Azad, Muhammad Ajmal, Arshad, Junaid ORCID: https://orcid.org/0000-0003-0424-9498 and Farhan, Riaz (2018) SOCIO-LENS: spotting unsolicited caller through network analysis. In: Guide to Vulnerability Analysis for Computer Networks and Systems. Springer, Cham, Switzerland. ISBN 9783319926230

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Abstract

Spam and unwanted content has been a significant challenge for the Internet technologies (Email, social networks, blogs, search engines etc.) for decades. However, in recent years, the advent of modern and cheap telephony technologies and larger user base (more than 6 billion users) has attracted scammers to use telephony for distributing unwanted content via instant messaging and calls. Detection of unwanted caller in the telephony has become challenging because the content is available only after the call has already been answered by the recipients thus is too late to block the unwanted caller after the call has already been established. One of the interesting possibilities is to develop a telephony blacklist database using social behavior of users towards their friends and family circle by modeling call metadata as a weighted network graph. In this chapter, we model user’s behavior
as a weighted call graph network and identify malicious users by analyzing different network features of users. To this extent, we have identified a set of features that
help represent malicious and non-malicious behavior of users in a network. We have conducted rigorous experimentation of the proposed system via its implementation with dataset collected by small scale telecommunication operator. We present the outcomes of our evaluation highlighting the efficacy of the system’s performance and identifying possible directions for future work.

Item Type:	Book Section
Subjects:	Computing > Information security > Cyber security Computing
Depositing User:	Junaid Arshad
Date Deposited:	17 Apr 2018 10:46
Last Modified:	28 Aug 2021 07:25
URI:	https://repository.uwl.ac.uk/id/eprint/4839

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