IEEE International Conference on Data Mining (ICDM) Deep Learning for Cyber Threat Intelligence (DL-CTI) WORKSHOP

Description
The regularity of devastating cyber-attacks has made cybersecurity a grand societal challenge. To combat this societal issue, many organizations have aimed to develop timely, relevant, and actionable intelligence about emerging threats and key threat actors to enable effective cybersecurity decisions. This process, also referred to as Cyber Threat Intelligence (CTI) has quickly emerged as a key aspect of cybersecurity. At its core, CTI is a data-driven process that requires relies on the systematic and large-scale analysis of log files, malware binaries, events, Open Source Intelligence (OSINT), and other rapidly evolving cybersecurity data sources. While numerous traditional text mining, web mining, and data mining approaches have seen remarkable developments in the past half-decade, these approaches rely on manual feature engineering approaches. In a rapidly evolving domain, such efforts are highly reactive, labor intensive, and can result in missing critical insights. Deep learning holds significant promise in automatically analyzing large quantities of structured, unstructured, and semi-structured data identify patterns, emerging threats, and key hackers without ad-hoc feature engineering efforts. As a result, deep learning-based CTI systems are more resilient, detect threats previously missed by conventional analyses, and are dynamic to the ever-evolving threat landscape. Despite its successes, Deep Learning for CTI (DL-CTI) remains a nascent, yet promising, research area.

Topics of Interest

This workshop seeks to foster a budding community of cybersecurity data scientists by recruiting high quality papers and holding discussions related to emerging applications, techniques, and methodologies related to deep learning for CTI applications.

Methodological topics of interest include, but are not limited to:
• Graph convolution networks and graph attention networks
• Interpretable deep learning
• Real-time and/or streaming deep learning
• Multi-view deep learning paradigms
• Deep adversarial learning (e.g., generative adversarial networks)
• Deep transfer learning
• Deep Bayesian learning
• Deep reinforcement learning

Application areas of interest include, but are not limited to:
• Malware evasion and detection
• IP reputation services
• Event correlation and anomaly detection
• Internet of Things (IoT) analysis (e.g., fingerprinting, network telescopes, etc.)
• Threat modeling (e.g., mapping exploits to MITRE ATT&CK)
• Security data fusion (e.g., event correlation) across multiple data sources
• Cybersecurity information sharing and automation
• Smart and large-scale vulnerability assessment and management systems
• Security Intelligence Augmentation (e.g., human-in-the-loop systems)
• Dark Web Analytics for CTI applications

Each manuscript must clearly articulate their data (e.g., key metadata, statistical properties, etc.), analytical procedures (e.g., representations, algorithm details, etc.), and evaluation set up and results (e.g., performance metrics, statistical tests, case studies, etc.). Providing these details will help reviewers better assess the novelty, technical quality, and potential impact. Making data, code, and processes publicly available to facilitate scientific reproducibility is not required. However, it is strongly encouraged, as it can help facilitate a culture of data/code sharing in this quickly developing discipline. Given the scope of this workshop, accepted articles are expected to clearly articulate the how and why their proposed approaches fall into the category of CTI.
Submission Guidelines

Authors are invited to submit original papers, which have not been published elsewhere and are not currently under consideration for another journal, conference or workshop.
Paper submissions should be limited to a maximum of ten (10) pages, in the IEEE 2-column format (https://www.ieee.org/conferences/publishing/templates.html), including the bibliography and any possible appendices. Submissions longer than 10 pages will be rejected without review. All submissions will be triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity. The following sections give further information for authors.

Triple blind submission guidelines
Since 2011, ICDM has imposed a triple blind submission and review policy for all submissions. Authors must hence not use identifying information in the text of the paper and bibliographies must be referenced to preserve anonymity. Any papers available on the Web (including Arxiv) no longer qualify for ICDM submissions, as their author information is already public.
What is triple blind reviewing? The traditional blind paper submission hides the referee names from the authors, and the double-blind paper submission also hides the author names from the referees. The triple-blind reviewing further hides the referee names among referees during paper discussions before their acceptance decisions. The names of authors and referees remain known only to the PC Co-Chairs, and the author names are disclosed only after the ranking and acceptance of submissions are finalized. It is imperative that all authors of ICDM submissions conceal their identity and affiliation information in their paper submissions. It does not suffice to simply remove the author names and affiliations from the first page, but also in the content of each paper submission.

Key Dates:
All deadlines are at 11:59PM Pacific Daylight Time.
• Workshop paper submissions: August 24, 2020
• Workshop paper notification: September 17, 2020
• Camera-ready deadline and copyright forms: September 24, 2020
• Conference dates: November 17-20, 2020
• Contest prize presentations at ICDM 2020: November 19, 2020
Workshop Co-Chairs:
• Dr. Hsinchun Chen, University of Arizona
• Dr. Sagar Samtani, Indiana University
• Dr. Victor Benjamin, Arizona State University
• Dr. Weifeng Li, University of Georgia