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Front Cover -- Contemporary Digital Forensic Investigations of Cloud and Mobile Applications -- Copyright -- Contents -- Contributors -- About the Editors -- Chapter 1 Contemporary Digital Forensics Investigations of Cloud and Mobile Applications -- References -- Chapter 2 Forensics Analysis of Android Mobile VoIP Apps -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 3.1 Phase I: Setup Phase-First Iteration -- 3.2 Phase II: Logical Acquisition -- 3.3 Phase III: Identification and Analysis-Second Iteration -- 4 Results and Discussion -- 4.1 Viber Artifacts -- 4.2 Skype Artifacts

Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- Abbreviations -- Keywords -- 1 Introduction -- References -- 2 Background and Literature Review -- 2.1 Background -- 2.2 Volume of Data -- 2.3 Growth of Media -- 2.4 Processing Time -- 2.5 Proposed Solutions -- 2.5.1 Data Mining -- 2.5.2 Data Reduction and Subsets -- 2.5.3 Triage -- 2.6 Other Proposed Solutions to the Data Volume Challenge -- 2.7 Discussion -- 2.8 Summary -- References -- 3 Data Reduction and Data Mining Frame-Work -- 3.1 Motivation -- 3.2 Proposed Digital Forensic Data Reduction and Data Mining Framework -- 3.3 Pilot Study Preliminary Findings -- 3.4 Discussion -- 3.5 Summary -- References -- 4 Digital Forensic Data Reduction by Selective Imaging -- 4.1 Digital Forensic Data Reduction by Selective Imaging -- 4.1.1 Load or Mount Forensic Image or Connect Physical Device -- 4.1.2 Processing Options -- 4.1.3 Filters for Subset Files -- 4.1.4 File System Filters -- 4.1.5 Operating System Filters -- 4.1.6 Software and Applications Filters -- 4.1.7 User Data Filters -- 4.1.8 Review of Large Files -- 4.1.9 Excluded File Types and Overwritten Files and Data -- 4.1.10 File and Data List (Spreadsheet) Report -- 4.1.11 Video Thumbnails Method -- 4.1.12 Picture Dimension Reduction -- 4.1.13 Preservation -- 4.2 Test Data Application and Results -- 4.3 Application to Real World Digital Forensic Case Data -- 4.3.1 Real World Data Subset Reduction -- 4.3.2 Real World Data-Video Thumbnailing -- 4.3.3 Real World Data-Case Examples and Post Case Analysis -- 4.3.4 Real World Data-Cross Case Analysis -- 4.3.5 Real World Data-Failing Hard Disk Drives -- 4.4 Discussion -- 4.5 Summary -- References -- 5 Summary of the Framework and DRbSI -- 5.1 Conclusion -- References.

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 data reduction, and evidence and intelligence extraction methods. Further, it includes the experimental results 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.

Contemporary Digital Forensic Investigations of Cloud and Mobile Applications comprehensively discusses the implications of cloud (storage) services and mobile applications on digital forensic investigations. The book provides both digital forensic practitioners and researchers with an up-to-date and advanced knowledge of collecting and preserving electronic evidence from different types of cloud services, such as digital remnants of cloud applications accessed through mobile devices. This is the first book that covers the investigation of a wide range of cloud services. Dr. Kim-Kwang Raymond Choo and Dr. Ali Dehghantanha are leading researchers in cloud and mobile security and forensics, having organized research, led research, and been published widely in the field. Users will gain a deep overview of seminal research in the field while also identifying prospective future research topics and open challenges

Is Blockchain a hype or a trend that is here to stay? Based on the increasing interest of blockchain in industry, governments and academia, one might tend to be inclined to think that blockchain has moved beyond a hype and a buzzword to something that is here to stay, somewhat analogous to cloud computing and Internet of Things (IoT) in their early days. While there are many (potential) applications of blockchain including in industrial IoT (IIoT), there are a number of ongoing challenges. In this special issue, we present existing state-of-the-art advances reported by the nine accepted papers. We then conclude the special issue with a number of potential research agenda.

Intro -- Preface -- Organization -- Steering Committee -- Advisory -- Honorary Chairs -- General Chairs -- Workshop and Special Session Chairs -- Technical Programme Committee Chairs -- Technical Programme Committee -- Proceeding Editors -- Publicity Chairs -- National Organizing Committee Chairs -- Community Chairs -- Contents -- The Relevance of Nature-Inspired Metaheuristic Algorithms in Smart Sport Training -- 1 Introduction -- 2 Nature-Inspired Metaheuristic Algorithms -- 3 Smart Sport Training -- 3.1 Applications of Nature-Inspired Metaheuristics in Smart Sport Training -- 4 Transition of Knowledge from Research to the Real-World -- 5 Conclusion -- References -- Analysis of Variable Learning Rate Back Propagation with Cuckoo Search Algorithm for Data Classification -- 1 Introduction -- 2 Learning Algorithms -- 2.1 Back Propagation Algorithm -- 2.2 Learning Rate -- 2.3 Variable Learning Rate -- 2.4 Cuckoo-Search (CS) -- 3 The Proposed CSVLRBP Algorithm -- 4 Result and Discussions -- 4.1 Preliminaries -- 4.2 Wisconsin Breast Cancer Classification Problem -- 4.3 Diabetes Classification Problem -- 5 Conclusion -- References -- A Metaheuristic Based Virtual Machine Allocation Technique Using Whale Optimization Algorithm in Cloud -- 1 Introduction -- 2 Background and Related Works -- 3 Problem Formulation of VM Scheduling -- 3.1 VM Model -- 3.2 The Expression of Load -- 3.3 Performance Metrics -- 4 Improved Whale Optimization Algorithm (IWOA) -- 4.1 Population Initialization -- 4.2 Encircling Prey -- 4.3 Bubble-Net Attacking Method (Exploitation Phase) -- 4.4 Search for Prey (Exploration Phase) -- 5 Experimental Setup -- 5.1 First Scenario -- 5.2 Second Scenario -- 5.3 Result and Discussion -- 5.4 Statistical Analysis -- 6 Conclusion and Future Work -- References -- Data Sampling-Based Feature Selection Framework for Software Defect Prediction.

AbstractDuring emergency situations, a quick and concise summary from the deluge of messages improves "situation awareness" and enables informed decisions. However, this is challenging due to the volume, variety, and veracity of information. Grounded in the Situation Awareness Theory, this study presents a streamlined protocol to process millions of social media messages and render them in an easily comprehensible format for various stakeholders to gain deeper insights. Specifically, using a design science approach, we develop a set of artifacts using incremental hierarchical clustering and enhanced text summarization algorithm to produce informative summaries under crisis situations. We implemented our protocol on 2.5 and 3 million tweets collected during the two major hurricanes in 2017 and 2018. The results show that our protocol can derive critical information not captured by Twitter's search tools and mainstream news media and significantly improve on accuracy and efficiency when compared to other contemporary tools.

To reduce the risk of digital forensic evidence being called into question in judicial proceedings, it is important to have a rigorous methodology and set of procedures for conducting digital forensic investigations and examinations. Digital forensic investigation in the cloud computing environment, however, is in infancy due to the comparatively recent prevalence of cloud computing. Cloud Storage Forensics presents the first evidence-based cloud forensic framework. Using three popular cloud storage services and one private cloud storage service as case studies, the authors show you how their framework can be used to undertake research into the data remnants on both cloud storage servers and client devices when a user undertakes a variety of methods to store, upload, and access data in the cloud. By determining the data remnants on client devices, you gain a better understanding of the types of terrestrial artifacts that are likely to remain at the Identification stage of an investigation. Once it is determined that a cloud storage service account has potential evidence of relevance to an investigation, you can communicate this to legal liaison points within service providers to enable them to respond and secure evidence in a timely manner. Learn to use the methodology and tools from the first evidenced-based cloud forensic framework Case studies provide detailed tools for analysis of cloud storage devices using popular cloud storage services Includes coverage of the legal implications of cloud storage forensic investigations Discussion of the future evolution of cloud storage and its impact on digital forensics