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The number of users of the world wide web is constantly increasing. However, this also increases the risks. There is the possibility that other users illegally gain access to a users' account of social networks, web shops or other web services. Previous work use graph-based methods to identify hijacked or compromised accounts. Most often posts are used in social networks to detect fraudulences. However, not every compromised account is used to spread propaganda information or phishing attacks. Therefore, we restrict ourselves to the clickstreams from the accounts. In order to identify compromised accounts by means of clickstreams, we will also consider a temporal aspect, since the preferences of a user change over time. We choose a hybrid approach consisting of methods from subsymbolic and symbolic AI to detect fraudulences in clickstreams. We will also take into account the experience of domain experts. Our approach can also be used to identify not only compromised accounts but also shared accounts on instance streaming sites.

Compromised Account Detection Based on Clickstream Data