Connectionists: [CFC] Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity

[Anacleto Correia]

*Call for Chapters*

*Apologies for cross-posting, and please help to disseminate.*



*Dear Researchers,*



You are invited to submit a chapter proposal for a book about *Applications
of Machine Learning and Deep Learning for Privacy and Cybersecurity*.

There are *no submission or acceptance fees* for manuscripts submitted to
this book publication. All manuscripts are accepted based on a *double-blind
peer review editorial process*.

Chapter proposals (1.000 to 2.000 words) may be submitted at this
https://www.igi-global.com/publish/call-for-papers/call-details/5297, on or
before *June 25, 2021*.



*Introduction*

The growth of innovative cyber threats, many based on metamorphosing
techniques, has led to security breaches and the exposure of critical
information in sites that appeared to be impenetrable. The consequences of
these hacking actions were, inevitably, privacy violation, data corruption,
or information leaking. Machine learning, in particular deep learning, and
data mining techniques have significant applications in the domains of
privacy protection (e.g., authentication, privacy-preserving, data privacy
compliance) and in cybersecurity (e.g., intrusion detection, malware
detection, phishing/spam detection, website defacement detection). This
book provides machine and deep learning methods for analysis and
characterization of events regarding privacy and anomaly detection, as well
as for establishing predictive models for cyber-attacks or privacy
violations. It also provides case studies of the use of these techniques.
Expected future developments on privacy and cybersecurity applications are
also discussed including research topics, such as the potential
consequences of quantum computing.

*Objective*

This comprehensive and timely book provides an overview of the field of
Machine and Deep Learning in the areas of cybersecurity and privacy,
followed by an in-depth view of emerging research exploring the theoretical
aspects of machine and deep learning, as well as real-world
implementations. The objective of the book is, therefore, to disseminate
the latest advances regarding privacy and cybersecurity fueled by machine
and deep learning techniques. The impact of the book lies in the
improvements expected by the widespread implementation of the machine and
deep learning techniques described. These techniques can be used by
automated systems for smart data analysis and a higher success rate in
threats and malware detection is expected, as well as a reduction in
nonconformities with privacy rules. The value-added of the book lies on the
innovative methods and techniques described that can help improve the
performance and reliability of privacy and cybersecurity applications.

*Target Audience*

This book is designed for IT specialists, computer engineers, industry
professionals, privacy specialists, security professionals, consultants,
researchers, academics, and students interested in the impact of machine
and deep learning in privacy and cybersecurity, and the methodologies that
can help improve the effectiveness of applications related to those aspects.

*Recommended Topics*

Machine and Deep Learning for Privacy-Preserving; Ontologies for Machine
and Deep Learning in Cyberattack Detection; Assessing Machine and Deep
Learning Algorithms for Cybersecurity; Advances in Machine and Deep
Learning for Malware Detection; Machine and Deep Learning Methods for
Intrusion Detection; Machine and Deep Learning for Authentication; Machine
and Deep Learning Algorithms for Predicting Cyberattack Rates;
Cybersecurity and Privacy on the Internet of Things with Machine and Deep
Learning; Machine and Deep Learning Challenges for Cybersecurity in Edge
Computing; Auditing and Privacy-Preserving with Machine Learning and
Blockchain; Machine and Deep Learning Algorithms for privacy compliance
(GDPR and others); The consequences of Quantum Computing in Machine and
Deep Learning Algorithms for Cybersecurity; Machine and Deep Learning in
Automotive Cybersecurity and Privacy; Future trends on Artificial
Intelligence, Machine Learning, and Deep Learning in Cybersecurity.

*Submission Procedure*

Researchers and practitioners are invited to submit on or before *June 25,
2021*, a chapter proposal of 1,000 to 2,000 words clearly explaining the
mission and concerns of his or her proposed chapter. Authors will be
notified by *June 30, 2021* about the status of their proposals and sent
chapter guidelines. Full chapters are expected to be submitted by *September
23, 2021*, and all interested authors must consult the guidelines for
manuscript submissions at
https://www.igi-global.com/publish/contributor-resources/before-you-write/
<https://www.igi-global.com/publish/contributor-resources/before-you-write/>prior
to submission. All submitted chapters will be reviewed on a double-blind
review basis. Contributors may also be requested to serve as reviewers for
this project.



All proposals should be submitted through the eEditorial Discovery® online
submission manager.


*Inquiries can be forwarded electronically by mail to*:
anacleto.correia at ieee.org

More information:
https://www.igi-global.com/publish/call-for-papers/call-details/5297

Important Dates
*June 25, 2021*: Chapter Proposal Submission Deadline
*June 30, 2021*: Notification of Acceptance
*September 23, 2021*: Full Chapter Submission
*November 21, 2021*: Review Results Returned
*January 2, 2022*: Final Acceptance Notification
*January 16, 2022*: Final Chapter Submission



Kind regards,

Editors

*Victor Lobo*, Full Professor, Nova-IMS, Naval Academy, Portugal

*Anacleto Correia*, Associate Professor, Naval Academy, Portugal
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