A. Selcuk Uluagac
Florida International University
146 Papers
611 Citations
A. Selcuk Uluagac is an academic researcher from Florida International University. The author has contributed to research in topics: Computer science & Smart grid. The author has an hindex of 26, co-authored 130 publications. Previous affiliations of A. Selcuk Uluagac include Georgia Institute of Technology & Carnegie Mellon University.
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Papers
Ivycide: Smart Intrusion Detection System Against E-IoT Driver Threats
TL;DR: In this article , a novel intrusion detection system called Ivycide is proposed to detect unexpected E-IoT network traffic from an enterprise IoT system using machine learning and signature-based classification to detect Poisonivy attacks.
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Detection of Compromised Smart Grid Devices with Machine Learning and Convolution Techniques
TL;DR: A machine learning and convolution-based classification framework that specifically utilizes system and library call lists at the kernel level of the operating system on both resource-limited and resource-rich smart grid devices such as RTUs, PLCs, PMUs, and IEDs is proposed.
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Efficient safety message forwarding using multi-channels in low density VANETs
Jinyoun Cho,A. Selcuk Uluagac,John A. Copeland,Yusun Chang +3 more
- 01 Dec 2014
TL;DR: A network coding technique with divide-and-deliver is introduced to solve this unique challenge for delivering multimedia contents through multiple service channels in a low vehicle density and is shown to significantly improves reliability with minimum usage of the control channel in a typical VANETs environment.
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SoK: Cryptojacking Malware
TL;DR: In this paper, the authors proposed cryptojacking malware detection methods using various dynamic/behavioral features, such as obfuscation techniques or changing their domains or scripts frequently, which can only provide a partial panacea to this emerging crypt-jacking issue.
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HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systems
TL;DR: HealthGuard, a novel machine learning-based security framework to detect malicious activities in a Smart Healthcare System, observes the vital signs of different connected devices of a SHS and correlates the vitals to understand the changes in body functions of the patient to distinguish benign and malicious activities.
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