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Data Science in Cybersecurity 2019 : Data Science in Cybersecurity and Cyberthreat Intelligence

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Link: https://www.lesliesikos.com/call-for-chapters/data-science-in-cybersecurity-and-cyberthreat-intelligence/
 
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Submission Deadline Jan 7, 2019
 

Call For Papers

The forthcoming volume "Data Science in Cybersecurity and Cyberthreat Intelligence" of the Springer book series "Intelligent Systems Reference Library" now invites chapters.

Aim and Scope

With the rapid increase of cyberattacks, accurate security information is becoming more and more difficult to obtain, for example due to inabilities to deal with the increasing data volume, data complexity, data variety, data veracity, and (in)scalability of data processing algorithms. To manipulate security data efficiently, one has to deal with the heterogeneity and inconsistency of data sources used to fuse security information, which come with different data structures, file formats, and serializations, and may constitute unstructured, semi-structured, and structured data. The quality and trustworthiness of data depend on how certain the represented knowledge is, which can be backed by data provenance.

Data scientists of organizations often do not work in security; instead they focus on business outcomes. However, data science can actually be a viable component of security strategies to gain cyber-situational awareness via advanced data processing techniques, fuse technical data from diverse sources, and ensure data quality and unambiguity. By using data science techniques, security professionals can manipulate and analyze network and security data efficiently, and uncover valuable insights from data-driven risk assessment and security performance management. Data science can be well utilized in cybersecurity in terms of data preparation, anomaly detection, exploratory data analysis, data visualization, modeling, and optimization. It is useful in preprocessing raw security data for machine learning, detecting anomalous behavior and malicious content, and creating machine learning algorithms to identify potential cyberthreats. The enormous datasets used in security applications require big data analytics and parallel computing frameworks, which can also be provided by data scientists to make security-related information meaningful.

Submission Guidelines

Submissions are expected from, but are not limited to, the following topics:

Introduction to Data Science in Cybersecurity and Cyberthreat Intelligence
Information fusion of communication network data for cyberthreat intelligence
Big data analysis to identify suspicious activities
Deep data mining to find hidden, potential threats
Minimize false alarms using data analytics
Demystifying security data to minimize misleading and ambiguous data
Adversarial machine learning

Proposal

Chapter proposals should be 100–150 words and should be submitted to volume editor Leslie Sikos (firstname.lastname at unisa.edu.au).

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