D
Devrim Unal
Researcher at Qatar University
Publications - 30
Citations - 692
Devrim Unal is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Computer security model. The author has an hindex of 9, co-authored 26 publications receiving 232 citations. Previous affiliations of Devrim Unal include Scientific and Technological Research Council of Turkey.
Papers
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Recent Advances in the Internet-of-Medical-Things (IoMT) Systems Security
TL;DR: This article comprehensively overviews IoMT systems’ potential attacks, including physical and network attacks, and proposes a security framework that covers IoMT security requirements and can mitigate most of its known attacks.
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Deep learning for detection of routing attacks in the internet of things
TL;DR: A highly scalable, deep-learning based attack detection methodology for detection of IoT routing attacks which are decreased rank, hello-flood and version number modification attacks, with high accuracy and precision is proposed.
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Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study
TL;DR: This paper aims to show that combining both network and biometric metrics as features performs better than using only one of the two types of features in intrusion detection, and builds a real-time Enhanced Healthcare Monitoring System testbed that monitors the patients’ biometrics and collects network flow metrics.
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Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach.
TL;DR: This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms, and indicates that the presented models outperform the existing models in the literature.
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Integration of federated machine learning and blockchain for the provision of secure big data analytics for Internet of Things
Devrim Unal,Mohammad Hammoudeh,Muhammad Asif Khan,Abdelrahman Abuarqoub,Gregory Epiphaniou,Ridha Hamila +5 more
TL;DR: This research presents a practical approach for the integration of Blockchain with FL to provide privacy-preserving and secure big data analytics services and proposes utilizing fuzzy hashing to detect variations and anomalies in FL-trained models against poisoning attacks.