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|a 1351645765
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|a 9781498705479
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|a 1315119455
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|a QA76.9.A25
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| 100 |
1 |
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|a Thuraisingham, Bhavani M.
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|a Big data analytics with applications in insider threat detection
|c Bhavani Thuraisingham, Mohammad Mehedy Masud, Pallabi Parveen, Latifur Khan
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| 260 |
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|a Boca Raton, FL
|b CRC Press
|c 2018
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| 300 |
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|a 1 volume
|b illustrations
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|a part PART III Stream Data Analytics for Insider Threat Detection -- chapter Introduction to Part III -- chapter 14 Insider Threat Detection as a Stream Mining Problem -- chapter 15 Survey of Insider Threat and Stream Mining -- chapter 16 Ensemble-Based Insider Threat Detection -- chapter 17 Details of Learning Classes -- chapter 18 Experiments and Results for Nonsequence Data -- chapter 19 Insider Threat Detection for Sequence Data -- chapter 20 Experiments and Results for Sequence Data -- chapter 21 Scalability Using Big Data Technologies -- chapter 22 Stream Mining and Big Data for Insider Threat Detection -- chapter Conclusion to Part III -- part PART IV Experimental BDMA and BDSP Systems -- chapter Introduction to Part IV -- chapter 23 Cloud Query Processing System for Big Data Management -- chapter 24 Big Data Analytics for Multipurpose Social Media Applications -- chapter 25 Big Data Management and Cloud for Assured Information Sharing --
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|a Includes bibliographical references and index
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|a Chapter 1 Introduction -- part PART I Supporting Technologies for BDMA and BDSP -- chapter Introduction to Part I -- chapter 2 Data Security and Privacy -- chapter 3 Data Mining Techniques -- chapter 4 Data Mining for Security Applications -- chapter 5 Cloud Computing and Semantic Web Technologies -- chapter 6 Data Mining and Insider Threat Detection -- chapter 7 Big Data Management and Analytics Technologies -- chapter Conclusion to Part I -- part PART II Stream Data Analytics -- chapter Introduction to Part II -- chapter 8 Challenges for Stream Data Classification -- chapter 9 Survey of Stream Data Classification -- chapter 10 A Multi-Partition, Multi-Chunk Ensemble for Classifying Concept-Drifting Data Streams -- chapter 11 Classification and Novel Class Detection in Concept-Drifting Data Streams -- chapter 12 Data Stream Classification with Limited Labeled Training Data -- chapter 13 Directions in Data Stream Classification -- chapter Conclusion to Part II --
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|a chapter 26 Big Data Management for Secure Information Integration -- chapter 27 Big Data Analytics for Malware Detection -- chapter 28 A Semantic Web-Based Inference Controller for Provenance Big Data -- part PART V Next Steps for BDMA and BDSP -- chapter Introduction to Part V -- chapter 29 Confidentiality, Privacy, and Trust for Big Data Systems -- chapter 30 Unified Framework for Secure Big Data Management and Analytics -- chapter 31 Big Data, Security, and the Internet of Things -- chapter 32 Big Data Analytics for Malware Detection in Smartphones -- chapter 33 Toward a Case Study in Healthcare for Big Data Analytics and Security -- chapter 34 Toward an Experimental Infrastructure and Education Program for BDMA and BDSP -- chapter 35 Directions for BDSP and BDMA -- chapter Conclusion to Part V -- chapter 36 Summary and Directions
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|a Computer networks / Access control / http://id.loc.gov/authorities/subjects/sh2007008424
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|a Big data / fast
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|a Big data / http://id.loc.gov/authorities/subjects/sh2012003227
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|a Logiciels malveillants
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|a Malware (Computer software) / http://id.loc.gov/authorities/subjects/sh2009005103
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|a Criminalité informatique / Enquêtes
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|a Données volumineuses
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|a Computer crimes / Investigation / fast
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|a Computer security / Data processing
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|a Sécurité informatique / Informatique
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|a Computer crimes / Investigation / http://id.loc.gov/authorities/subjects/sh85029493
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|a Malware (Computer software) / fast
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|a Computer networks / Access control / fast
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|a Masud, Mehedy
|e author
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|a Parveen, Pallabi
|e author
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|a Khan, Latifur
|e author
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|a eng
|2 ISO 639-2
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 9781315119458
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| 776 |
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|z 9781498705486
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| 776 |
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|u https://learning.oreilly.com/library/view/~/9781351645768/?ar
|x Verlag
|3 Volltext
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|a 331
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|a 364.1
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|a 005.8
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|a Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection
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