Big Data in Reliability and Security: Applications

By Saurabh Bagchi

Electrical and Computer Engineering, Purdue University, West Lafayette, IN

Published on

Bio

Saurabh Bagchi Saurabh Bagchi is a Professor in the School of Electrical and Computer Engineering and the Department of Computer Science (by courtesy) at Purdue University. His founding Director of a university-wide resilience center CRISP, started in 2017. He received the Alexander von Humboldt Research Award (2018), the Adobe Faculty Award (2017), the AT&T Labs VURI Award (2016), the Google Faculty Award (2015), and the IBM Faculty Award (2014). He is an ACM Distinguished Scientist (2013), a Senior Member of IEEE (2007) and of ACM (2009), and a Distinguished Speaker for ACM (2012). He has served on the IEEE Computer Society Board of Governors for the 2017-19 term. Prof. Bagchi's research interests are in distributed systems and dependable computing.

Prof. Bagchi received MS and PhD degrees from the University of Illinois, Urbana-Champaign in the Computer Science department, in 1998 and 2001, respectively. Where he worked with Prof. Ravishankar Iyer and Dr. Zbigniew Kalbarczyk. His Ph.D. dissertation was on error detection protocols in distributed systems. His undergraduate degree is from the Indian Institute of Technology at Kharagpur in Computer Science and Engineering. In 2001, he worked at the IBM T. J. Watson Research Center in Hawthorne, New York in the Distributed Messaging Systems group on a project called Gryphon.

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Researchers should cite this work as follows:

  • Saurabh Bagchi (2019), "Big Data in Reliability and Security: Applications," https://nanohub.org/resources/30676.

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Location

Burton Morgan, Room 121, Purdue University, West Lafayette, IN

Tags

Big Data in Reliability and Security: Applications
  • Big Data in Reliability and Security: Applications 1. Big Data in Reliability and Se… 0
    00:00/00:00
  • Four Relevant Aspects 2. Four Relevant Aspects 15.849182515849183
    00:00/00:00
  • Reliability Principles in order to Use Big Data 3. Reliability Principles in orde… 47.981314647981314
    00:00/00:00
  • Big Data Helping in Reliable Operation of Systems 4. Big Data Helping in Reliable O… 204.10410410410412
    00:00/00:00
  • Analyzing Job Failures on Compute Clusters 5. Analyzing Job Failures on Comp… 358.05805805805807
    00:00/00:00
  • Effect of Resource Usages on Job Failures 6. Effect of Resource Usages on J… 520.05338672005337
    00:00/00:00
  • Effect of Memory Usage 7. Effect of Memory Usage 706.74007340674007
    00:00/00:00
  • Local IO 8. Local IO 896.7634300967635
    00:00/00:00
  • Network File System 9. Network File System 1190.6573239906575
    00:00/00:00
  • Job Execution Time 10. Job Execution Time 1213.8805472138806
    00:00/00:00
  • Big Data Helping in Secure Operation of Systems 11. Big Data Helping in Secure Ope… 1514.8148148148148
    00:00/00:00
  • Features Used in Security Classification 12. Features Used in Security Clas… 1516.016016016016
    00:00/00:00
  • Early Days 13. Early Days 1517.1171171171172
    00:00/00:00
  • Bayesian Network 14. Bayesian Network 1523.4234234234234
    00:00/00:00
  • Use of BN for Sensor Placement 15. Use of BN for Sensor Placement 1587.6876876876877
    00:00/00:00
  • From attack graph to a Bayesian Network 16. From attack graph to a Bayesia… 1638.8722055388723
    00:00/00:00
  • Application to be Protected 17. Application to be Protected 1647.7143810477144
    00:00/00:00
  • Problem #7 18. Problem #7 1786.4864864864865
    00:00/00:00
  • Proposed Method 19. Proposed Method 1957.9579579579581
    00:00/00:00
  • Experimental Results 20. Experimental Results 1964.4978311644979
    00:00/00:00
  • Conclusions 21. Conclusions 2019.9866533199868
    00:00/00:00
  • Decision Trees 22. Decision Trees 2021.4214214214214
    00:00/00:00
  • Support Vector Machine 23. Support Vector Machine 2022.9562896229563
    00:00/00:00
  • Considerations for ML in Security 24. Considerations for ML in Secur… 2024.3910577243912
    00:00/00:00
  • Adversarial ML 25. Adversarial ML 2026.2929596262929
    00:00/00:00
  • Adversarial ML 26. Adversarial ML 2028.0280280280281
    00:00/00:00