Real-Time Detection of Malware Downloads via Large-Scale URL-≫File-≫Machine Graph Mining
Title | Real-Time Detection of Malware Downloads via Large-Scale URL-≫File-≫Machine Graph Mining |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Rahbarinia, Babak, Balduzzi, Marco, Perdisci, Roberto |
Conference Name | Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security |
Publisher | ACM |
Conference Location | New York, NY, USA |
ISBN Number | 978-1-4503-4233-9 |
Keywords | composability, cyber physical systems, False Data Detection, graph mining, Human Behavior, machine learning, malware detection, pubcrawl, Resiliency |
Abstract | In this paper we propose Mastino, a novel defense system to detect malware download events. A download event is a 3-tuple that identifies the action of downloading a file from a URL that was triggered by a client (machine). Mastino utilizes global situation awareness and continuously monitors various network- and system-level events of the clients' machines across the Internet and provides real time classification of both files and URLs to the clients upon submission of a new, unknown file or URL to the system. To enable detection of the download events, Mastino builds a large download graph that captures the subtle relationships among the entities of download events, i.e. files, URLs, and machines. We implemented a prototype version of Mastino and evaluated it in a large-scale real-world deployment. Our experimental evaluation shows that Mastino can accurately classify malware download events with an average of 95.5% true positive (TP), while incurring less than 0.5% false positives (FP). In addition, we show the Mastino can classify a new download event as either benign or malware in just a fraction of a second, and is therefore suitable as a real time defense system. |
URL | http://doi.acm.org/10.1145/2897845.2897918 |
DOI | 10.1145/2897845.2897918 |
Citation Key | rahbarinia_real-time_2016 |