In loco identity fraud detection model using statistical analysis for social networking sites: a case study with facebook

Shalini Hanok, Shankaraiah
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Abstract

Rapid advancement in internet has made many Social Networking Sites (SNS) popular among a huge population, as various SNS accounts are interlinked with each other, spread of stored susceptible information of an individual is increasing. That has led to various security and privacy issues; one of them is impersonation or identity fraud. Identity fraud is the outcome of illegitimate or secret use of account owner’s identity to invade his/her account to track personal information. There are possibilities that known persons like parents, spouse, close friends, siblings who are interested in knowing what is going on in the account owner’s online life may check their personal SNS accounts. Hence an individual’s private SNS accounts can be invaded by an illegitimate user secretly without the knowledge of the account owner’s which results in compromise of private information. Thus, this paper proposes an in loco identity fraud detection strategy that employs a statistical analysis approach to constantly authenticate the authorized user, which outperforms the previously known technique. This strategy may be used to prevent stalkers from penetrating a person's SNS account in real time. The accuracy attained in this research is greater than 90% after 1 minute and greater than 95% after 5 minutes of observation.
基于统计分析的社交网站身份欺诈检测模型:以facebook为例
互联网的飞速发展使得许多社交网站在庞大的人群中流行起来,由于各种社交网站账户之间的相互联系,存储的个人敏感信息的传播越来越大。这导致了各种安全和隐私问题;其中之一是冒充或身份欺诈。身份欺诈是指非法或秘密使用帐户所有者的身份侵入其帐户以跟踪个人信息的结果。父母、配偶、亲密的朋友、兄弟姐妹等熟悉的人有可能对账户所有者的网络生活感兴趣,可能会查看他们的个人SNS账户。因此,一个人的私人SNS账户可能会被非法用户在不知情的情况下秘密入侵,从而导致私人信息的泄露。因此,本文提出了一种采用统计分析方法对授权用户进行持续身份验证的在线身份欺诈检测策略,该策略优于现有技术。这种策略可以用来防止跟踪者实时侵入一个人的SNS账户。本研究在观察1分钟后获得的准确度大于90%,在观察5分钟后获得的准确度大于95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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