This work is concerned with GSM subscription fraud detection system using some network techniques. Fraud is a hitch around the globe with huge loss of income. Fraud has an effect on the credibility and performance of ...This work is concerned with GSM subscription fraud detection system using some network techniques. Fraud is a hitch around the globe with huge loss of income. Fraud has an effect on the credibility and performance of telecommunication companies. The most difficult problem that faces the industry is the fact that fraud is dynamic, which means that whenever fraudsters feel that they will be detected, they devise other ways to circumvent security measures. In such cases, the perpetrators’ intention is to completely avoid or at least reduce the charges for using the services. Subscription fraud is one of the major types of telecommunication fraud in which a customer obtains an account without intention to pay the bill. Thus at the level of a phone number, all transactions from the number will be fraudulent. In such cases abnormal usage occurs throughout the active period of the account;which is usually used for call selling or intensive self usage. This provides a means for illegal high profit business for fraudsters requiring minimal investment and relatively low risk of getting caught. A system to prevent subscription fraud in GSM telecommunications with high impact on long distance carriers is proposed to detect fraud. The system employs adaptive flexible techniques using advanced data analysis like Artificial Neural Networks (ANN). This study aims at developing a fraud detection model occurrence in GSM Network. The paper also gives analysis of the fraud detection Systems, fraud detection and prevention, fraud prevention methods etc. Fraud affects us all and is of particular concern to those who manage large government and business organisations where the potential losses are greatest. The operation of a mobile network is complex, and fraudsters invest a lot of energy to find and exploit every weakness of the system. A typical example would be subscription fraud, where a fraudster acquires a subscription to the mobile network under a false identity;and start reselling the use of his phone to unscrupulous customers (typically for international calls to distant foreign countries) at rate less than the regular tariff.展开更多
Data Mining, also known as knowledge discovery in data (KDC), is the process of uncovering patterns and other valuable information from large data sets. According to https://www.geeksforgeeks.org/data-mining/, it can ...Data Mining, also known as knowledge discovery in data (KDC), is the process of uncovering patterns and other valuable information from large data sets. According to https://www.geeksforgeeks.org/data-mining/, it can be referred to as knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. With advance research in health sector, there is multitude of Data available in healthcare sector. The general problem then becomes how to use the existing information in a more useful targeted way. Data Mining therefore is the best available technique. The objective of this paper is to review and analyse some of the different Data Mining Techniques such as Application, Classification, Clustering, Regression, etc. applied in the Domain of Healthcare.展开更多
文摘This work is concerned with GSM subscription fraud detection system using some network techniques. Fraud is a hitch around the globe with huge loss of income. Fraud has an effect on the credibility and performance of telecommunication companies. The most difficult problem that faces the industry is the fact that fraud is dynamic, which means that whenever fraudsters feel that they will be detected, they devise other ways to circumvent security measures. In such cases, the perpetrators’ intention is to completely avoid or at least reduce the charges for using the services. Subscription fraud is one of the major types of telecommunication fraud in which a customer obtains an account without intention to pay the bill. Thus at the level of a phone number, all transactions from the number will be fraudulent. In such cases abnormal usage occurs throughout the active period of the account;which is usually used for call selling or intensive self usage. This provides a means for illegal high profit business for fraudsters requiring minimal investment and relatively low risk of getting caught. A system to prevent subscription fraud in GSM telecommunications with high impact on long distance carriers is proposed to detect fraud. The system employs adaptive flexible techniques using advanced data analysis like Artificial Neural Networks (ANN). This study aims at developing a fraud detection model occurrence in GSM Network. The paper also gives analysis of the fraud detection Systems, fraud detection and prevention, fraud prevention methods etc. Fraud affects us all and is of particular concern to those who manage large government and business organisations where the potential losses are greatest. The operation of a mobile network is complex, and fraudsters invest a lot of energy to find and exploit every weakness of the system. A typical example would be subscription fraud, where a fraudster acquires a subscription to the mobile network under a false identity;and start reselling the use of his phone to unscrupulous customers (typically for international calls to distant foreign countries) at rate less than the regular tariff.
文摘Data Mining, also known as knowledge discovery in data (KDC), is the process of uncovering patterns and other valuable information from large data sets. According to https://www.geeksforgeeks.org/data-mining/, it can be referred to as knowledge mining from data, knowledge extraction, data/pattern analysis, data archaeology, and data dredging. With advance research in health sector, there is multitude of Data available in healthcare sector. The general problem then becomes how to use the existing information in a more useful targeted way. Data Mining therefore is the best available technique. The objective of this paper is to review and analyse some of the different Data Mining Techniques such as Application, Classification, Clustering, Regression, etc. applied in the Domain of Healthcare.
