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010 a| 2022035672;

020 a| 9781119884163q| (epub);

020 a| 9781119884156q| (adobe pdf);

020 z| 9781119884149q| (hardback);

035 a| (WaSeSS)ssj0002713657;

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050 00 a| TK5105.8857;

082 00 a| 004.67/82| 23/eng/20220923;

100 1 a| Abdel-Basset, Mohamed,d| 1985-?| UNAUTHORIZED;

245 10 a| Deep learning approaches for security threats in IoT environmentsh| [electronic resource] /c| Mohamed Abdel-Basset, Zagazig University, Egypt, Nour Moustafa, UNSW Canberra at the Australian Defence Force Academy, Australia, Hossam Hawash, Zagazig University, Egypt.;

250 a| First edition.;

260 a| Hoboken, New Jersey :b| John Wiley & Sons, Inc.,c| [2023];

263 a| 2211;

300 a| 1 online resource;

504 a| Includes bibliographical references and index.;

506 a| Available only to authorized users.;

520 a| "Deep Learning Approaches for Security Threats in IoT Environments discusses approaches and measures to ensure our IoT systems are secure. This book discusses important concepts of AI and IoT and applies vital approaches that can be used to protect our systems - these include supervised, unsupervised, and semi-supervised Deep Learning approaches as well as Reinforcement and Federated Learning for privacy-preserving. This book applies Digital Forensics to IoT and discusses problems that professionals may encounter when working in the field of IoT forensics, providing ways in which smart devices can solve cyber security issues. Aimed at readers within the cyber security field, this book presents the most recent challenges that are faced in deep learning when creating a secure platform for IoT systems and addresses the possible solutions, paving the way for a more secure future"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

588 a| Description based on print version record and CIP data provided by publisher; resource not viewed.;

650 0 a| Internet of thingsx| Security measuresx| Data processing.=| ^A1157117;

650 0 a| Deep learning (Machine learning)=| ^A1458889;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Moustafa, Nour.?| UNAUTHORIZED;

700 1 a| Hawash, Hossam.?| UNAUTHORIZED;

856 40 z| Full text available from eBooks on EBSCOhostu| https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3460934&scope=site&custid=s5822723&authtype=ip,shib;

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Deep learning approaches for security threats in IoT environments / Mohamed Abdel-Basset, Zagazig University, Egypt, Nour Moustafa, UNSW Canberra at the Australian Defence Force Academy, Australia, Hossam Hawash, Zagazig University, Egypt.

Author/creator	Abdel-Basset, Mohamed, 1985-
Other author	Moustafa, Nour.
Other author	Hawash, Hossam.
Format	Electronic
Edition	First edition.
Publication Info	Hoboken, New Jersey : John Wiley & Sons, Inc., [2023]
Description	1 online resource
Supplemental Content	Full text available from eBooks on EBSCOhost
Subjects	Internet of things--Security measures. --Data processing. Deep learning (Machine learning)

Abstract	"Deep Learning Approaches for Security Threats in IoT Environments discusses approaches and measures to ensure our IoT systems are secure. This book discusses important concepts of AI and IoT and applies vital approaches that can be used to protect our systems - these include supervised, unsupervised, and semi-supervised Deep Learning approaches as well as Reinforcement and Federated Learning for privacy-preserving. This book applies Digital Forensics to IoT and discusses problems that professionals may encounter when working in the field of IoT forensics, providing ways in which smart devices can solve cyber security issues. Aimed at readers within the cyber security field, this book presents the most recent challenges that are faced in deep learning when creating a secure platform for IoT systems and addresses the possible solutions, paving the way for a more secure future"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Source of description	Description based on print version record and CIP data provided by publisher; resource not viewed.
Issued in other form	Print version: Abdel-Basset, Mohamed, 1985- Deep learning approaches for security threats in IoT environments First edition. Hoboken, New Jersey : John Wiley & Sons, Inc., [2023] 9781119884149
Genre/form	Electronic books.
LCCN	2022035672
ISBN	9781119884163 (epub)
ISBN	9781119884156 (adobe pdf)
ISBN	(hardback)

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Deep learning approaches for security threats in IoT environments / Mohamed Abdel-Basset, Zagazig University, Egypt, Nour Moustafa, UNSW Canberra at the Australian Defence Force Academy, Australia, Hossam Hawash, Zagazig University, Egypt.

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