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This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender systems, malware detection, intrusion detection and sentiment analysis. It explains how such classification problems can be solved using various statistical and machine learning methods, including K nearest neighbours, Bayesian classifiers, the logit method, discriminant analysis, several kinds of artificial neural networks, support vector machines, classification trees and other kinds of rule-based methods, as well as random forests and other kinds of classifier ensembles. The book covers a wide range of available classification methods and their variants, not only those that have already been used in the considered kinds of applications, but also those that have the potential to be used in them in the future. The book is a valuable resource for post-graduate students and professionals alike.
This book is a collection of high-quality peer reviewed contributions from the academicians, researchers, practitioners, and industry professionals, accepted in the International Conference on Advances in Data Computing, Communication and Security (I3CS2021) organized by the Department of Electronics and Communication Engineering in collaboration with the Department of Computer Engineering, National Institute of Technology, Kurukshetra, India during 08-10 Sep 2021. The fast pace of advancing technologies and growing expectations of the next-generation requires that the researchers must continuously reinvent themselves through new investigations and development of the new products. The theme of this conference is devised as "Embracing Innovations" for the next-generation data computing and secure communication system.
The authors develop a malware fingerprinting framework to cover accurate android malware detection and family attribution in this book. The authors emphasize the following: (1) the scalability over a large malware corpus; (2) the resiliency to common obfuscation techniques; (3) the portability over different platforms and architectures. First, the authors propose an approximate fingerprinting technique for android packaging that captures the underlying static structure of the android applications in the context of bulk and offline detection at the app-market level. This book proposes a malware clustering framework to perform malware clustering by building and partitioning the similarity network of malicious applications on top of this fingerprinting technique. Second, the authors propose an approximate fingerprinting technique that leverages dynamic analysis and natural language processing techniques to generate Android malware behavior reports. Based on this fingerprinting technique, the authors propose a portable malware detection framework employing machine learning classification. Third, the authors design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. The authors then leverage graph analysis techniques to generate relevant intelligence to identify the threat effects of malicious Internet activity associated with android malware. The authors elaborate on an effective android malware detection system, in the online detection context at the mobile device level. It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. Researchers working in mobile and network security, machine learning and pattern recognition will find this book useful as a reference. Advanced-level students studying computer science within these topic areas will purchase this book as well.
The book is a collection of high-quality peer-reviewed research papers presented at International Conference on Frontiers of Intelligent Computing: Theory and applications (FICTA 2016) held at School of Computer Engineering, KIIT University, Bhubaneswar, India during 16 – 17 September 2016. The book presents theories, methodologies, new ideas, experiences and applications in all areas of intelligent computing and its applications to various engineering disciplines like computer science, electronics, electrical and mechanical engineering.
The conference proceedings book is a collection of high-quality research articles in the field of intelligent vision and computing. It also serves as a forum for researchers and practitioners from both academia and industry to meet and share their expertise and experience. It provides opportunities for academicians and scientists along with professionals, policymakers, and practitioners from various fields in a global realm to present their research contributions and views, on one forum and interact with members inside and outside their own particular disciplines.
This book constitutes revised selected papers of the Third International Conference on Computing Science, Communication and Security, COMS2 2022, held in Gandhinagar, India, in February 2022. Due to the COVID-19 pandemic the conference was held virtually. The 22 full papers were thoroughly reveiwed and selected from 143 submissions. The papers present ideas, and research results on the aspects of computing science, network communication, and security.
This book constitutes the refereed post-conference proceedings of the 11th International Conference on Broadband Communications, Networks, and Systems, Broadnets 2020, which took place in Qingdao, China, in December 2020. The 13 full papers presented were carefully reviewed and selected from 32 submissions. The papers are thematically grouped as a session on wireless network and security and a session on communication quality.
With the rapid evolution of technology, identifying new risks is a constantly moving target. The metaverse is a virtual space that is interconnected with cloud computing and with companies, organizations, and even countries investing in virtual real estate. The questions of what new risks will become evident in these virtual worlds and in augmented reality and what real-world impacts they will have in an ever-expanding internet of things (IoT) need to be answered. Within continually connected societies that require uninterrupted functionality, cyber security is vital, and the ability to detect potential risks and ensure the security of computing systems is crucial to their effective use and success. Proper utilization of the latest technological advancements can help in developing more efficient techniques to prevent cyber threats and enhance cybersecurity. Risk Detection and Cyber Security for the Success of Contemporary Computing presents the newest findings with technological advances that can be utilized for more effective prevention techniques to protect against cyber threats. This book is led by editors of best-selling and highly indexed publications, and together they have over two decades of experience in computer science and engineering. Featuring extensive coverage on authentication techniques, cloud security, and mobile robotics, this book is ideally designed for students, researchers, scientists, and engineers seeking current research on methods, models, and implementation of optimized security in digital contexts.
This book offers a comprehensive study of computer recognition systems – one of the most promising directions in artificial intelligence. It presents a collection of 52 carefully selected articles contributed by experts in the field of pattern recognition, discussing both methodological aspects and applications of current research. It includes the following sections: · Features, learning, and classifiers · Biometrics · Data stream classification and big data analytics · Image processing and computer vision · Medical applications · Applications It is a valuable reference tool for scientists dealing with the problems of designing computer pattern recognition systems, including researchers and students of computer science, artificial intelligence and robotics.