Special Issue on Security Data Science and Cyber Threat Management

Data Science is the practice of deriving valuable information from data. Security Data Science can be referred to the application of advanced analytics to activity and access data to uncover unidentified risks. Security Data Science is "data driven" meaning that new insights and value comes directly from data. In Security the valuable insight leads to reduced threat, where it is appearing to meet the challenges of processing very large data sets (as known as "Big Data") and the explosion of new data generated from smart devices, IoT, web, mobile, social media and the cyber world. Data Science has a long and rich history in security and fraud monitoring. The information security and fraud prevention community have been evolving Security Data Science in order to tackle the challenges of managing and gaining insights from enormous data stream, discovery of insider threats and prevention of cyber fraud.

Cyber Threat Management (CTM) is an advanced management framework enabling early identification of threats, data driven situational awareness, accurate decision-making, and timely threat mitigating actions. CTM includes the use of advanced analytics to optimize intelligence, generate security intelligence, and provide Situational Awareness. Security Data Science plays a critical role in the CTM framework.

This special issue is proposed to bring together researchers to exchange the state-of-art research results in advances of Security Data Science and the CTM framework. This issue will concentrate on both theoretical aspects and practical research on Security Data Science.

Selected papers relevant to Data Science and Cyber Threat Management from ACISP 2016 (The 21st Australasian Conference on Information Security and Privacy) are also invited to submit an extended version to this Special Issue. Each paper submitted from this stream must be substantially extended, with at least 50% difference from its conference version.

Topics

The aim of the Special Issue is to promote research and reflect the most recent advances of Security Data Science and the CTM, with emphasis on the following aspects, but certainly not limited to:

Security Modelling and Threat in Big Data
Ÿ Vulnerability Analysis
Ÿ Secure Data Management Outsourcing
Ÿ Intrusion Detection for Large Scale Network
Ÿ Privacy in Big Data Integration and Transformation
Ÿ Data Confidentiality
Ÿ Cyber Crime Detection and Prevention
Ÿ Network Forensics
Ÿ Secure Sharing for Big Data
Ÿ Malware Detection
Ÿ Governance, Risk Management, and Compliance Automation
Ÿ Big Data Privacy Policies and Standard
Ÿ Data Mining Security for Big Data
Ÿ Visualizing Large Scale Security Data

Guest Editors

Professor Javier Lopez, University of Malaga, Spain

Professor Xinyi Huang, Fujian Normal University, China

Dr Joseph Liu, Monash University, Australia

Important Dates

Submission Due: August 1, 2016

1st Round Notification: October 20, 2016

Revision: January 10, 2017

Final Notification: March 15, 2017