Conference Proceedings and Papers

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Conference proceeding is a collection of academic papers published in the context of an academic conference or workshop. Conference proceedings typically contain the contributions made by researchers at the conference. They are the written record of the work that is presented to fellow researchers.

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    Survey of Mobile Malware Analysis, Detection Techniques and Tool
    (2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, 2018-11) Gyamfi, N.K.; Owusu, E.
    The rapid increase in the use of smartphones, has contributed to the increase in mobile attackers. In most situations deceitful applications are infected with malicious contents to cause harm to both the hardware and the software. These malicious programs or malware are usually designed to disrupt or gather information from the device. By attempts to curtail these problems various techniques are proposed. This paper attempts to analyze the most popular and recent techniques and suggests which is better.
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    Bank Fraud Detection Using Support Vector Machine
    (2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, 2018-11) Gyamfi, N.K.; Abdulai, J.D.
    With the significant development of communications and computing, bank fraud is growing in its forms and amounts. In this paper, we analyze the various forms of fraud to which are exposed banks d data mining tools allowing its early detection data already accumulated in a bank. We use supervised learning methods Support Vector Machines with Spark (SVM-S) to build models representing normal and abnormal customer behavior and then use it to evaluate validity of new transactions. The results obtained from databases of credit card transactions show that these techniques are effective in the fight against banking fraud in big data. Experiment result from the study show that SVM-S have better prediction performance than Back Propagation Netw orks (BPN). Besides the average prediction, accuracy reaches a maximum when training the data ratio arrives at 0.8.