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IEEE TDSC - XAI-CTI 2020 : Special Issue on Explainable Artificial Intelligence for Cyber Threat Intelligence (XAI-CTI) Applications

Worldwide
Submission Deadline: Dec 01, 2020
Notification of Acceptance: Feb 15, 2021
Camera Ready Version Due : Apr 15, 2021

About

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).

Call For Paper

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)

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