all AI news
Temporal assessment of malicious behaviors: application to turnout field data monitoring
May 7, 2024, 4:43 a.m. | Sara Abdellaoui, Emil Dumitrescu, C\'edric Escudero, Eric Zama\"i
cs.LG updates on arXiv.org arxiv.org
Abstract: Monitored data collected from railway turnouts are vulnerable to cyberattacks: attackers may either conceal failures or trigger unnecessary maintenance actions. To address this issue, a cyberattack investigation method is proposed based on predictions made from the temporal evolution of the turnout behavior. These predictions are then compared to the field acquired data to detect any discrepancy. This method is illustrated on a collection of real-life data.
abstract application arxiv assessment behavior cs.cr cs.lg cs.sy cyberattack cyberattacks data data monitoring eess.sy evolution investigation issue maintenance monitoring predictions railway temporal type vulnerable
More from arxiv.org / cs.LG updates on arXiv.org
Efficient Data-Driven MPC for Demand Response of Commercial Buildings
2 days, 22 hours ago |
arxiv.org
Testing the Segment Anything Model on radiology data
2 days, 22 hours ago |
arxiv.org
Calorimeter shower superresolution
2 days, 22 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US