جهت دسترسی به کاربرگه ی زیر، از این لینک استفاده کنید. http://localhost/handle/Hannan/835
عنوان: Network Intrusion Detection using Deep Learning
عنوان دیگر: A Feature Learning Approach /
پدیدآورنده: Kim, Kwangjo. ;;Aminanto, Muhamad Erza. ;;Tanuwidjaja, Harry Chandra. ;
کلید واژه ها: Data protection. ;;Artificial Intelligence;Wireless communication systems. ;;Mobile communication systems. ;;Big data. ;;Data Mining;Security. ; http://scigraph.springernature.com/things/product-market-codes/I28000. ;;Artificial Intelligence and Robotics;Systems and Data Security. ; http://scigraph.springernature.com/things/product-market-codes/I14050. ;;Wireless and Mobile Communication. ; http://scigraph.springernature.com/things/product-market-codes/T24100. ;;Big Data. ; http://scigraph.springernature.com/things/product-market-codes/I29120. ;;Data Mining and Knowledge Discovery. ; http://scigraph.springernature.com/things/product-market-codes/I18030. ;
تاریخ انتشار: 2018
محل نشر: Singapore :
ناشر: Springer Singapore :
Imprint: Springer,
فروست / شماره : SpringerBriefs on Cyber Security Systems and Networks, ; 2522-5561. ;
SpringerBriefs on Cyber Security Systems and Networks, ; 2522-5561. ;
چکیده: This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity. ;
توضیحات : SpringerLink (Online service) ;


Printed edition: ; 9789811314438. ;
QA76.9.A25 ;
005.8 ; 23 ;

Printed edition: ; 9789811314452. ;
آدرس: http://localhost/handle/Hannan/835
شابک : 9789811314445 ;
9789811314438 (print) ;
9789811314452 (print) ;
اطلاعات بیشتر: XVII, 79 p. 30 illus., 11 illus. in color. ; online resource. ;
مجموعه(های):مدیریت فناوری اطلاعات

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
9789811314438.pdf2.11 MBAdobe PDFتصویر
مشاهده فایل
عنوان: Network Intrusion Detection using Deep Learning
عنوان دیگر: A Feature Learning Approach /
پدیدآورنده: Kim, Kwangjo. ;;Aminanto, Muhamad Erza. ;;Tanuwidjaja, Harry Chandra. ;
کلید واژه ها: Data protection. ;;Artificial Intelligence;Wireless communication systems. ;;Mobile communication systems. ;;Big data. ;;Data Mining;Security. ; http://scigraph.springernature.com/things/product-market-codes/I28000. ;;Artificial Intelligence and Robotics;Systems and Data Security. ; http://scigraph.springernature.com/things/product-market-codes/I14050. ;;Wireless and Mobile Communication. ; http://scigraph.springernature.com/things/product-market-codes/T24100. ;;Big Data. ; http://scigraph.springernature.com/things/product-market-codes/I29120. ;;Data Mining and Knowledge Discovery. ; http://scigraph.springernature.com/things/product-market-codes/I18030. ;
تاریخ انتشار: 2018
محل نشر: Singapore :
ناشر: Springer Singapore :
Imprint: Springer,
فروست / شماره : SpringerBriefs on Cyber Security Systems and Networks, ; 2522-5561. ;
SpringerBriefs on Cyber Security Systems and Networks, ; 2522-5561. ;
چکیده: This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity. ;
توضیحات : SpringerLink (Online service) ;


Printed edition: ; 9789811314438. ;
QA76.9.A25 ;
005.8 ; 23 ;

Printed edition: ; 9789811314452. ;
آدرس: http://localhost/handle/Hannan/835
شابک : 9789811314445 ;
9789811314438 (print) ;
9789811314452 (print) ;
اطلاعات بیشتر: XVII, 79 p. 30 illus., 11 illus. in color. ; online resource. ;
مجموعه(های):مدیریت فناوری اطلاعات

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
9789811314438.pdf2.11 MBAdobe PDFتصویر
مشاهده فایل
عنوان: Network Intrusion Detection using Deep Learning
عنوان دیگر: A Feature Learning Approach /
پدیدآورنده: Kim, Kwangjo. ;;Aminanto, Muhamad Erza. ;;Tanuwidjaja, Harry Chandra. ;
کلید واژه ها: Data protection. ;;Artificial Intelligence;Wireless communication systems. ;;Mobile communication systems. ;;Big data. ;;Data Mining;Security. ; http://scigraph.springernature.com/things/product-market-codes/I28000. ;;Artificial Intelligence and Robotics;Systems and Data Security. ; http://scigraph.springernature.com/things/product-market-codes/I14050. ;;Wireless and Mobile Communication. ; http://scigraph.springernature.com/things/product-market-codes/T24100. ;;Big Data. ; http://scigraph.springernature.com/things/product-market-codes/I29120. ;;Data Mining and Knowledge Discovery. ; http://scigraph.springernature.com/things/product-market-codes/I18030. ;
تاریخ انتشار: 2018
محل نشر: Singapore :
ناشر: Springer Singapore :
Imprint: Springer,
فروست / شماره : SpringerBriefs on Cyber Security Systems and Networks, ; 2522-5561. ;
SpringerBriefs on Cyber Security Systems and Networks, ; 2522-5561. ;
چکیده: This book presents recent advances in intrusion detection systems (IDSs) using state-of-the-art deep learning methods. It also provides a systematic overview of classical machine learning and the latest developments in deep learning. In particular, it discusses deep learning applications in IDSs in different classes: generative, discriminative, and adversarial networks. Moreover, it compares various deep learning-based IDSs based on benchmarking datasets. The book also proposes two novel feature learning models: deep feature extraction and selection (D-FES) and fully unsupervised IDS. Further challenges and research directions are presented at the end of the book. Offering a comprehensive overview of deep learning-based IDS, the book is a valuable reerence resource for undergraduate and graduate students, as well as researchers and practitioners interested in deep learning and intrusion detection. Further, the comparison of various deep-learning applications helps readers gain a basic understanding of machine learning, and inspires applications in IDS and other related areas in cybersecurity. ;
توضیحات : SpringerLink (Online service) ;


Printed edition: ; 9789811314438. ;
QA76.9.A25 ;
005.8 ; 23 ;

Printed edition: ; 9789811314452. ;
آدرس: http://localhost/handle/Hannan/835
شابک : 9789811314445 ;
9789811314438 (print) ;
9789811314452 (print) ;
اطلاعات بیشتر: XVII, 79 p. 30 illus., 11 illus. in color. ; online resource. ;
مجموعه(های):مدیریت فناوری اطلاعات

پیوست های این کاربرگه
فایل توضیحات اندازهفرمت  
9789811314438.pdf2.11 MBAdobe PDFتصویر
مشاهده فایل