Hard Topic Theme: Big Data for Security
Background
Since 2013, ACSAC has had a hard topic theme that focuses the conference on tackling a hard, cutting-edge, cyber security problem requiring cooperation from government, industry and academia. This year, ACSAC especially encourages contributions in the area of Big Data for Security.
ACSAC welcomes contributions on the Big Data for Security topic of not only technical papers, but also of panels, workshops, posters, and works-in-progress, as well as other "out-of-the-box" ideas. ACSAC also welcomes specific suggestions for invited speakers and presenters on this topic. See the Call for Submissions for specific instructions for each submission type.
During the conference, a number of orchestrated sessions will include government and industry speakers to frame the hard topic theme, industry and academic speakers to discuss issues and challenges related to the topic, and academic speakers to introduce promising security research. The primary goal of these special sessions will be to foster discussion that can expose opportunities for further collaboration and highlight promising research directions.
Hard Topic Description
The security industry is rapidly amassing an incredible amount of information: billions of malicious samples and emails attachments, Internet-wide scans that can be repeated multiple times a day, collections of DNS queries and HTTP requests, and an unprecedented amount of open source software to mine for vulnerabilities are only the tip of the iceberg. This information allows researchers to observe phenomena that do not manifest on a small scale and can play an important role in many other aspects of security, from analytics and intelligence support, to training automated classification and reasoning techniques. However, using large datasets in security also faces many technical and scientific challenges. Therefore, we need to design new data-driven techniques and we need to rethink our existing solutions to take advantage of this vast amount of information to improve security.
Submissions in this area include (but are not limited to) the use of machine learning and data mining techniques to explore and extract information from large datasets of security-relevant information, or to better cluster and classify their data. They also include the presentation of new large-scale data collection and analysis techniques, and the discussion of longitudinal studies that bring new insights into attackers' behavior, or help researchers to better understand the evolution of a given threat.