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This book provides an essential compilation of relevant and cutting edge academic and industry work on key cybersecurity applications topics. Further, it introduces cybersecurity applications to the public at large to develop their cybersecurity applications knowledge and awareness. The book concentrates on a wide range of advances related to Cybersecurity Applications which include, among others, applications in the areas of Data Science, Internet of Things, Artificial Intelligence, Robotics, Web, High-Tech Systems, Cyber-Physical Systems, Mobile Devices, Digital Media, and Cloud Computing. It introduces the concepts, techniques, methods, approaches and trends needed by cybersecurity application specialists and educators for keeping current their cybersecurity applications knowledge. Further, it provides a glimpse of future directions where cybersecurity applications are headed. The book can be a valuable resource to applied cybersecurity experts towards their professional development efforts and to students as a supplement to their cybersecurity courses.
Mobile advertisement fraud has been growing ever since mobile Internet became popular. From click redirections to user APPs, many different fraud methods are developed in the last decade. Some of these methods are easy to identify, while some are not. Besides these, some methods are specially designed to avoid fraud detection. These methods are making mobile anti-fraud become a more and more challenging task for mobile Internet companies. This problem blocks the application of supervised learning in the anti-fraud area since there is no training data for a model to learn. In another word, one of the most serious problems in the mobile anti-fraud area is the uncertainty of fraudsters’ identification. Thus, to solve the problem of uncertainty, we are applying fuzzy and rough set theory to mobile fraud detection. Fuzzy sets and rough sets address two important and mutually orthogonal characteristics of imperfect data and knowledge