Kulworatit, Chaichana

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Kulworatit, Chaichana
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Email
chaichana.kul@kmitl.ac.th

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    Item type:Publication,
    Unknown Computerized-Attack Recognition via Open-Set Circumstance-Adaptive Learning Approach
    (2025-01-01)
    Petchhan, Jirayu
    ;
    Kulworatit, Chaichana
    Cyber threats come in all forms and constantly evolve into newly unknown malicious attacks. The status quo is that current mobile and/or embedded devices are unaware of embedded data with newly unknown threats, making it difficult to predict new threat types. To access much higher mobile security, we have deployed open-set domain adaptation to understand existing information yet still be realizable and recognizable to the novel unseen class instances. Our demonstrations are validated against visual benchmarks, such as handwritten data, and applied to our datasets to verify the embedding of unfamiliar data on embedded devices. As a result, it can gather a lot of information for both known and unknown instances from both datasets. Besides, it raises the issue of what cyber threat data to use in the circumstance like embedding over system-level firmware updates.
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    Analysis of Urban Heat Islands Using Nighttime Land Surface Temperature Data
    (2025-01-01)
    Kulworatit, Chaichana
    ;
    Petchhan, Jirayu
    This study analyzes Urban Heat Island (UHI) in Thailand using Nighttime Land Surface Temperature (LST) data from the MODIS MYD11A2 dataset, covering 2002-2025, during the winter months (December-January). The research aims to assess heat accumulation in urban areas and identify factors such as heat-absorbing materials, energy consumption, and the lack of green spaces. It also addresses the misconception that water bodies reduce heat, showing that they can contribute to UHI when not managed properly. The data was processed using Google Earth Engine and QGIS to identify UHI zones and calculate Zonal Statistics at the provincial level.
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    Cross-Domain Robust Liveness Detection: A Transfer Learning Approach for Combating Sophisticated Presentation Attacks in Mobile Authentication
    (2025-01-01)
    Kerdpramote, Phuvis
    ;
    Poomdeesittinon, Akeanant
    ;
    Jamsri, Rapeeploy
    ;
    Krueyos, Phanuwit
    ;
    Muangkan, Ronnakorn
    As biometric authentication systems become ubiquitous in Southeast Asia's digital economy, sophisticated presentation attacks using deepfakes, high-resolution displays, and 3D masks pose critical security threats. This paper presents a comprehensive cross-domain liveness detection framework that addresses the generalization challenges plaguing current systems. Our approach leverages MobileNetV2-based transfer learning with a novel two-phase training strategy, achieving superior cross-domain performance while maintaining computational efficiency for mobile deployment. We introduce domain-aware augmentation techniques and evaluate our system across multiple benchmark datasets including NUAA and a locally-collected Thai demographic dataset. Experimental results demonstrate 84.35% accuracy on NUAA and 78.62% cross-domain accuracy, with significant improvements in Attack Presentation Classification Error Rate (APCER) reduction from 28.7% to 15.4% compared to baseline methods. The system successfully detects emerging attack vectors including deepfake videos and tablet-based spoofing attempts. We provide comprehensive analysis of deployment challenges in resource-constrained environments demonstrating practical applicability for Thailand's mobile banking and digital identity verification ecosystem.