Analysis and detection of SIMbox fraud in mobility networks

Murynets, Ilona; Zabarankin, Michael; Jover, Roger Piqueras; Panagia, Adam

doi:10.1109/infocom.2014.6848087

Cited by 26 publications

(34 citation statements)

References 13 publications

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“…In fact, telecommunications was one of the first industries that adopted machine learning technologies due to the huge amount of high-quality data they store [67]. Most of the academic work in this field focus on applying machine learning on certain behavior patterns extracted from CDRs to detect: subscription fraud [30], [32], account takeover [33], [34], [55], simbox fraud [28], [43] and voice spam [41]. As each fraud type exhibit different call patterns, features used in these previous work varies.…”

Section: Related Workmentioning

confidence: 99%

Understanding and Detecting International Revenue Share Fraud

Şahin

Francillon

2021

Proceedings 2021 Network and Distributed System Security Symposium

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Premium rate phone numbers are often abused by malicious parties (e.g., via various phone scams, mobile malware) as a way to obtain monetary benefit. This benefit comes from the 'revenue share' mechanism that enables the owner of the premium rate number to receive some part of the call revenue for each minute of the call traffic generated towards this number. This work focuses on International Revenue Share Fraud (IRSF), which abuses regular international phone numbers as the so-called International Premium Rate Numbers (IPRN). IRSF often involves multiple parties (e.g., a fraudulent telecom operator in collaboration with a premium rate service provider) who collect and share the call revenue, and is usually combined with other fraud schemes to generate call traffic without payment. Although this fraud scheme has been around for several years, it remains to be one of the most common phone fraud schemes, reportedly leading to billions of dollars of losses every year. In this paper we explore the IRSF ecosystem from multiple angles, via: (i) A telephony honeypot that observes IRSF attempts towards an unused phone number range (i.e., a phone number gray space), (ii) A dataset of more than 3 Million test IPRNs and more than 206K test call logs we collected from several online IPRN service providers during 4 years, and finally, (iii) A realworld call data set from a small European operator, involving 689K call records, that we analyze to find IRSF cases. By leveraging our observations from (ii), we propose several machine learning features that can be used in IRSF detection. We validate our approach on the dataset in (iii), achieving 98% accuracy with a 0.28% false positive rate in detecting the fraudulent calls.

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Section: Related Workmentioning

confidence: 99%

Understanding and Detecting International Revenue Share Fraud

Şahin

Francillon

2021

Proceedings 2021 Network and Distributed System Security Symposium

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“…(4) Classifier---Immune Algorithm [14]: (5) The introduction of the artificial immune system originated in Japan in December 1996, it was the first time that an international symposium based on the immune system was held in Japan. Inspired by the biological immune system, the concept of the "Artificial Immune System" (AIS) was first proposed.…”

Section: Principle Of Algorithmmentioning

confidence: 99%

Application of adaboost algorithm and immune algorithm in telecommunication fraud detection

Bing¹,

Li²,

Zhou³

2018

Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

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Fraud detection is one of the biggest challenges facing the telecommunication industry now and, in the future, the fight against fraud and anti-fraud has also reached a new stage. Finding a time-sensitive fraud detection method is an important way for operators to solve this problem. In this article, to solve the low accuracy of the general algorithm, an adaptive improvement algorithm is proposed. The common algorithms are combined and enhanced, which greatly improves the accuracy of the detection results. Here we take the artificial immune algorithm as an example. The main application is combining machine learning and immune algorithm to apply to telecommunication fraud detection. The combination of the two is more conducive to pushing research on telecommunication fraud to a new stage for the future telecommunication industry.

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“…For example, if a SIM card constantly makes large volumes of calls, makes calls to a large number of different destinations, or never moves out of a cell, then it is likely to be on a SIM box. Murynets et al [26] built a predictive model based on decision trees using more than 40 different features. They however do not report the final classification rules perhaps due to the same reasons mentioned above.…”

Section: B) Sim Box Detectionmentioning

confidence: 99%

“…Murynets et al . [26] built a predictive model based on decision trees using more than 40 different features. They however do not report the final classification rules perhaps due to the same reasons mentioned above.…”

Section: Use Casesmentioning

confidence: 99%

Use cases and challenges in telecom big data analytics

Chen¹

2016

SIP

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This paper examines the driving forces of big data analytics in the telecom domain and the benefits it offers. We provide example use cases of big data analytics and the associated challenges, with the hope to inspire new research ideas that can eventually benefit the practice of the telecommunication industry.Keywords: Big data, Analytics, Telecom, QoE I . I N T R O D U C T I O NThere has been much hype about big data analytics -a collection of technologies, including the Hadoop distributed file system, NoSQL databases, and machine learning tools. One study estimated that it can generate hundreds of billions of dollars of value across industries [1]. Another study reported that 75 of telecom operators surveyed would implement big data initiatives by 2017 [2]. Every operator is seeking new ways to increase operational efficiency and marketing effectiveness by leveraging big data technologies. But the question is how does big data analytics differ from prior arts such as data warehousing and statistical methods, in terms of the capabilities of uncovering insights from large volume of datasets? What are the compelling use cases in telecom, and what are the challenges?This paper provides a retrospect on how telecom operators have been striving, before the era of big data, to analyze large volumes of data in order to support their business and operation. We then examine the driving forces of big data analytics in the telecom domain and the benefits it offers. Finally we provide example use cases of big data analytics and the associated challenges, with the hope to inspire new research ideas that can eventually benefit the practice of the telecommunication industry. Corresponding author: C. Chen Email: cchen@iconectiv.com processing (OLAP), and data mining are adopted by telecom carriers to improve operation efficiency and user experience. To appreciate that, it helps to understand how a telecom network is managed. Figure 1 shows a simplified telecom management framework adopted from the TM Forum [3]. The framework contains three horizontal layers -resource, service, and customer, spanning across two vertical perspectives -infrastructure & product and operations. Examples of big data analytics use cases are shown in places according to their nature.The resource layer includes activities related to network build-out, planning, and monitoring. Operators constantly monitor performance of the networks (including user devices and network devices such as routers, switches, base stations, etc.) in order to assure smooth operation. Data collected at this layer includes alarms generated by the network devices and key performance indicators (KPIs) such as packet loss ratio, latency, traffic load, etc. The datasets support use cases for network planning, capacity management, and fault management.The service layer includes activities related to provisioning of user services (voice, data, and video). It also supports proactive monitoring and reactive diagnostics required by service-level agreements -a contractual agreement betw...

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Analysis and detection of SIMbox fraud in mobility networks

Cited by 26 publications

References 13 publications

Understanding and Detecting International Revenue Share Fraud

Understanding and Detecting International Revenue Share Fraud

Application of adaboost algorithm and immune algorithm in telecommunication fraud detection

Use cases and challenges in telecom big data analytics

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