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IJISP-SI 2017 : Special Issue in IJISP: Machine learning techniques for Information Security and Data Privacy

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Link: http://www.igi-global.com/calls-for-papers-special/international-journal-information-security-privacy/1096
 
When Dec 1, 2017 - Dec 2, 2017
Where Changa, Anand, India
Submission Deadline Jun 1, 2017
Categories    machine learning   information security   fuzzy computing   neuro computing
 

Call For Papers

SPECIAL ISSUE ON
Machine learning techniques for Information Security and Data Privacy

International Journal of Information Security and Privacy (IJISP)

http://www.igi-global.com/calls-for-papers-special/international-journal-information-security-privacy/1096

SUBMISSION DUE DATE: 01 June, 2017

Current technology for information security and data privacy, often does not mature completely because of regular changes in computer, network and the general evolution of information and communication technology (ICT). Many improvements, from different perspectives, should be considered in the technology, so that the challenging nature of the requirements for the current and future computing environments can be accommodated. Till now efficient solutions for information security and data privacy, remains an elusive goal and a big challenge. Promising methodologies and technologies are required for the design and development of effective intelligent solutions. Machine Learning (ML) is a field of Artificial Intelligence (AI) that is concerned with constructing programs/systems that can improve their behavior with experience. Various machine learning techniques can be used for information security and data privacy to lead a secure information system to the extent possible. The focus of the special issue is on high quality research that addresses paradigms, development, applications and implications in the field of machine learning for addressing unique security issues introduced by new computing paradigms.

OBJECTIVE OF THE SPECIAL ISSUE:

The objective of the special issue is to provide readers with a compilation of stimulating and up-to-date articles within the field of machine learning for information security and data privacy.



RECOMMENDED TOPICS:

Topics to be discussed in this special issue include (but are not limited to) the following:

Anomaly detection
Autonomous and adaptive security
Big data and digital forensics
Biometrics
Critical information and infrastructure security
Cryptography and cryptanalysis
Cloud forensics and security
Cyber physical system security
Dimensionality reduction
Frequent event patterns mining
Information assurance
Information security audits
Intrusion detection and prevention
Network security and defense
Malware analysis and detection
Misuse detection
Mobile networks and devices security
Security in Software Defined Networks (SDN)
Security in social networks
Outlier detection
Spam filtering
Ransomware analysis and detection
Rule mining and optimization
Recent trends, technologies and applications
Security risk analysis, modelling, evaluation and management
Trust and privacy
Video surveillance



SUBMISSION PROCEDURE:

Researchers and practitioners are invited to submit papers for this special theme issue on Machine learning techniques for Information Security and Data Privacy on or before June 01, 2017. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.


All inquiries should be directed to the attention of:


Kanubhai K. Patel,

Guest Editor

Charotar University of Science and Technology, India
International Journal of Information Security and Privacy (IJISP)

E-mail: kanupatel.mca@charusat.ac.in


All manuscript submissions to the special issue should be sent through the online submission system:

http://www.igi-global.com/authorseditors/titlesubmission/newproject.aspx

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