Abstract: As Online Social Networks such as Facebook, Linkedln and Twitter are increasing becoming a part and parcel of one's daily lives, personal information is at stake. Easy access to personal information has made the attackers to steal information from influential users using various forms of attacks. Attackers take advantage of the user’s trustworthiness when using Online Social Networks. Hence, there is a need for the third party applications of various Online Social Networks sites to provide defence mechanisms against adversaries. Colluding attack is a way of creating fake profiles of friends of the target in the same OSN or others. Colluders impersonate their victims and send friend requests to the target with an intention to infiltrate their private circle to steal information. These types of attacks are difficult to detect in because multiple malicious users may have a similar purpose to gain information from their targeted user. In this regard, the work intends to overcome this type of attack by addressing the problem of identity clones across multiple Online Social Networks using machine learning.
Keywords: Identity Clone Attack, Machine Learning, Predictive FP growth, Online Social Networks
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DOI:
10.17148/IJARCCE.2019.8414
