Machine Learning for Cyber Security

Description

Without mathematics no science would survive. This especially applies to the engineering sciences which highly depend on the applications of mathematics and mathematical tools such as optimization techniques, finite element methods, differential equations, fluid dynamics, mathematical modelling, and simulation. Neither optimization in engineering, nor the performance of safety-critical system and system security; nor high assurance software architecture and design would be possible without the development of mathematical applications. De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences (AMEIS) focusses on the latest applications of engineering and information technology that are possible only with the use of mathematical methods. By identifying the gaps in knowledge of engineering applications the AMEIS series fosters the international interchange between the sciences and keeps the reader informed about the latest developments. Preface Contents List of contributors Editor’s biography Differential privacy: a solution to privacy issue in social networks Cracking Captcha using machine learning algorithms: an intersection of Captcha categories and ML algorithms The ransomware: an emerging security challenge to the cyberspace Property-based attestation in device swarms: a machine learning approach A review of machine learning techniques in cybersecurity and research opportunities A framework for seborrheic keratosis skin disease identification using Vision Transformer Mapping AICTE cybersecurity curriculum onto CyBOK: a case study Index