Secure Voice Processing Systems Against Malicious Voice Attacks

This book provides readers with the basic understanding regarding the threats to the voice processing systems, the state-of-the-art defense methods as well as the current research results on securing voice processing systems.It also introduces three mechanisms to secure the voice processing systems against malicious voice attacks under different scenarios, by utilizing time-domain signal waves, frequency-domain spectrum features, and acoustic physical attributes. First, the authors uncover the modulated replay attack, which uses an inverse filter to compensate for the spectrum distortion caused by the replay attacks to bypass the existing spectrum-based defenses. The authors also provide an effective defense method that utilizes both the time-domain artifacts and frequency-domain distortion to detect the modulated replay attacks. Second, the book introduces a secure automatic speech recognition system for driverless car to defeat adversarial voice command attacks launched from car loudspeakers, smartphones, and passengers. Third, it provides an acoustic compensation system design to reduce the effects from the spectrum reduction attacks, by the audio spectrum compensation and acoustic propagation principle. Finally, the authors conclude with their research effort on defeating the malicious voice attacks and provide insights into more secure voice processing systems. This book is intended for security researchers, computer scientists, and electrical engineers who are interested in the research areas of biometrics, speech signal processing, IoT security, and audio security. Advanced-level students who are studying these topics will benefit from this book as well.

This book presents the security and privacy challenges of the smart home following the logic of “terminal device – voice interface – application platform”. For each component, the authors provide answers to the three questions: 1) In the terminal device layer, how to conduct cross-layer privacy breach analysis and provide effective countermeasures; 2) In the voice interface layer, how to design effective and lightweight schemes to defend against voice spoofing; 3) In the application layer, how to design an effective anomaly detection system without breaching the application platform. The authors conduct a thorough analysis of the security threats and challenges in each component of the smart home, review the existing state-of-the-art solutions proposed by other researchers, and elaborate on proposed countermeasures. This book aims to provide both security threats analysis and state-of-the-art countermeasures for the smart home network.

This book focuses on advanced techniques used for feature extraction, analysis, recognition, and classification in the area of biomedical signal and image processing. Contributions cover all aspects of artificial intelligence, machine learning, and deep learning in the field of biomedical signal and image processing using novel and unexplored techniques and methodologies. The book covers recent developments in both medical images and signals analyzed by artificial intelligence techniques. The authors also cover topics related to development based artificial intelligence, which includes machine learning, neural networks, and deep learning. This book will provide a platform for researchers who are working in the area of artificial intelligence for biomedical applications. Provides insights into medical signal and image analysis using artificial intelligence; Includes novel and recent trends of decision support system for medical research; Outlines employment of evolutionary algorithms for biomedical data, big data analysis for medical databases, and reliability, opportunities, and challenges in clinical data.

Internet of things networks have shown promising outcomes in the provisioning of potentially critical services such as safety applications, healthcare, and manufacturing. However, there are many challenges related to the security, data analysis, and limited resources of the performed operations that require further investigation. Additional research is necessary to address the concerns and doubts of researchers and industry professionals in the Internet of Things. Security, Data Analytics, and Energy-Aware Solutions in the IoT reports novel methodologies, theories, technologies, and solutions for security and data analytics techniques and energy-aware solutions for the Internet of Things. Covering a wide range of topics such as laser attacks and personal data, it is ideal for academicians, industry professionals, researchers, instructors, and students.

This book constitutes the thoroughly refereed post-conference proceedings of the 19th International Conference on Information Security Applications, WISA 2018, held on Jeju Island, Korea, in August 2018. The 11 revised full papers and 11 short papers presented in this volume were carefully reviewed and selected from 44 submissions. #The primary focus of WISA 2018 was on systems and network security including all other technical and practical aspects of security applications and also on the embedded, unmanned or autonomous systems and cyber physical systems in general.

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

This book constitutes the refereed proceedings of the Fourth International Conference on Futuristic Trends in Network and Communication Technologies, FTNCT 2021. The prime aim of the conference is to invite researchers from different domains of network and communication technologies to a single platform to showcase their research ideas. The selected papers are organized in topical sections on network and computing technologies; wireless networks and Internet of Things (IoT); futuristic computing technologies; communication technologies, security, and privacy. The volume will serve as a reference resource for researchers and practitioners in academia and industry.