Wei Emma Zhang - Böcker

The text highlights a comprehensive survey that focuses on all security aspects and challenges facing the Internet of Things systems, including outsourcing techniques for partial computations on edge or cloud while presenting case studies to map security challenges. It further covers three security aspects including Internet of Things device identification and authentication, network traffic intrusion detection, and executable malware files detection.This book:Presents a security framework model design named Behavioral Network Traffic Identification and Novelty Anomaly Detection for the IoT InfrastructuresHighlights recent advancements in machine learning, deep learning, and networking standards to boost Internet of Things securityBuilds a near real-time solution for identifying Internet of Things devices connecting to a network using their network traffic traces and providing them with sufficient access privilegesDevelops a robust framework for detecting IoT anomalous network trafficCovers an anti-malware solution for detecting malware targeting embedded devicesIt will serve as an ideal text for senior undergraduate and graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

The Internet of Vehicles (IoV) is referred to as an efficient and inevitable convergence of the Internet of Things, intelligent transportation systems, edge / fog and cloud computing, and big data, all of which could be intelligently harvested for the cooperative vehicular safety and non-safety applications as well as cooperative mobility management. A secure and low-latency communication is, therefore, indispensable to meet the stringent performance requirements of the safety-critical vehicular applications. Whilst the challenges surrounding low latency are being addressed by the researchers in both academia and industry, it is the security of an IoV network which is of paramount importance, as a single malicious message is perfectly capable enough of jeopardizing the entire networking infrastructure and can prove fatal for the vehicular passengers and the vulnerable pedestrians. This book thus investigates the promising notion of trust in a bid to strengthen the resilience of the IoV networks. It not only introduces trust categorically in the context of an IoV network, i.e., in terms of its fundamentals and salient characteristics, but further envisages state-of-the-art trust models and intelligent trust threshold mechanisms for segregating both malicious and non-malicious vehicles. Furthermore, open research challenges and recommendations for addressing the same are discussed in the same too.

The Internet of Vehicles (IoV) is referred to as an efficient and inevitable convergence of the Internet of Things, intelligent transportation systems, edge / fog and cloud computing, and big data, all of which could be intelligently harvested for the cooperative vehicular safety and non-safety applications as well as cooperative mobility management. A secure and low-latency communication is, therefore, indispensable to meet the stringent performance requirements of the safety-critical vehicular applications. Whilst the challenges surrounding low latency are being addressed by the researchers in both academia and industry, it is the security of an IoV network which is of paramount importance, as a single malicious message is perfectly capable enough of jeopardizing the entire networking infrastructure and can prove fatal for the vehicular passengers and the vulnerable pedestrians. This book thus investigates the promising notion of trust in a bid to strengthen the resilience of the IoV networks. It not only introduces trust categorically in the context of an IoV network, i.e., in terms of its fundamentals and salient characteristics, but further envisages state-of-the-art trust models and intelligent trust threshold mechanisms for segregating both malicious and non-malicious vehicles. Furthermore, open research challenges and recommendations for addressing the same are discussed in the same too.

This book looks at conversational search in intelligent dialogue systems, as it investigates and addresses the challenges pertinent to effective context incorporation in conversational question answering (ConvQA). The authors explore the possibility of designing a scalable Conversational Question Answering Agent that can handle the challenges of incomplete/ambiguous questions, better able to relate to co-references to cope with the problems of effective weights and optimal threshold selection in vehicular networks. A fundamental emphasis is the understanding of ambiguous follow-up questions and the generation of contextual and question entities to fill in the missing information gaps. Key topics are studied, such as ‘hard history selection’ to filter out the context that is not relevant and performing a re-ranking of the selected turns based on their significance to answer the question as a part of the soft history selection process.This book aims to demonstrate that the history selection and modelling approaches proposed can effectively improve the performance of ConvQA models in different settings. The proposed models are compared with the state-of-the-art vis-à-vis different conversational datasets and provide new insights into conversational information retrieval. Through a systematic study of structured representations, entity-aware history selection, and open-domain passage retrieval using contrastive learning, this book presents a robust framework for advancing multi-turn QA systems.It is an essential resource for researchers, practitioners, and graduate students working at the intersection of NLP, dialogue systems, and intelligent information access.

Their feature modelling approach eschews the knowledge requirement on both the querying languages and system.To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities.

This book constitutes the refereed proceedings of the 13th International Conference on Advanced Data Mining and Applications, ADMA 2017, held in Singapore in November 2017. The 20 full and 38 short papers presented in this volume were carefully reviewed and selected from 118 submissions. data mining applications;

This book constitutes revised selected papers from the Australasian Symposium on Service Research and Innovation, ASSRI, held in Sydney Australia.The 11 full papers presented from ASSRI 2017, which took place during October 19-20, 2017, were carefully reviewed and selected from 26 submissions.

Their feature modelling approach eschews the knowledge requirement on both the querying languages and system.To extract knowledge from unstructured Web sources, the authors examine two kinds of Web sources containing unstructured data: the source code from Web repositories and the posts in programming question-answering communities.

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions.

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions.

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions.

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions.

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions.