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Detecting SIMBox Fraud Using CDR Files And Neo4j Technology
... Fig. 1. Graph Schema for Workforce Trend Analysis using Neo4j [27] The same database technology was used for detecting fraud from voice traffic [29]. The call detail records were analyzed for any suspicious behavior through the Cypherstructured queries coded on Neo4j. ...
Massive data are generated and processed every day. Insights from this big data make significant difference in one's way of living. But insight is one thing and intelligence is another. To draw out an intelligence, one must be able to discern patterns and deeper relationships among data. Intelligence on the road comes in various forms and applies in different segments of the public transportation. This study focused on geospatial intelligence that will benefit the passengers in their commuting experience with the intention of restoring their confidence in using the mass transit. A bi-directional unweighted path cost search and geodesic distance priority algorithms were structured using graph query language (GQL) to query the framework developed using TigerGraph database for: public transit routes from a given source and destination locations, and facilities within the specified distance from a location, respectively. Graph database was selected because it naturally represents geospatial data, and it focuses on relationships. To visualize the intelligence obtained, output was overlaid into maps and exhibited through Jupyter notebook. The goal of this study is to empower the public transport sector with geospatial intelligence by introducing and proving the viability of developing GQL-structured algorithms in answering its persistent priority areas.
The high price of incoming international calls is a common method of subsidizing telephony infrastructure in the developing world. Accordingly, international tele- phone system interconnects are regulated to ensure call quality and accurate billing. High call tariffs create a strong incentive to evade such interconnects and deliver costly international calls illicitly. Specifically, adversaries use VoIP-GSM gateways informally known as “simboxes” to receive incoming calls over wired data connections and deliver them into a cellular voice network through a local call that appears to originate from a customer’s phone. In this paper, we analyze and compare known methods of fraud detection (sim-box), explaining the advantages and defects for each method and proposed a new method. System relies on analyze CDR files using data mining technology (Neo4j), and then use known method TCG (test call generation) to increase efficiency and to be
more sure to results.
Telecom companies are severely damaged by bypass fraud or SIM boxing. However, there is a shortage of published research to tackle this problem. The traditional method of Test Call Generating is easily overcome by fraudsters and the need for more sophisticated ways is inevitable. In this work, we are developing intelligent algorithms that mine a huge amount of mobile operator's data and detect the SIMs that are used to bypass international calls. This method will make it hard for fraudsters to generate revenue and hinder their work. Also by reducing fraudulent activities, quality of service can be increased as well as customer satisfaction. Our technique has been evaluated and tested on real world mobile operator data, and proved to be very efficient.
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