A Comprehensive Survey on Online AutoML and Adversarial Robustness for IoT and EV Charging Network Security
Wajiha Zaheer, Chukwunonso Henry Nwokoye, Seyedeh Negar Afrasiabi, Khalil El-Khatib, Li Yang · 2026
Read A Comprehensive Survey on Online AutoML and Adversarial Robustness for IoT and EV Charging Network Security by Wajiha Zaheer, Chukwunonso Henry Nwokoye, Seyedeh Negar Afrasiabi, Khalil El-Khatib, Li Yang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13306787.txt.
Description
Wajiha Zaheer surveyed self-tuning machine learning for guarding electric car charging networks in a 2026 review. Linked chargers and home devices create shifting data patterns where fixed learning pipelines age quickly and miss new attacks.
Online AutoML adapts to streaming data with little human help, and can learn under tight computing and privacy limits in the authors' account. Attackers can also poison or fool such adaptive systems, so defences must handle drift, scale, privacy and hostile input together. Shared public benchmarks are urged to make competing systems comparable. The team says joint rules will help guard charge nets as they grow in size.
Catalog record
| CREATOR | Wajiha Zaheer, Chukwunonso Henry Nwokoye, Seyedeh Negar Afrasiabi, Khalil El-Khatib, Li Yang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 183395 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |