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Start date: 2020Type: search.filters.itemtype.Conference PaperAuthor: search.filters.author.Gelal, EceSubject: search.filters.subject.Machine-learningSubject: search.filters.subject.Adversarial Machine Learning

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    Advancing WebRTC QoE Assessment with Machine Learning in Real-World Wi-Fi Scenarios
    (Institute of Electrical and Electronics Engineers Inc., 2024) Argın, Berke; Demir, Mehmet Özgün; Salik, Elif DIlek; Onalan, Aysun Gurur; Batum, Öykü Han; Gelal, Ece; Argın, Berke, Lifemote Networks, Istanbul, Turkey; Demir, Mehmet Özgün, Lifemote Networks, Istanbul, Turkey; Salik, Elif DIlek, Lifemote Networks, Istanbul, Turkey; Onalan, Aysun Gurur, Lifemote Networks, Istanbul, Turkey; Batum, Öykü Han, Lifemote Networks, Istanbul, Turkey; Gelal, Ece, Bahçeşehir Üniversitesi, Istanbul, Turkey
    Video conferencing applications play a key role in enabling use cases like remote working, education, and potentially the metaverse. From the perspective of Internet service providers, predicting the end user’s Quality of Experience (QoE) in such applications is critical in allocating the right resources to ensure consistently high QoE. This work addresses the estimation of user QoE from link-layer performance metrics such as transferred packets, queue size, signal strength, and channel occupancy for WebRTC-supported applications. Our study entails collecting a data set capturing various Wi-Fi scenarios in practical environments and training machine learning models on this data to estimate the perceived QoE. Our findings demonstrate improvement in prediction accuracy compared to earlier models and QoE representations, furthermore, we also investigate the explainability of the models with the help of SHAP values. © 2024 Elsevier B.V., All rights reserved.
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    Analysis of Adversarial Training for Resilient Image Recognition Against Different Attacks
    (Institute of Electrical and Electronics Engineers Inc., 2024) Karakuş, Emir Can; Islim, Firat Kutluhan; Gelal, Ece; Adali, E.; Karakuş, Emir Can, Bahçeşehir Üniversitesi, Istanbul, Turkey; Islim, Firat Kutluhan, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gelal, Ece, Bahçeşehir Üniversitesi, Istanbul, Turkey
    Image recognition systems are used for various critical use cases, from cancer detection to autonomous vehicles, while their accuracy closely relies on the data that they are trained on. Recently, adversarial machine learning has been flagged a s a possible threat against the successful operation of such systems and models. While the research towards understanding how to manipulate such models continues, one mitigation approach is to increase the model's resilience against such samples via including such samples in the training process. In this work, we examine the impact of adversarial training on improving robustness against other adversarial attack types. We generate adversarial samples via two image perturbation methods, Fast Gradient Sign Method (FGSM) and Jacobian-based Saliency Map Attack (JSMA), and include these samples respectively in the training set of two independent ResNet-18 models on the CIFAR-10 dataset. Obtaining two adversarially-trained models, we compare their accuracies upon each attack and discuss the impact on model resiliency when the training set includes samples generated by different approaches. Our results highlight that FGSM-fine tuned (adversarially trained) model earns the model greater resilience against FGSM attack compared to JSMA attack. Adversarial JSMA-training, when JSMA samples are generated to target the original class, earns the model resilience against both attacks. © 2025 Elsevier B.V., All rights reserved.