The widespread adoption of the PDF format for document exchange has given rise to the use of PDF files as a prime vector for malware propagation. As vulnerabilities in the major PDF viewers keep surfacing, effective detection of malicious PDF documents remains an important issue. In this paper we present MDScan, a standalone malicious document scanner that combines static document analysis and dynamic code execution to detect previously unknown PDF threats. Our evaluation shows that MDScan can detect a broad range of malicious PDF documents, even when they have been extensively obfuscated.

While technology matures and becomes more productive, mobile devices can be affordable and, consequently, fully integrated in people's lives. After their unexpected bloom and acceptance, Online Social Networks are now sources of valuable information. The authors therefore use them for tasks varying from direct marketing to forensic analysis. The authors have already seen Social Network Forensics techniques focused on particular networks implementing methods that collect data from user accounts. During the forensic analysis it is common to aggregate information from different sources but, usually, this procedure causes correlation problems. Here, the authors present their method to correlate data gathered from various social networks in combination with smartphones creating a new form of social map of the user under investigation. In addition, the authors introduce a multi level graph that utilises the correlated information from the smartphone and the social networks and demonstrates in three dimensions the relevance of each contact with the suspect.

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