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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]
Subjects:
Computer security > Data processing
Malware (Computer software)
Big data
Computer crimes > investigation
Computer networks > access control
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Table of Contents:
  • 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

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