Design of a GSM-Based Skimming Reporting System for Automated Teller Machines

In recent years, there has been the unpleasant advent of a new type of credit card fraud called Automated Teller Machine (ATM) skimming. This type of fraud poses a substantial threat to the banking sector because its modus operandi is quite subtler than other known types of ATM fraud. It consists of a criminal implanting a disguised dummy card reader very similar to the ATM’s original card reader at the ATM. This is done to intercept the ATM card data of any unsuspecting customer who tries to withdraw cash. This paper seeks to design a system which will be able to detect and report such devices before they cause harm. The objective of this research was achieved by designing a skimmer incorporating the use of a metal detector for detecting new electronic components within the ATMs card slot region, an ultrasonic sensor for detecting unfamiliar skimmer overlays and the processing power of a microcontroller to coordinate theses sensors which monitor the status of the ATM terminal’s original card reader and send a Short Message Service (SMS) text message whenever the system detects that a skimmer has been attached to the ATM terminal. This concept of skimming detection was designed, tested and simulated under several operating conditions in Proteus 8.0 simulation software to prove the detection method’s efficacy. The simulation results showed that the proposed system provided a decent theoretical skimmer detection technique. However, other factors such as metal detector oscillator instability and the difficulty in accurately modelling the composition of ATM skimmers served as this design’s major drawbacks.Keywords : ATM, Skimming, GSM, Microcontrolle

Design of a GSM-Based Skimming Reporting System for Automated Teller Machines

In recent years, there has been the unpleasant advent of a new type of credit card fraud called Automated Teller Machine (ATM) skimming. This type of fraud poses a substantial threat to the banking sector because its modus operandi is quite subtler than other known types of ATM fraud. It consists of a criminal implanting a disguised dummy card reader very similar to the ATMs original card reader at the ATM. This is done to intercept the ATM card data of any unsuspecting customer who tries to withdraw cash. This paper seeks to design a system which will be able to detect and report such devices before they cause harm. The objective of this research was achieved by designing a skimmer incorporating the use of a metal detector for detecting new electronic components within the ATMs card slot region, an ultrasonic sensor for detecting unfamiliar skimmer overlays and the processing power of a microcontroller to coordinate theses sensors which monitor the status of the ATM terminals original card reader and send a Short Message Service (SMS) text message whenever the system detects that a skimmer has been attached to the ATM terminal. This concept of skimming detection was designed, tested and simulated under several operating conditions in Proteus 8.0 simulation software to prove the detection methods efficacy. The simulation results showed that the proposed system provided a decent theoretical skimmer detection technique. However, other factors such as metal detector oscillator instability and the difficulty in accurately modelling the composition of ATM skimmers served as this designs major drawbacks. Keywords : ATM, Skimming, GSM, Microcontrolle

Bankacılık işlemlerinde konum destekli sahtekarlık önleme sistemi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Sahtekârlık (fraud) işlemlerinin tespiti ulusal ve uluslararası ekonomiler için oldukça önemli bir görev haline gelmiştir. Bankalar ve diğer finansal kuruluşların gerçekleştirdikleri işlemlerin güvenilirliğini sağlaması başta ülke ekonomisi olmak üzere, finansal kuruluşun da itibar ve kârlılığını etkileyen temel faktörlerden birisidir. Sahtekârlık işlemlerinin tespit edilebilmesi ve önlenmesi amacıyla kamu ve özel finans kuruluşlarında bu kontrolleri yapmaktan sorumlu birimler oluşturulmuştur. Ancak sahtekârlık işlemlerini gerçekleştirmeye çalışan kişilerin, yakalanmamak amacıyla sürekli yöntem değiştirmeleri, bu tip işlemlerin tespit edilmesini zorlaştırmaktadır. Bu işlemlerin tespiti, işlem hacimlerinin yoğunluğu da dikkate alındığında teknoloji desteğini zorunlu kılmaktadır. Sahtekârlık işlemlerinin tespiti için geliştirilmiş uygulamalar içerisinde özellikle kural tabanlı sistemlerin yaygınlığı dikkate değerdir. Bu sistemler basit ve bileşik kurallar kullanan, doğrulanmış sahtekârlık veritabanları ve diğer önemli veri setlerinde karşılaştırma yapan ileri teknoloji veri eşleme sistemleri olabileceği gibi şüpheli davranışları tespit edebilen ve bu bilgiyi doğru kanala yönlendiren veritabanları gibi basit sistemler de olabilmektedir. Bununla birlikte, sahtekârlık işlemlerinin tespitinde işlem konumlarının (lokasyonlarının) dikkate alınması üzerine geliştirilmiş bir modele rastlanmamıştır. Bu tez çalışmasında hedeflenen bankacılık ürün ve hizmetlerine yönelik sahtekârlık işlemlerinin tespiti ve önlenmesi için finansal işlemlerin konum bilgisinin kullanılması ile daha iyi sonuçlar elde edilip edilemeyeceğinin incelenmesidir. Çalışma kapsamında coğrafi bilgi sistemlerinin yardımıyla ve veri madenciliği modelleri kullanılarak, konum ve zaman bilgisinin dahil edildiği senaryolar keşfedilmiştir. Anahtar kelimeler: Sahtekârlık (fraud) işlemleri, veri madenciliği, coğrafi bilgi sistemleri, lokasyon zekâsıFraud detection procedures for national and international economies have become quite important tasks. Ensuring the security of transactions carried out by banks and other financial institutions is one of the major factors affecting the reputation and profitability of such organizations. Public and private financial institutions establish organizational bodies responsible for carrying out controls for detecting and preventing fraudulent transactions. However, since people who perform fradulent transactions change their methods constantly in order not to get caught up, it gets more difficult to identify and detect this type of transactions. Detecting this type of transactions makes the support of technology compulsory, considering high volume and intensity of transactions. Among the applications that has been developed for the detection of fraudulent transactions, the prevalence of the rule-based systems are particularly noteworthy. As these systems may use of simple and compound rules, advanced data mapping technologies that make comparison in validated fraud databases, and other important databases mapping systems, they may be simple database systems that can detect suspicious behavior and directs this information to the right. However, we have not come across any model that takes into account of transaction location. The aim of this thesis study is to study the worth of location information of financial transactions for detecting the fraudulent transactions. The scope of work is to discover scenarios to detect fraudulent transactions by the support of geographic information systems with location, and time information and the help of models built by using data mining. Keywords: Fraudulent Transactions, Data Mining, Geographical Information Systems, Location Intelligenc

IMAGE UNDERSTANDING OF MOLAR PREGNANCY BASED ON ANOMALIES DETECTION

Cancer occurs when normal cells grow and multiply without normal control. As the cells multiply, they form an area of abnormal cells, known as a tumour. Many tumours exhibit abnormal chromosomal segregation at cell division. These anomalies play an important role in detecting molar pregnancy cancer. Molar pregnancy, also known as hydatidiform mole, can be categorised into partial (PHM) and complete (CHM) mole, persistent gestational trophoblastic and choriocarcinoma. Hydatidiform moles are most commonly found in women under the age of 17 or over the age of 35. Hydatidiform moles can be detected by morphological and histopathological examination. Even experienced pathologists cannot easily classify between complete and partial hydatidiform moles. However, the distinction between complete and partial hydatidiform moles is important in order to recommend the appropriate treatment method. Therefore, research into molar pregnancy image analysis and understanding is critical. The hypothesis of this research project is that an anomaly detection approach to analyse molar pregnancy images can improve image analysis and classification of normal PHM and CHM villi. The primary aim of this research project is to develop a novel method, based on anomaly detection, to identify and classify anomalous villi in molar pregnancy stained images. The novel method is developed to simulate expert pathologists’ approach in diagnosis of anomalous villi. The knowledge and heuristics elicited from two expert pathologists are combined with the morphological domain knowledge of molar pregnancy, to develop a heuristic multi-neural network architecture designed to classify the villi into their appropriated anomalous types. This study confirmed that a single feature cannot give enough discriminative power for villi classification. Whereas expert pathologists consider the size and shape before textural features, this thesis demonstrated that the textural feature has a higher discriminative power than size and shape. The first heuristic-based multi-neural network, which was based on 15 elicited features, achieved an improved average accuracy of 81.2%, compared to the traditional multi-layer perceptron (80.5%); however, the recall of CHM villi class was still low (64.3%). Two further textural features, which were elicited and added to the second heuristic-based multi-neural network, have improved the average accuracy from 81.2% to 86.1% and the recall of CHM villi class from 64.3% to 73.5%. The precision of the multi-neural network II has also increased from 82.7% to 89.5% for normal villi class, from 81.3% to 84.7% for PHM villi class and from 80.8% to 86% for CHM villi class. To support pathologists to visualise the results of the segmentation, a software tool, Hydatidiform Mole Analysis Tool (HYMAT), was developed compiling the morphological and pathological data for each villus analysis