Increasingly, sophisticated techniques from machine learning, data mining, statistics and natural language processing are being applied to challenges in security and privacy fields. However, experts from these areas have no medium where they can meet and exchange ideas so that strong collaborations can emerge, and cross-fertilization of these areas can occur. Moreover, current courses and curricula in security do not sufficiently emphasize background in these areas and students in security and privacy are not emerging with deep knowledge of these topics.

Hence, we propose a workshop that will address the research and development efforts in which analytical techniques from machine learning, data mining, natural language processing and statistics are applied to solve security and privacy challenges (“security analytics”). Submissions of papers related to methodology, design, techniques and new directions for security and privacy that make significant use of machine learning, data mining, statistics or natural language processing are welcome. Furthermore, submissions on educational topics and systems in the field of security analytics are also highly encouraged.

Short Submission Guidelines:

Papers must be at most 10 pages in length total and use the ACM CODASPY template. All submissions must describe original research, not published nor currently under review for another workshop, conference, or journal. Submission implies the willingness of at least one author to attend the workshop and present the paper. A limited number of high-quality submissions may be selected for publication in a special issue of a journal. All papers must be submitted electronically via the Easychair system.

For more information contact: Stephen Huang at s_huang@cs.uh.edu or Rakesh Verma at rverma@uh.edu

IMPORTANT DATES
Paper Submission Deadline: December 23, 2018 (11.59pm AOE)

Acceptance Notification: January 10, 2019

Camera Ready Submission Deadline: January 28, 2019

The proceedings of IWSPA 2019 will be available in the ACM Digital Library.