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008 190814s2019 sz | s |||| 0|eng d
020 _a9783030130572
_9978-3-030-13057-2
024 7 _a10.1007/978-3-030-13057-2
_2doi
050 4 _aQA76.9.B45
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a005.7
_223
245 1 0 _aDeep Learning Applications for Cyber Security
_h[electronic resource] /
_cedited by Mamoun Alazab, MingJian Tang.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXX, 246 p. 78 illus., 54 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvanced Sciences and Technologies for Security Applications,
_x2363-9466
505 0 _aAdversarial Attack, Defense, and Applications with Deep Learning Frameworks -- Intelligent Situational-Awareness Architecture for Hybrid Emergency Power Systems in More Electric Aircraft -- Deep Learning in Person Re-identication for Cyber-Physical Surveillance Systems -- Deep Learning-based Detection of Electricity Theft Cyber-attacks in Smart Grid AMI Networks -- Using Convolutional Neural Networks for Classifying Malicious Network Traffic -- DBD: Deep Learning DGA-based Botnet Detection -- Enhanced Domain Generating Algorithm Detection Based on Deep Neural Networks -- Intrusion Detection in SDN-based Networks: Deep Recurrent Neural Network Approach -- SeqDroid: Obfuscated Android Malware Detection using Stacked Convolutional and Recurrent Neural Networks -- Forensic Detection of Child Exploitation Material using Deep Learning -- Toward Detection of Child Exploitation Material: A Forensic Approach.
520 _aCybercrime remains a growing challenge in terms of security and privacy practices. Working together, deep learning and cyber security experts have recently made significant advances in the fields of intrusion detection, malicious code analysis and forensic identification. This book addresses questions of how deep learning methods can be used to advance cyber security objectives, including detection, modeling, monitoring and analysis of as well as defense against various threats to sensitive data and security systems. Filling an important gap between deep learning and cyber security communities, it discusses topics covering a wide range of modern and practical deep learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security. The book focuses on mature and proven techniques, and provides ample examples to help readers grasp the key points. .
650 0 _aBig data.
650 0 _aComputer crimes.
650 0 _aNeural networks (Computer science) .
650 0 _aData protection.
650 0 _aSecurity systems.
650 1 4 _aBig Data.
650 2 4 _aCybercrime.
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
650 2 4 _aData and Information Security.
650 2 4 _aSecurity Science and Technology.
700 1 _aAlazab, Mamoun.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aTang, MingJian.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030130565
776 0 8 _iPrinted edition:
_z9783030130589
776 0 8 _iPrinted edition:
_z9783030130596
830 0 _aAdvanced Sciences and Technologies for Security Applications,
_x2363-9466
856 4 0 _uhttps://doi.org/10.1007/978-3-030-13057-2
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c175315
_d175315