ICOST 2018 - Book Title: Machine Learning and Cognitive Science Applications in Cyber Security

Machine learning, artificial intelligence, predictive analytic and other similar disciplines of cognitive science applications have recently found significant attention in the domain of cyber security. Traditionally, signature based detection techniques using natural language processing and/or formal algebraic/automata methods have been the forte of cyber security. However, in the last few years, with the evolution of advanced persistent threats and mutation techniques including but not limited to state of the art metamorphic malware mechanisms, new venues of challenges are opened for security researchers, professionals and policy makers. Due to the dynamic and varying nature of advanced obfuscated threats and with the recent disclosures of data breaches in international organizations, there has been observed a paradigm shift in defending data networks; from a traditional reactive detection approach using firewalls, IDSs and antimalware technologies to proactive and hunting approach using machine learning techniques. Role of cognitive science is important in devising machine learning based solutions because threat actors are humans and without incorporating the fundamental concepts of human cognition, which is nevertheless the basis of machine learning systems, it is not possible to lead the continual battle with the malicious actors. Cyber security is a very broad domain and encompasses cryptography, network and host based security frameworks, intrusion detection and prevention systems and the due role of security audit and policy mechanisms, to name a few. As literature reflects, the application area of cyber security for machine learning tools is posed with challenges which are unprecedented in other applications domains whether it is image processing, medical sciences, financial engineering or marketing space. Therefore, it is required to explore new paradigms of machine intelligence to solve these unique challenges of cyber security. With the evolution of cognitive sciences and better understanding of human brain, it is possible to improve the detection performance of machine learning algorithms and advance their level of reliability and performance.
Target Audience:
The book will address researchers, academicians, and industry professionals who are working in the domain of cyber security or who would like to add machine learning capabilities in their particular work in cyber security. Further, this book will also help data scientists and incidence response and threat intelligence teams in relevant information and cyber security departments. In addition, experts of government and private funding organizations can benefits through the diverse topics in cyber security. This book will provide insight into state of the art research to various graduate level academic departments e.g. electrical and computer engineering, computer science, cyber security, mathematics etc.
Recommended Topics:
• Malware characterization
• Attribution and Detection of poly/meta-morphic malware
• Modeling of malware actions
• Theoretical foundations of malware mutation
• Challenges of machine learning applications in cyber security
• Ensemble learning mechanisms for threat detection
• Deep learning models in threat discovery
• Efficient computational intelligence methods in cyber security
• New Artificial Immune Systems for threat detection
• Application of graph theoretical methods in network based threat detection
• Application of graph theoretical methods in host based threat detection
• Collaborative methods in cyber security
• Threats to machine learning application in cyber security
• Efficient feature extraction techniques to characterize threats
• Cognitive evolution in cyber security
• Efficient computational methods to generate signatures of threats
• Cyber Kill Chain analysis
• New tools to improve machine learning applications in advanced threat detection
Submission Procedures
Researchers and practitioners are invited to submit on or before January 31 2018, 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 February 15 2018 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by March 15, 2018, and all interested authors must consult the guidelines for manuscript submissions at http://www.igi-global.com/publish/contributor-reso... 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.
Note: 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.
All proposals should be submitted through the eEditorial Discovery®TM online submission manager.
Publisher:
This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. For additional information regarding the publisher, please visit www.igi-global.com. This publication is anticipated to be released in 2018-19.
Contact:
salman.scarlet-AT-gmail.com