Kim-Kwang Raymond Choo

Songqing Chen

Xinwen Fu

Wenjing Lou

Aziz Mohaisen

This two-volume set LNICST 304-305 constitutes the post-conference proceedings of the 15thInternational Conference on Security and Privacy in Communication Networks, SecureComm 2019, held in Orlando, FL, USA, in October 2019. The 38 full and 18 short papers were carefully reviewed and selected from 149 submissions. The papers are organized in topical sections on blockchains, internet of things, machine learning, everything traffic security communicating covertly, let’s talk privacy, deep analysis, systematic theory, bulletproof defenses, blockchains and IoT, security and analytics, machine learning, private, better clouds, ATCS workshop.

15th EAI International Conference, SecureComm 2019, Orlando, FL, USA, October 23–25, 2019, Proceedings, Part II

Security and Privacy in Communication Networks

Mohammed Atiquzzaman

Guojun Wang

Zheng Yan

This book constitutes the refereed proceedings of the 10th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage, SpaCCS 2017, held in Guangzhou, China, in December 2017. The 47 papers presented in this volume were carefully reviewed and selected from 140 submissions. They deal with research findings, achievements, innovations and perspectives in information security and related fields covering topics such as security algorithms and architectures, privacy-aware policies, regulations and techniques, anonymous computation and communication, encompassing fundamental theoretical approaches, practical experimental projects, and commercial application systems for computation, communication and storage.

10th International Conference, SpaCCS 2017, Guangzhou, China, December 12-15, 2017, Proceedings

Security, Privacy, and Anonymity in Computation, Communication, and Storage

James J. Park

Gangman Yi

Vincenzo Loia

This book presents the combined proceedings of the 12th International Conference on Multimedia and Ubiquitous Engineering (MUE 2018) and the 13th International Conference on Future Information Technology (Future Tech 2018), both held in Salerno, Italy, April 23 - 25, 2018. The aim of these two meetings was to promote discussion and interaction among academics, researchers and professionals in the field of ubiquitous computing technologies. These proceedings reflect the state of the art in the development of computational methods, involving theory, algorithms, numerical simulation, error and uncertainty analysis and novel applications of new processing techniques in engineering, science, and other disciplines related to ubiquitous computing.

MUE/FutureTech 2018

Advanced Multimedia and Ubiquitous Engineering

Jie Wu

Ernesto Damiani

This book constitutes the refereed proceedings of the Second International Conference, UbiSec 2022, held in Zhangjiajie, China, during December 28–31, 2022. The 34 full papers and 4 short papers included in this book were carefully reviewed and selected from 98 submissions. They were organized in topical sections as follows: cyberspace security, cyberspace privacy, cyberspace anonymity and short papers.

Second International Conference, UbiSec 2022, Zhangjiajie, China, December 28–31, 2022, Revised Selected Papers

Ubiquitous Security

Leslie F. Sikos

This book presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in enterprises, to identify malicious URLs and DGA-generated domain names, and to improve the security of mHealth wearables. This book details how analyzing the likelihood of vulnerability exploitation using machine learning classifiers can offer an alternative to traditional penetration testing solutions. In addition, the book describes a range of techniques that support data aggregation and data fusion to automate data-driven analytics in cyberthreat intelligence, allowing complex and previously unknown cyberthreats to be identified and classified, and countermeasures to be incorporated in novel incident response and intrusion detection mechanisms.

Data Science in Cybersecurity and Cyberthreat Intelligence

Research on Secure Key Establishment has become very active within the last few years. Secure Key Establishment discusses the problems encountered in this field. This book also introduces several improved protocols with new proofs of security. Secure Key Establishment identifies several variants of the key sharing requirement. Several variants of the widely accepted Bellare and Rogaway (1993) model are covered. A comparative study of the relative strengths of security notions between these variants of the Bellare–Rogaway model and the Canetti–Krawczyk model is included. An integrative framework is proposed that allows protocols to be analyzed in a modified version of the Bellare–Rogaway model using the automated model checker tool. Secure Key Establishment is designed for advanced level students in computer science and mathematics, as a secondary text or reference book. This book is also suitable for practitioners and researchers working for defense agencies or security companies.

Secure Key Establishment

Jemal H. Abawajy

Zheng Xu

This book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to secure our cyberfuture. The book describes approaches and findings that are of interest to business professionals and governments seeking to secure our data and underpin infrastructures, as well as to individual users.

Applications and Techniques in Cyber Intelligence (ATCI 2020)

2020 International Conference on Applications and Techniques in Cyber Intelligence

Ali Dehghantanha

This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud’s log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter. The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS’s cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS’s cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated. This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well as a proposed clustering approach to detect IoT malware in the last two chapters. This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.

Handbook of Big Data Analytics and Forensics

This handbook provides comprehensive knowledge and includes an overview of the current state-of-the-art of Big Data Privacy, with chapters written by international world leaders from academia and industry working in this field. The first part of this book offers a review of security challenges in critical infrastructure and offers methods that utilize acritical intelligence (AI) techniques to overcome those issues. It then focuses on big data security and privacy issues in relation to developments in the Industry 4.0. Internet of Things (IoT) devices are becoming a major source of security and privacy concern in big data platforms. Multiple solutions that leverage machine learning for addressing security and privacy issues in IoT environments are also discussed this handbook. The second part of this handbook is focused on privacy and security issues in different layers of big data systems. It discusses about methods for evaluating security and privacy of big data systems on network, application and physical layers. This handbook elaborates on existing methods to use data analytic and AI techniques at different layers of big data platforms to identify privacy and security attacks. The final part of this handbook is focused on analyzing cyber threats applicable to the big data environments. It offers an in-depth review of attacks applicable to big data platforms in smart grids, smart farming, FinTech, and health sectors. Multiple solutions are presented to detect, prevent and analyze cyber-attacks and assess the impact of malicious payloads to those environments. This handbook provides information for security and privacy experts in most areas of big data including; FinTech, Industry 4.0, Internet of Things, Smart Grids, Smart Farming and more. Experts working in big data, privacy, security, forensics, malware analysis, machine learning and data analysts will find this handbook useful as a reference. Researchers and advanced-level computer science students focused on computer systems, Internet of Things, Smart Grid, Smart Farming, Industry 4.0 and network analysts will also find this handbook useful as a reference.

Handbook of Big Data Privacy

This handbook provides an overarching view of cyber security and digital forensic challenges related to big data and IoT environment, prior to reviewing existing data mining solutions and their potential application in big data context, and existing authentication and access control for IoT devices. An IoT access control scheme and an IoT forensic framework is also presented in this book, and it explains how the IoT forensic framework can be used to guide investigation of a popular cloud storage service. A distributed file system forensic approach is also presented, which is used to guide the investigation of Ceph. Minecraft, a Massively Multiplayer Online Game, and the Hadoop distributed file system environment are also forensically studied and their findings reported in this book. A forensic IoT source camera identification algorithm is introduced, which uses the camera's sensor pattern noise from the captured image. In addition to the IoT access control and forensic frameworks, this handbook covers a cyber defense triage process for nine advanced persistent threat (APT) groups targeting IoT infrastructure, namely: APT1, Molerats, Silent Chollima, Shell Crew, NetTraveler, ProjectSauron, CopyKittens, Volatile Cedar and Transparent Tribe. The characteristics of remote-controlled real-world Trojans using the Cyber Kill Chain are also examined. It introduces a method to leverage different crashes discovered from two fuzzing approaches, which can be used to enhance the effectiveness of fuzzers. Cloud computing is also often associated with IoT and big data (e.g., cloud-enabled IoT systems), and hence a survey of the cloud security literature and a survey of botnet detection approaches are presented in the book. Finally, game security solutions are studied and explained how one may circumvent such solutions. This handbook targets the security, privacy and forensics research community, and big data research community, including policy makers and government agencies, public and private organizations policy makers. Undergraduate and postgraduate students enrolled in cyber security and forensic programs will also find this handbook useful as a reference.

Handbook of Big Data and IoT Security

This handbook discusses challenges and limitations in existing solutions, and presents state-of-the-art advances from both academia and industry, in big data analytics and digital forensics. The second chapter comprehensively reviews IoT security, privacy, and forensics literature, focusing on IoT and unmanned aerial vehicles (UAVs). The authors propose a deep learning-based approach to process cloud's log data and mitigate enumeration attacks in the third chapter. The fourth chapter proposes a robust fuzzy learning model to protect IT-based infrastructure against advanced persistent threat (APT) campaigns. Advanced and fair clustering approach for industrial data, which is capable of training with huge volume of data in a close to linear time is introduced in the fifth chapter, as well as offering an adaptive deep learning model to detect cyberattacks targeting cyber physical systems (CPS) covered in the sixth chapter. The authors evaluate the performance of unsupervised machine learning for detecting cyberattacks against industrial control systems (ICS) in chapter 7, and the next chapter presents a robust fuzzy Bayesian approach for ICS's cyber threat hunting. This handbook also evaluates the performance of supervised machine learning methods in identifying cyberattacks against CPS. The performance of a scalable clustering algorithm for CPS's cyber threat hunting and the usefulness of machine learning algorithms for MacOS malware detection are respectively evaluated. This handbook continues with evaluating the performance of various machine learning techniques to detect the Internet of Things malware. The authors demonstrate how MacOSX cyberattacks can be detected using state-of-the-art machine learning models. In order to identify credit card frauds, the fifteenth chapter introduces a hybrid model. In the sixteenth chapter, the editors propose a model that leverages natural language processing techniques for generating a mapping between APT-related reports and cyber kill chain. A deep learning-based approach to detect ransomware is introduced, as well as a proposed clustering approach to detect IoT malware in the last two chapters. This handbook primarily targets professionals and scientists working in Big Data, Digital Forensics, Machine Learning, Cyber Security Cyber Threat Analytics and Cyber Threat Hunting as a reference book. Advanced level-students and researchers studying and working in Computer systems, Computer networks and Artificial intelligence will also find this reference useful.

15th EAI International Conference, SecureComm 2019, Orlando, FL, USA, October 23-25, 2019, Proceedings, Part I

This book constitutes the refereed proceedings of 11 symposia and workshops held at the 10th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage, SpaCCS 2017, held in Guangzhou, China, in December 2017. The total of 75 papers presented in this volume was carefully reviewed and selected from a total of 190 submissions to all workshops: UbiSafe 2017: The 9th IEEE International Symposium on UbiSafe Computing ISSR 2017: The 9th IEEE International Workshop on Security in e-Science and e-Research TrustData 2017: The 8th International Workshop on Trust, Security and Privacy for Big Data TSP 2017: The 7th International Symposium on Trust, Security and Privacy for Emerging Applications SPIoT 2017: The 6th International Symposium on Security and Privacy on Internet of Things NOPE 2017: The 5th International Workshop on Network Optimization and Performance Evaluation DependSys 2017: The Third International Symposium on Dependability in Sensor, Cloud, and Big Data Systems and Applications SCS 2017: The Third International Symposium on Sensor-Cloud Systems WCSSC 2017: The Second International Workshop on Cloud Storage Service and Computing MSCF 2017: The First International Symposium on Multimedia Security and Digital Forensics SPBD 2017: The 2017 International Symposium on Big Data and Machine Learning in Information Security, Privacy and Anonymity

SpaCCS 2017 International Workshops, Guangzhou, China, December 12-15, 2017, Proceedings

James J. (Jong Hyuk) Park

Shu-Ching Chen

This book presents the proceedings of the 11th International Conference on Multimedia and Ubiquitous Engineering (MUE2017) and the 12th International Conference on Future Information Technology (FutureTech2017), held in Seoul, South Korea on May 22–24, 2017. These two conferences provided an opportunity for academic and industrial professionals to discuss recent advances in the area of multimedia and ubiquitous environments including models and systems, new directions, and novel applications associated with the utilization and acceptance of ubiquitous computing devices and systems. The resulting papers address the latest technological innovations in the fields of digital convergence, multimedia convergence, intelligent applications, embedded systems, mobile and wireless communications, bio-inspired computing, grid and cloud computing, semantic web, user experience, HCI, and security and trust computing. The book offers a valuable resource for a broad readership, including students, academic researchers, and professionals. Further, it provides an overview of current research and a “snapshot” for those new to the field.

MUE/FutureTech 2017

Reza Parizi

Mohammad Hammoudeh

This book presents the outcomes of the 2019 International Conference on Cyber Security Intelligence and Analytics (CSIA2019), an international conference dedicated to promoting novel theoretical and applied research advances in the interdisciplinary field of cyber security, particularly focusing on threat intelligence, analytics, and countering cyber crime. The conference provides a forum for presenting and discussing innovative ideas, cutting-edge research findings, and novel techniques, methods and applications on all aspects of Cyber Security Intelligence and Analytics.

Cyber Security Intelligence and Analytics

Xiaolu Zhang

In this book, the editors explain how students enrolled in two digital forensic courses at their institution are exposed to experiential learning opportunities, where the students acquire the knowledge and skills of the subject-matter while also learning how to adapt to the ever-changing digital forensic landscape. Their findings (e.g., forensic examination of different IoT devices) are also presented in the book. Digital forensics is a topic of increasing importance as our society becomes “smarter” with more of the “things” around us been internet- and inter-connected (e.g., Internet of Things (IoT) and smart home devices); thus, the increasing likelihood that we will need to acquire data from these things in a forensically sound manner. This book is of interest to both digital forensic educators and digital forensic practitioners, as well as students seeking to learn about digital forensics.

An Experiential Learning Approach

Digital Forensic Education

Darren Quick

This book provides an in-depth understanding of big data challenges to digital forensic investigations, also known as big digital forensic data. It also develops the basis of using data mining in big forensic data analysis, including data reduction, knowledge management, intelligence, and data mining principles to achieve faster analysis in digital forensic investigations. By collecting and assembling a corpus of test data from a range of devices in the real world, it outlines a process of big digital forensic data analysis for evidence and intelligence. It includes the results of experiments on vast volumes of real digital forensic data. The book is a valuable resource for digital forensic practitioners, researchers in big data, cyber threat hunting and intelligence, data mining and other related areas.

Volume 2: Quick Analysis for Evidence and Intelligence

Big Digital Forensic Data

Rafiqul Islam

Jemal Abawajy

This book presents the outcomes of the 2017 International Conference on Applications and Techniques in Cyber Security and Intelligence, which focused on all aspects of techniques and applications in cyber and electronic security and intelligence research. The conference provides a forum for presenting and discussing innovative ideas, cutting-edge research findings, and novel techniques, methods and applications on all aspects of cyber and electronic security and intelligence.

Applications and Techniques in Cyber Security and Intelligence

International Conference on Applications and Techniques in Cyber Security and Intelligence

The book highlights innovative ideas, cutting-edge findings, and novel techniques, methods and applications touching on all aspects of technology and intelligence in smart city management and services. Above all, it explores developments and applications that are of practical use and value for Cyber Intelligence-related methods, which are frequently used in the context of city management and services.

International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018

Gregorio Martinez Perez

Abdulmotaleb El Saddik

Xuejia Lai

This book constitutes the refereed proceedings of the 7th International Conference on Smart City and Informatization, iSCI 2019, held in Guangzhou, China, in November 2019. The volume presents 52 full papers, which were carefully reviewed and selected from 139 submissions. The papers are organized in topical sections on Internet of Things (IoT) and smart sensing; urban computing and big data; smart society informatization technologies; cloud/edge/fog computing for smart city; applications for smart city informatization; assistive engineering and information technology; cyberspace security; blockchain and applications.

Kim-Kwang Raymond Choo

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