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Amr M. Youssef
Researcher at Concordia University
Publications - 252
Citations - 4812
Amr M. Youssef is an academic researcher from Concordia University. The author has contributed to research in topics: Block cipher & Cryptanalysis. The author has an hindex of 28, co-authored 232 publications receiving 3877 citations. Previous affiliations of Amr M. Youssef include Concordia University Wisconsin & IBM.
Papers
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Journal ArticleDOI
Ultra-Dense Networks: A Survey
TL;DR: This paper provides a survey-style introduction to dense small cell networks and considers many research directions, namely, user association, interference management, energy efficiency, spectrum sharing, resource management, scheduling, backhauling, propagation modeling, and the economics of UDN deployment.
Journal ArticleDOI
Security, Privacy, and Safety Aspects of Civilian Drones: A Survey
Riham AlTawy,Amr M. Youssef +1 more
TL;DR: It is forecast that security will be a central enabling technology for the next generation of civilian unmanned aerial vehicles and the security properties required by their critical operation environment.
Proceedings ArticleDOI
On the analysis of the Zeus botnet crimeware toolkit
Hamad Binsalleeh,T. Ormerod,Amine Boukhtouta,Prosenjit Sinha,Amr M. Youssef,Mourad Debbabi,Lingyu Wang +6 more
TL;DR: The reverse engineering insights allow for a better understanding of the technologies and behaviors of such modern HTTP botnet crimeware toolkits and opens an opportunity to inject falsified information into the botnet communications which can be used to defame this Crimeware toolkit.
Journal ArticleDOI
Power quality disturbance classification using the inductive inference approach
TL;DR: This paper presents a novel approach for the classification of power quality disturbances based on inductive learning by using decision trees, where the wavelet transform is utilized to produce representative feature vectors that can accurately capture the unique and salient characteristics of each disturbance.
Journal ArticleDOI
Attack Detection and Identification for Automatic Generation Control Systems
TL;DR: An anomaly based attack detection and identification method for protecting the AGC system against cyber vulnerabilities and the effectiveness of the proposed method is corroborated using simulation results for a three-area power system and the IEEE 39-bus network.