IEEE Transactions on Dependable and Secure Computing (TDSC) 
Call for Papers: 
Special Issue on Explainable Artificial Intelligence for Cyber Threat
  Intelligence (XAI-CTI) Applications
(Due Date: December 1, 2020)

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
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. Artificial intelligence
(AI)-based methods such as machine learning, data mining, text mining,
network science, and deep learning hold significant promise in sifting
through large quantities of structured, unstructured, and
semi-structured cybersecurity data to deliver novel CTI capabilities
with unprecedented efficiency and effectiveness. Despite their rapid
proliferation through the academic and industry CTI landscape, AI
methods are often black boxes. As a result, it is often unclear how
and/or why an algorithm executed its decision-making process. Lack of
interpretability can affect model performance, prevent systematic
model tuning, and reduce algorithm trustworthiness. Ultimately, these
drawbacks hinder key stakeholders (e.g., security analysts) from
effectively leveraging AI-based decisions for critical CTI tasks
(e.g., security control deployment).

In light of these critical limitations, this special issue seeks
high-quality papers related to emerging applications, techniques, and
methodologies related to Explainable Artificial Intelligence (XAI) for
CTI applications. Topics of interest include, but are not limited to:

  *   Interpretable multi-view representation learning for fusing
        disparate CTI data sources (e.g., threat feeds)
  *   Interpretable adversarial learning for CTI applications
  *   Explainable deep learning on graph structured cybersecurity data
  *   Real-time XAI for cyber threat detection
  *   Explainable Deep Bayesian learning for CTI
  *   Intelligent feature selection for interpretable CTI analytics
        (e.g., malware analysis, IP reputation services, etc.)
  *   XAI-based diachronic linguistics to detect emerging threats from
        Social Media Intelligence (SOCINT)
  *   Dark Web Analytics for Proactive Cyber Threat Intelligence applications
  *   Explainable OSINT analytics for cybersecurity applications
  *   XAI methods for Internet of Things (IoT) fingerprinting, anomaly
        detection, network telescopes, measurements, and others
  *   Fusion of emerging XAI-based methods with conventional CTI
        analytics (e.g., event correlation, IP reputation services)
  *   XAI for CTI augmentation (e.g., human-in-the-loop systems)

All accepted manuscripts are expected to make a significant scientific
contribution. Contributions in this special issue include, but are not
limited to, novel representations of emerging cybersecurity data,
novel algorithms, and new CTI systems. 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.). Making data, code,
and processes publicly available to facilitate scientific
reproducibility is not required, but is strongly encouraged. Given the
scope of this special issue, articles are expected to clearly
articulate the how and why their proposed approaches fall into the
category of XAI.

Important Dates
Manuscript Submission Deadline: December 1, 2020
First Round of Reviews: February 15, 2021
Revised Papers Due: April 15, 2021
Final Notification: May 31, 2021
Final Manuscript Due: July 15, 2021
Tentative Publication Date: August/September or November/December of 2021

Submission Guidelines

Papers submitted to this special issue for possible publication must
be original and must not be under consideration for publication in any
other journal or conference. TDSC requires meaningful technical
novelty in submissions that extend previously published conference
papers. Extension beyond the conference version(s) is not simply a
matter of length. Thus, expanded motivation, expanded discussion of
related work, variants of previously reported algorithms, incremental
additional experiments/simulations, may provide additional length but
will fall below the line for proceeding with review. Please read the
Author Information
and Journal Peer Review

Submissions must be directly submitted via the TDSC submission website

Guest Editors

  * Dr. Hsinchun Chen,
      Regents Professor, Management Information Systems, University of

  * Dr. Bhavani Thuraisingham,
      Professor, Computer Science, University of Texas at Dallas

  * Dr. Murat Kantarcioglu
      Professor,  Computer Science, University of Texas at Dallas

  * Dr. Sagar Samtani
      Assistant Professor, Operations and Decision Technologies,
      Indiana University 

Questions can be directed to the guest editor team. When contacting
the guest editors, please ensure that multiple editors are included in
the correspondence to ensure timely turnaround of inquiries.