Call for Paper: Cyber Threat Intelligence

A Springer Book - Advances in Information Security series 

These days cyber security and forensics specialists are supposed to detect, analyze and defend against many cyber threats in almost real-time conditions. Timely dealing with such a huge number of attacks is not possible without employment of artificial intelligence and machine learning techniques. When a significant amount of data is collected from or generated by different security monitoring solutions; intelligent big-data analytical techniques are necessary to mine, interpret and extract knowledge of those data. The emerging field of cyber threat intelligence is investigating applications of artificial intelligence and machine learning techniques to perceive, reason, learn and act intelligently against advanced cyber attacks.

This book will focus on cutting-edge research from both academia and industry, with a particular emphasis on providing wider knowledge of the field, novelty of approaches, combination of tools and so forth to perceive, reason, learn and act  on a wide range of data collected from different cyber security and forensics solutions.

Specifically, this book welcomes two categories of papers: (1) invited articles from qualified experts; and (2) contributed papers from open call with list of addressed topics. Topics of interest include but not limited to:

  • Detection and analysis of advanced threat actors tactics, techniques and procedures
  • Application of machine learning tools and techniques in cyber threat intelligence
  • Theories and models for detection and analysis of advanced persistent threats
  • Automated and smart tools for collection, preservation and analysis of digital evidences
  • Threat intelligence techniques for constructing, detecting, and reacting to advanced intrusion campaigns
  • Applying machines learning tools and techniques for malware analysis and fighting against cyber crimes
  • Intelligent forensics tools, techniques and procedures for cloud, mobile and data-centre forensics
  • Intelligent analysis of different types of data collected from different layers of network security solutions
  • Threat intelligence in cyber security domain utilising big data solutions such as Hadoop
  • Intelligent methods to manage, share, and receive logs and data relevant to variety of adversary groups
  • Interpretation of cyber threat and forensic data utilising intelligent data analysis techniques
  • Infer intelligence of existing cyber security data generated by different monitoring and defense solutions
  • Automated and intelligent methods for adversary profiling
  • Automated integration of analysed data within incident response and cyber forensics capabilities

Important Dates:

 

  • Submission deadline: 1 Jun 2017 (extended/firm deadline) 
  • Authors’ notification: 1 Jul 2017 (or 2-month after submission)
  • Revisions due: 15 Sep 2017 
  • Camera ready version due: 01 Oct 2017
  • Tentative publication date: Late 2017

Note: Papers are gradually reviewed and accepted from 1st Oct 2016 until submission deadline or when we reach to maximum number of pages agreed with Springer. Authors are suggested to submit their papers as early as possible and consult with one of the editors prior to the paper submission to validate relevance and suitability of their papers.

Submission:

All accepted papers will be published as contributed chapters to a book titled "Cyber Threat Intelligence". The book will be published as part of Springer Advancement in Information Security series. Please carefully format your manuscript in accordance with Springer Book Manuscript Guideline.  To Submit your paper please CLICK HERE  

Editors

For any enquires regarding your submission or suitability of your paper, please feel free to contact book editors: 

  • Mauro Conti, University of Padova, Italy (conti - at - math.unipd.it ).
  • Tooska Dargahi, University of Rome Tor Vergata, Italy (tooska.dargahi – at – cnit.it  /   tooska.dargahi – at – uniroma2.it)
  • Ali Dehghantanha, University of Salford, UK (a.dehghantanha - at - Salford.ac.uk)