Researcher:
Uysal, Ahmet

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Undergraduate Student

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Ahmet

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Uysal

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Uysal, Ahmet

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20202

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Researcher Of Publications: search.filters.isAuthorOfPublication.ea432fab-0313-4b28-ae17-499bb5a53a40

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    Chain FL: Decentralized federated machine learning via blockchain
    (Ieee, 2020) Masry, Ahmed; Department of Electrical and Electronics Engineering; Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; Korkmaz, Caner; Koçaş, Halil Eralp; Uysal, Ahmet; Özkasap, Öznur; Akgün, Barış; Undergraduate Student; Undergraduate Student; Undergraduate Student; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Department of Computer Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 113507; 258784
    Federated learning is a collaborative machine learning mechanism that allows multiple parties to develop a model without sharing the training data. It is a promising mechanism since it empowers collaboration in fields such as medicine and banking where data sharing is not favorable due to legal, technical, ethical, or safety issues without significantly sacrificing accuracy. In centralized federated learning, there is a single central server, and hence it has a single point of failure. Unlike centralized federated learning, decentralized federated learning does not depend on a single central server for the updates. In this paper, we propose a decentralized federated learning approach named Chain FL that makes use of the blockchain to delegate the responsibility of storing the model to the nodes on the network instead of a centralized server. Chain FL produced promising results on the MNIST digit recognition task with a maximum 0.20% accuracy decrease, and on the CIFAR-10 image classification task with a maximum of 2.57% accuracy decrease as compared to non-FL counterparts.
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    PublicationOpen Access
    LogDoS: a novel logging-based DDoS prevention mechanism in path identifier-based information centric networks
    (Elsevier, 2020) Al-Duwairi, Basheer; Department of Computer Engineering; Özkasap, Öznur; Uysal, Ahmet; Kocaoğullar, Ceren; Yıldırım, Kaan; Faculty Member; Department of Computer Engineering; College of Engineering; 113507; N/A; N/A; N/A
    Information Centric Networks (ICNs) have emerged in recent years as a new networking paradigm for the next-generation Internet. The primary goal of these networks is to provide effective mechanisms for content distribution and retrieval based on in-network content caching. Several network architectures were proposed in recent years to realize this communication model. This include Named Data Networks (NDN) and Path-Identifier (PID) based ICN. This paper proposes LogDoS as a novel mechanism to address the problem of data flooding attacks in PID-based ICNs. The proposed LogDoS mechanism is a unique hybrid approach that combines the best of NDN networks and PID-based ICNs, and it is the first to employ Bloom-filter based logging approach in a novel way to filter attack traffic efficiently. In this context, we develop and model three versions of LogDoS with varying levels of storage overhead at LogDoS-enabled routers. Extensive simulation experiments show that LogDoS is very effective against DDoS attacks as it can filter more than 99.98% of attack traffic in different attack scenarios while incurring acceptable storage overhead.