000 | 04074nam a22006015i 4500 | ||
---|---|---|---|
001 | 978-3-030-13057-2 | ||
003 | DE-He213 | ||
005 | 20240423125209.0 | ||
007 | cr nn 008mamaa | ||
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 |