bank.

In case of the existing system the fraud is detected after the fraud is done that is, the fraud is detected after the complaint of the holder. And so the card holder faced a lot of trouble before the investigation finish. And also as all the transaction is maintained in a log, and need to maintain a huge data, and also now a day's lot of online purchase are made so don't know the person how is using the card online, and just capture the ip address for verification purpose. So there need a help from the cyber crime to investigate the fraud. To avoid the entire above disadvantage , propose the system to detect the fraud in a best easy way.


1. New card

2. Login

3. Security information

4. Transaction

5. Verification

Module Description

New Card:

In this module, the customer gives there information to enroll a new card. The information is all about
There contact details. They can create their own and password for their future use of the card.


In login form module presents site visitors with a form with username and password fields . if the user enters a valid username and password combination they will be granted access to additional resources on websites .Which additional resources they will have access to can be configured separately.

Security information:

In security information module it will get the information details and its store's in database. If the card lost then the information module form arise .it has a set of question where the user has to answer the correctly to move to the transaction section. It contain informational privacy and informational self-determination are addressed squarely by the invention affording persons and entities a trusted means to user, search, process and exchange personal and /or confidential information


The method and apparatus for pre-authorizing transactions includes providing a communications device to a vendor and credit card owner. The credit owner initiates a credit card transaction by communicating to a credit card number, and storing therein, a distinguishing piece of information that characterizes a specific transaction to be made by an authorized user of the credit card at a later time. The information is accepted as "network date" in the data in the date base only if a correct personal identification code (PIC) used with the communication . The "Network data "will serve to later authorize that specific transaction. The credit card owner or other authorized user can then only make that specific transaction with the credit card. Because transaction pre- authorized, the vendor does need to see or transmit a PIC.

Verification information is provided with respect to a transaction between an initiating party and a verification-seeking party, the verification information being given by a third , verifying party, based on confidential information in the possession of the initiating party. In verification the process will seeks card number and if the card number is correct the relevant process will be executed. If the number is wrong, mail will be sent to the user saying the card no has been block and he can't do the further transaction.

Module Input and output:

New card:

Give input -Request from the user for the card.
Expected Output -Assigning an account to requested user.

Login :

Given input- give the security information by answering security questions.
Expected output- Updating of account with the security details.


Given input-Checks with user's stored details like security answers or hidden details expected output- if the verification is success user and perform transaction , else blocks the card.

Hardware Requirements

¢ SYSTEM : Pentium IV 2.4 GHz
¢ MONITOR : 15 VGA colour
¢ MOUSE : Logitech.
¢ RAM : 256 MB
¢ KEYBOARD : 110 keys enhanced.

Software Requirements
¢ Operating system :- Windows XP Professional
¢ Front End :- Microsoft Visual Studio .Net 2003
¢ Coding Language :- Visual C# .Net
¢ Webtechnology
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04-04-2010, 03:36 PM

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16-10-2010, 10:27 AM

This article is presented by:Kayo Narukawa
Tadashi Kadowaki
Transmembrane regions prediction for G-protein-coupled
receptors by hidden Markov model

G-protein-coupled receptors (GPCRs) belong to one of the largest superfamilies of membrane proteins and are the target of majority of drugs on the market. GPCRs are divided into six major classes. These receptors have a central core domain constituted of seven TM (transmembrane) helices connected by three intracellular and three extracellular loops. The prediction of TM regions gives basic information for studying functions and structures of GPCRs in common. A variety of tools are available to predict the topology of TM proteins. However, for GPCRs research, we need a more accurate tool of TM regions prediction for GPCRs. In this study, we proposed a novel method to predict of TM regions for GPCRs with more accuracy.

Data sets

We collected protein sequences of GPCRs from GPCRDB [1]. The data consists of all the spices available in GPCRDB. The sequences of 3 classes were used for building TM topological models: class A (4083 sequences), class B (187 sequences) and class C (133 sequences). Training data for building a predictive TM model needs TM region information, which can be predicted by TMHMM2.0 [2]. We sampled the training data from the sequences, which were predicted as seven TM proteins, and used the rest sequences as test data. We used 1,000 sequences from SWISS-PROT database except GPCRs as non-GPCR data for training data and test data, respectively.

Building a model and Prediction
The model building process is the following: The regions vary between genes, thus we refined the regions using multiple alignment by ClustalW 1.83. The tuned alignment of TM regions was processed by ‘hmmbuild’ program in the HMMER 2.3.2 package [3]. We combined seven TM hidden Markov models (HMMs) into a HMM with intra- and extra-cellular loop models. A loop model can be expressed as insertion state in HMM with a transition probability parameter obtained from average loop length. Once the model is built, we can predict TM topology of GPCRs as follows: TM regions and their scores of GPCRs were obtained by ‘hmmalign’ and ‘hmmsearch’ programs. We identified the seven regions of training data, test data, and non-GPCR data. The thresholds of the score for each region were calculated from score distributions of training data and non-GPCR data. When all scores of seven regions for test data were higher than each threshold, we classified the sequence as GPCR and identified these regions as TM regions.

For more information about this article,please follow the link:
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11-01-2012, 09:09 PM

Guyz please help me out for d prject - Fraud credit card detection using hidden markov model. i want the documentation. please help me out and please mail me d doc to
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