Explain what MLCCS-2018 is.  

Book on “Machine Learning for Computer and Cyber Security: Principles, Algorithms, and Practices”

Submission Guidelines

Introduction:

Computer security is the use of technology, policies, and education to assure the confidentiality, integrity, and availability of data during its storage, processing, and transmission. To secure data, we pursue three activities: prevention, detection, and recovery. This book will be about the use of machine learning and data mining methods to secure data, and such methods are best suited for detection. Detection is simply the process of identifying something’s true characteristic. For example, we might want to detect if a program contains malicious logic. Informally, a detector is a program that reports positively when it detects the characteristic of interest; otherwise, it reports negatively or nothing at all. There are two ways to build a detector: We can build or program a detector ourselves, or we can let software build a detector from data. To build a detector ourselves, it is not enough to know what we want to detect, for we must also know how to detect what we want. The complexity of today’s networked computers makes this a daunting task in all but the simplest cases. Researchers may debate where the exact point lies, but starting somewhere on this spectrum leading to the ideal are methods of machine learning.

This book will use a wealth of examples and illustrations to effectively demonstrate the principles, algorithm, challenges and applications of machine learning and data mining for Computer and Cyber Security. This comprehensive book will be full of the valuable insight into machine learning and data mining for computer and cyber security and will touch every corner of the topic of computer and cyber security and will cover most of important security aspects and current trends that are missed in other books. In addition, it will be an excellent book to teach a course on Machine learning and data mining for Computer and security. The material will prepare the students for exercising better protection and defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation in a better manner.

This book will be an invaluable resource for students at all levels interested in machine learning and data mining for computer and cyber security. It will also serve as an excellent reference in cyber security professionals in this fast-evolving and critical field. The contents of this book will be very refreshing, informative, and easy to follow for students ranging from novice to advanced levels. It will contain an impressive collection of up-to-date computer and cyber security issues, analysis and machine learning and data mining based solutions.

Objective of the Book:

The main objective of the book is to give an of principles, algorithm and applications of machine learning based Computer and Cyber security as well as the future research directions overview to students, researchers, subject matter experts, and practitioners. This comprehensive book will be full of the valuable insight into computer and cyber security and will touch every corner of the topic of machine learning for computer and cyber security and will covers most of important security aspects and current trends that are missed in other books The book will help to identify the interesting and exciting areas of future research to apply these techniques.

In addition, it will be an excellent book to teach a course on machine learning for computer and cyber security. The material will prepare the students for exercising better protection in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation in a better manner using machine learning based approaches. The chapter proposals will be selected in the following categories to make a balance of theory, future research directions and practical/use case i.e. Original research article, Case studies, and Review articles in the aforementioned domain.

Target Audience:

The target audience of this book will be composed of professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who are seeking to carry out research and develop software in the field of information and cyber security using Machine learning based approaches. The main objective of the book is to give an overview of principles, algorithm and applications of machine learning based Computer and Cyber security as well as the future research directions. The proposed book is likely to have global reader since the said subject is taught almost by every university worldwide by computer science and engineering or other relevant departments.

All Chapters must be original and not simultaneously submitted to another Book or journal/conference.

Researchers and practitioners are invited to submit their full chapter of 8,000 to 12,000 words clearly explaining the mission and concerns of his or her proposed chapter on or before December 31, 2017 via https://easychair.org/conferences/?conf=mlccs2018 or can also email to Email id: bbgupta@nitkkr.ac.in. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project. Authors will be notified by February 15, 2018 about the decision of their chapter.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices. All manuscripts are accepted based on a double-blind peer review editorial process.

Publisher:

This book is scheduled to be published by CRC press, USA. For additional information regarding the publisher, please visit https://www.crcpress.com/. This publication is anticipated to be released in 2018.

Important Dates:
December 31, 2017: Full Chapter Submission
February 15, 2018: Review Results Returned
March 25, 2018: Final Acceptance Notification
April 15, 2018: Final Chapter Submission

List of Topics

• Machine-Learning Paradigms for Data Mining and Cyber Security
• Supervised Learning for Signature Detection
• Machine Learning for Anomaly Detection
• Machine learning for Biometric security and privacy
• Machine learning for Security and privacy of Web service
• Machine learning for Security and privacy in cloud computing
• Machine learning for Security and privacy in e-services
• Machine learning for Security and privacy in cloud computing
• Machine learning for Security Policies and Access Control
• Machine Learning in Hybrid Intrusion Detection Systems
• Machine Learning for Scan Detection
• Machine Learning for Profiling Network Traffic
• Data-Mining and Machine-Learning Applications in Network Profiling
• Privacy-Preserving Data Mining
• Fundamentals, Overviews, and Machine Learning Trends for Computer and Cyber Security
• Machine learning for Security and privacy in mobile systems
• Machine learning for Security and privacy in wireless sensor networks
• Cyber risk and vulnerability assessment cybercrime
• Machine learning for Visual analytics for cyber security
• Machine learning for Security and privacy in smart grid and distributed generation systems
• Machine learning for Security and privacy in social applications and networks
• Machine learning for Critical infrastructure protection
• Machine learning for Security and privacy in industrial systems
• Machine learning for Security and privacy in pervasive/ubiquitous computing
• Machine learning for Intrusion detection and prevention
• Data-Mining and Machine-Learning Applications in PPDM
• Machine learning for Botnet detection and mitigation
• Machine learning for Security and privacy of Robotic systems
• Machine learning for Security and privacy in ambient intelligence
• Machine learning for Network security and management
• Machine learning for Wireless security
• Machine learning for Bluetooth, WiFi, WiMax security
• Machine learning for Cyber threats, implications and their defense
• Machine learning for Security modelling

Committees

Editor(s):

Dr. Brij Gupta (National Institute of Technology Kurukshetra, Haryana, India)

Dr. Michael Sheng (Macquarie University, Sydney, Australia)

Contact

All questions about submissions should be emailed to 

Dr. Brij B. Gupta, National Institute of Technology Kurukshetra, Haryana, India.

E-mail: bbgupta@nitkkr.ac.in