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TitleHandbook of Big Data and IoT Security [electronic resource] / edited by Ali Dehghantanha, Kim-Kwang Raymond Choo
ImprintCham : Springer International Publishing : Imprint: Springer, 2019
Edition 1st ed. 2019
Connect tohttps://doi.org/10.1007/978-3-030-10543-3
Descript IX, 384 p. 164 illus., 129 illus. in color. online resource

SUMMARY

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


CONTENT

1 Big Data and Internet of Things Security and Forensics: Challenges and Opportunities -- 2 Privacy of Big Data - a Review -- 3 A Bibliometric Analysis of Authentication and Access Control in IoT Devices -- 4 Towards Indeterminacy - Tolerant Access Control in IoT -- 5 Private Cloud Storage Forensics: Seafile as a Case Study -- 6 Distributed Filesystem Forensics: Ceph as a Case Study -- 7 Forensic Investigation of Cross Platform Massively Multiplayer Online Games: Minecraft as a Case Study -- 8 Big Data Forensics: Hadoop Distributed File Systems as a Case Study -- 9 Internet of Things Camera Identi cation Algorithm Based on Sensor Pattern Noise Using Color Filter Array and Wavelet Transform -- 10 Protecting IoT and ICS Platforms Against Advanced Persistent Threat Actors: Analysis of APT1, Silent Chollima and Molerats -- 11 Analysis of APT Actors Targeting IoT and Big Data Systems: Shell_Crew, NetTraveler, ProjectSauron, CopyKittens, Volatile Cedar and Transparent Tribe as a Case Study -- 12 A Cyber Kill Chain Based Analysis of Remote Access Trojans -- 13 Evaluation and Application of Two Fuzzing Approaches for Security Testing of IoT Applications -- 14 Bibliometric Analysis on the Rise of Cloud Security -- 15 A Bibliometric Analysis of Botnet Detection Techniques -- 16 Security in Online Games: Current Implementations and Challenges.


Data protection Information systems Artificial intelligence Security. http://scigraph.springernature.com/things/product-market-codes/I28000 Information Systems and Communication Service. http://scigraph.springernature.com/things/product-market-codes/I18008 Artificial Intelligence. http://scigraph.springernature.com/things/product-market-codes/I21000



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