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Atmaca, Uğur İlker

Profile URL
https://hdl.handle.net/20.500.12573/2702
Name Variants
Atmaca, UI
Job Title
Öğr. Gör.
Email Address
ugur.atmaca@agu.edu.tr
Main Affiliation
02. 04. Bilgisayar Mühendisliği
Status
Current Staff
Website
ORCID ID
0000-0002-7428-3504
Scopus Author ID
57211019950
Turkish CoHE Profile ID
D00598A88C13CF5F
Google Scholar ID
I9k1mj0AAAAJ
WoS Researcher ID
JZE-0671-2024
Files
5522_Uğur İlker Atmaca.jpg (8.66 KB)

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Now showing 1 - 2 of 2
  • Article
    Threat Landscape of Edge-AI-Assisted Connected Autonomous Vehicles (CAV)
    (Elsevier B.V., 2026) He, Ligang; Maple, Carsten; Atmaca, Ugur Ilker; Kasyap, Harsh; Nezhad, Mahshid Mehr; Atmacaa, Ugur Ilker; Nezhada, Mahshid Mehr; Kasyapa, Harsh
    Connected Autonomous Vehicles (CAVs) powered by Edge Artificial Intelligence (Edge-AI) are revolutionising intelligent transportation systems through real-time data processing for obstacle avoidance, adaptive learning of dynamic traffic patterns, and improved decision-making in safety-critical tasks like traffic sign recognition. However, deploying machine learning (ML) models directly at the vehicle level introduces new security vulnerabilities, making CAVs susceptible to adversarial attacks that can compromise system integrity, reliability, and safety. These attacks primarily target onboard ML systems, highlighting the need for a structured threat modelling approach to identify and mitigate risks. This paper investigates the threat landscape of Edge-AI-assisted CAVs, focusing on the CAV layer as a critical attack surface within the hierarchical system architecture. Employing the STRIDE framework, we analyse vulnerabilities across four stages of the ML lifecycle: input data, model training, aggregation and inference. We present adversarial concept drift as a case study of an evolving threat in which malicious actors introduce subtle data manipulations over time to degrade ML model performance while avoiding detection. Through attack tree analysis and experimental evaluation, we show how adversarial concept drift propagates through the system, ultimately affecting its reliability. To the best of our knowledge, this is the first study to illustrate the impact of gradual adversarial concept drift on robustness. Our results reveal that gradual drifts are harder to detect than sudden ones, even when using Byzantine-robust defenses. These findings underscore the limitations of existing security mechanisms and emphasise the urgent need for adaptive, resilient countermeasures to address the dynamic, evolving threat landscape facing Edge-AI-enabled CAV systems.
  • Article
    Data-Driven Medical Devices and the EU MDR: Mapping Gaps in Standards for Regulatory Compliance
    (Springer Nature, 2026-03-09) Epiphaniou, Gregory; Harrison, Stuart; Khadka, Anita; Maple, Carsten; Matragkas, Nikolaos; Islam, Saif Ul; Atmaca, Ugur Ilker