Big Data Analytics with Applications in Insider Threat Detection
Auteurs : Thuraisingham Bhavani, Parveen Pallabi, Masud Mohammad Mehedy, Khan Latifur
Supporting Technologies. Introduction. Data Mining Techniques. Cyber Security and Malware. Data Mining for Malware Detection. Conclusion. Stream-Based Novel Class Detection. Stream Mining. Novel Class Detection Problem. SNOD. Conclusion. Reactively Adaptive Malware. Reactively Adaptive Malware. RAMAL Design. RAMAL Implementation. SNODMAL. Introduction. SNODMAL Design. SNODMAL Implementation. SNODMAL FOR RAMAL. SNODMAL Extensions. Introduction. SNODMAL on the Cloud. SNODCAL. SNODMAL++. Conclusion. Summary and Directions. References. Appendix A: Data Management Systems. Appendix B: Malware Products.
Dr. Bhavani Thuraisingham is the Louis A. Beecherl, Jr. Distinguished Professor of Computer Science and the Executive Director of the Cyber Security Research and Education Institute (CSI) at the University of Texas at Dallas.
Dr. Kevin W. Hamlen is an Assistant Professor in CS at UTD where he directs the Software Security Lab.
Dr. Latifur R. Khan is currently an Associate Professor in CS at UTD.
Dr. Mehedy Masud is an associate professor at the College of Information Technology, United Arab Emirates University.
Date de parution : 09-2020
17.8x25.4 cm
Date de parution : 01-2018
17.8x25.4 cm
Thèmes de Big Data Analytics with Applications in Insider Threat... :
Mots-clés :
Insider Threat Detection; Concept Drift; concept; Data Chunks; drift; Data Stream Classification; MapReduce Jobs; Cloud Storage; Semantic Web Technologies; Big Data Systems; RDF Graph; Big Data; semantic; Big Data Management; web; RDF Data; Mohammad Mehedy Masud; Insider Threat; Pallabi Parveen; RDF Triple; Latifur Khan; SPARQL Query; Inference Controller; QD; Classifying Concept Drifting Data Streams; Data Stream Mining; Data Stream Classification Problem; Malware Detection; Semantic Web; Class Detection Technique; Data Streams; Chunk Size