Big data analytics with applications in insider threat detection

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...

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Bibliographic Details
Main Authors: Thuraisingham, Bhavani (Author), Parveen, Pallabi (Author), Masud, Mohammad Mehedy (Author), Khan, Latifur (Author)
Format: Book
Language:English
Published: Boca Raton, FL CRC Press [2018]
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020 |a 9781498705479 
020 |z 9781315119458 (electronic bk.) 
020 |z 9781498705486 (electronic bk.) 
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040 |a UPNM  |b eng  |c UPNM  |e rda 
090 |a QA 76.9.A25  |b P384 2018 
100 1 |a Thuraisingham, Bhavani  |e author 
245 1 0 |a Big data analytics with applications in insider threat detection  |c Bhavani Thuraisingham, Mohammad Mehedy Masud, Pallabi Parveen, Latifur Khan 
264 1 |a Boca Raton, FL  |b CRC Press  |c [2018] 
264 4 |c ©2018 
300 |a xxxiv, 543 pages  |b illustrations  |c 26 cm 
336 |a text  |2 rdacontent 
337 |a unmediated  |2 rdamedia 
338 |a volume  |2 rdacarrier 
504 |a Includes bibliographical references and index 
505 0 |a Introduction -- Data security and privacy -- Data mining techniques -- Data mining for security applications -- Cloud computing and semantic web technologies -- Data mining and insider threat detection -- Big data management and analytics technologies -- Challenges for stream data classification -- Survey of stream data classification -- A multi-partition, multi-chunk ensemble for classifying concept-drifting data streams -- Classification and novel class detection in concept-drifting data streams -- Data stream classification with limited labeled training data -- Directions in data stream classification -- Insider threat detection as a stream mining problem -- Survey of insider threat and stream mining -- Ensemble-based insider threat detection -- Details of learning classes -- Experiments and results for nonsequence data -- Insider threat detection for sequence data -- Experiments and results for sequence data -- Scalability using big data technologies -- Stream mining and big data for insider threat detection -- Cloud query processing system for big data management -- Big data analytics for multipurpose social media applications -- Big data management and cloud for assured information sharing -- Big data management for secure information integration -- Big data analytics for malware detection -- A semantic web-based inference controller for provenance big data -- Confidentiality, privacy, and trust for big data systems -- Unified framework for secure big data management and analytics -- Big data, security, and the internet of things -- Big data analytics for malware detection in smartphones -- Toward a case study in healthcare for big data analytics and security -- Toward an experimental infrastructure and education program for BDMA and BDSP -- Directions for BDSP and BDMA -- Summary and directions 
520 |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. 
592 |a 37583  |b 1/8/19  |c RM448.63  |h Bookline Services 
650 0 |a Computer security  |x Data processing 
650 0 |a Malware (Computer software) 
650 0 |a Big data 
650 0 |a Computer crimes  |x investigation 
650 0 |a Computer networks  |x access control 
700 1 |a Parveen, Pallabi  |e author 
700 1 |a Masud, Mohammad Mehedy  |e author 
700 1 |a Khan, Latifur  |e author 
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