Web: http://arxiv.org/abs/2211.01827

Nov. 22, 2022, 2:20 a.m. | Ioannis Mavromatis, Aftab Khan

cs.CR updates on arXiv.org arxiv.org

This paper presents LE3D; a novel data drift detection framework for
preserving data integrity and confidentiality. LE3D is a generalisable platform
for evaluating novel drift detection mechanisms within the Internet of Things
(IoT) sensor deployments. Our framework operates in a distributed manner,
preserving data privacy while still being adaptable to new sensors with minimal
online reconfiguration. Our framework currently supports multiple drift
estimators for time-series IoT data and can easily be extended to accommodate
new data types and drift detection …

data demo detection framework privacy

More from arxiv.org / cs.CR updates on arXiv.org

Senior Cloud Security Engineer

@ HelloFresh | Berlin, Germany

Senior Security Engineer

@ Reverb | Remote, US

Sr. Product Manager - Cloud Security/CNAPP

@ Zscaler | Atlanta, GA, United States

ISSO - Security Delivery

@ Novetta | Columbia, MD

Junior Cyber Security Recruitment Consultant (possibility for work abroad)

@ Gradfuel | London, England, United Kingdom

Internship, Cybersecurity

@ Qontigo | Eschborn, Hessen, Germany

Security Administrator

@ Zero Hash | Melbourne, VIC - Remote

Cybersecurity Project Manager, Reactive Lead - Unit 42 Consulting (Remote)

@ Palo Alto Networks | Santa Clara, CA, United States

Consultant, GRC, Proactive Services (Unit 42) - Remote

@ Palo Alto Networks | New York City, United States

Senior Manager, Security Operations (Secure Access Engineering)

@ GitHub | Remote - United States

Junior Penetration Tester - Amsterdam

@ BreachLock | Amsterdam, North Holland, Netherlands

Senior Product Security Engineer

@ 8x8, Inc. | Remote, Romania