One of the ways in which attackers try to steal sensitive information from corporations is by sending spearphishing emails. This type of emails typically appear to be sent by one of the victim's coworkers, but have instead been crafted by an attacker. A particularly insidious type of spearphishing emails are the ones that do not only claim to come from a trusted party, but were actually sent from that party's legitimate email account that was compromised in the first place. In this paper, we propose a radical change of focus in the techniques used for detecting such malicious emails: instead of looking for particular features that are indicative of attack emails, we look for possible indicators of impersonation of the legitimate owners. We present IdentityMailer, a system that validates the authorship of emails by learning the typical email-sending behavior of users over time, and comparing any subsequent email sent from their accounts against this model. Our experiments on real world e-mail datasets demonstrate that our system can effectively block advanced email attacks sent from genuine email accounts, which traditional protection systems are unable to detect. Moreover, we show that it is resilient to an attacker willing to evade the system. To the best of our knowledge, IdentityMailer is the first system able to identify spearphishing emails that are sent from within an organization, by a skilled attacker having access to a compromised email account.