The popularity of Social Networks during the last years has caught the attention of cybercriminals for the distribution of Spam and malicious contents. In order to do that, they create fake profiles to send spam messages to legitimate users, leading to fraud or malware campaigns. Sometimes cybercriminals use stolen accounts of legitimate users to send these malicious messages. The goal of this work is to use information available for any user to detect malicious profiles in Twitter, the second most popular Social Network in the world. Also we explore the possibility of distinguishing into different kind of malicious profiles: spammers and hacked accounts. We show how it is possible to obtain a set of features derived from the public information available in order to correctly classify malicious and clean profiles with a success rate over 90%. We show, also, how the same method could be used to detect hacked profiles with similar results. Based on these results, we propose a global system that could use both local and global information for improved results in the detection of malicious profiles.