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A research on usage pattern and analysis technique for communication fraud: SIM cloning and surfing
Xerandy H.a, Suksmono A.B.a, Nugraha T.b
a Telecommunication Information System, School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Indonesia
b Cellular Revenue Assurance Division, PT Indosat Tbk
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]One of the problems which occur in telecommunication is fraud. The terminology of fraud can be defined as the particular actions of deployment any telecommunication services without being charged intention. The loss imposed by this problem is not only suffered by operator, but also can be suffered by the subscriber it self. There are several common types of frauds found on telecommunication. The fraud types covered by this paper are only focused to two of them. They are 81M cloning and surfing. Particularly, they exhibit specific behavior characteristics, which can be observed from subscriber’s service usage data. Regarding to its special characteristic, there is a need to discover a technique to capture those fraudulent actions. Furthermore, this research can be as initial point to develop a fraud warning and detection system. This paper tries to explain the scenario of those frauds, tracking their specific usage pattern and then determine the most related information, which can be extracted and analyzed from subscriber usage data to capture those patterns. The possible technique offered by this paper is applying artificial neural network. For information, at the time this paper is written, the technique is still in intense research and investigation. © 2006 IEEE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Analysis techniques,Artificial Neural Network,Behavior characteristic,Detection system,Fraud,Initial point,Service usage,SIM cloning,Usage data,Usage patterns[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Fraud,Neural network,SIM cloning,Surfing,Technique,Telecommunication[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICOCI.2006.5276492[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]