Chaos Communication Congress

Exploring fraud in telephony networks (35x55)

Data di messa in onda: Dic 28, 2018

Telephone networks form the oldest large scale network that has grown to touch over 7 billion people. Telephony is now merging many complex technologies (PSTN, cellular and IP networks) and enabling numerous services that can be easily monetized. However, security challenges for telephony are often neither well understood, nor well addressed. As a result, telephone networks attract a lot of fraud. In this talk, we will systematically explore the fraud in telephone networks, focusing on voice telephony. We will present a taxonomy of fraud, and analyze two prevalent fraud schemes in more detail: looking into the ecosystem of International Revenue Share Fraud (IRSF), and discussing a new countermeasure to the well-known problem of voice spam. This talk aims to improve the understanding of the fraud ecosystem in telephony networks. We first provide a clear taxonomy that differentiates between the root causes, the vulnerabilities, the exploitation techniques, the fraud types and finally the way fraud benefits fraudsters. As concrete examples, we first look into International Revenue Share Fraud (IRSF), where phone calls to certain destinations are hijacked by fraudulent operators and diverted to the so-called ‘international premium rate services’. This fraud often involves multiple parties who collect and share the call revenue, and is usually combined with other techniques (such as voice scam, mobile malware, PBX hacking) to generate call traffic without payment. We will further explore the IRSF ecosystem by analyzing more than 1 million `premium rate' phone numbers that we collected from several online service providers over the past 3 years. In the second part, we will look into voice spam, a prevalent fraud in many countries. After giving an overview of various types of unwanted phone calls, we will focus on a recent countermeasure which involves connecting the phone spammer with a phone bot (“robocallee”) that mimic