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Data-Driven Approach for Automatic Telephony Threat Analysis and Campaign Detection

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Data-Driven Approach for Automatic Telephony Threat Analysis and Campaign Detection

Bordjiba, Houssem Eddine (2017) Data-Driven Approach for Automatic Telephony Threat Analysis and Campaign Detection. Masters thesis, Concordia University.

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Abstract

The growth of the telephone network and the availability of Voice over Internet Protocol (VoIP) have both contributed to the availability of a flexible and easy to use artifact for users, but also to a significant increase in cyber-criminal activity. These criminals use emergent technologies to conduct illegal and suspicious activities. For instance, they use VoIP’s flexibility to abuse and scam victims.
A lot of interest has been expressed into the analysis and assessment of telephony cyber-threats. A better understanding of these types of abuse is required in order to detect, mitigate, and attribute these attacks. The purpose of this research work is to generate relevant and timely telephony abuse intelligence that can support the mitigation and/or the investigation of such activities. To achieve this objective, we present, in this thesis, the design and implementation of a Telephony Abuse Intelligence Framework (TAINT) that automatically aggregates, analyzes and reports on telephony abuse activities.
Such a framework monitors and analyzes, in near-real-time, crowd-sourced telephony complaints data from various sources. We deploy our framework on a large dataset of telephony complaints, spanning over seven years, to provide in-depth insights and intelligence about merging telephony threats. The framework presented in this thesis is of paramount importance when it comes to the mitigation, the prevention and the attribution of telephony abuse incidents. We analyze the data and report on the complaint distribution, the used numbers and the spoofed callers’ identifiers. In addition, we identify and geo-locate the sources of the phone calls, and further investigate the underlying telephony threats. Moreover, we quantify the similarity between reported phone numbers to unveil potential groups that are behind specific telephony abuse activities that are actually launched as telephony abuse campaigns.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering
Item Type:Thesis (Masters)
Authors:Bordjiba, Houssem Eddine
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Information Systems Security
Date:18 September 2017
Thesis Supervisor(s):Debbabi, Mourad
ID Code:983071
Deposited By: HOUSSEM EDDINE BORDJIBA
Deposited On:10 Nov 2017 15:52
Last Modified:15 Apr 2018 00:00
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