M
Mohsen Guizani
Researcher at Qatar University
Publications - 1337
Citations - 48275
Mohsen Guizani is an academic researcher from Qatar University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 79, co-authored 1110 publications receiving 31282 citations. Previous affiliations of Mohsen Guizani include Jaypee Institute of Information Technology & University College for Women.
Papers
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Journal ArticleDOI
A sentence-level text adversarial attack algorithm against IIoT based smart grid
TL;DR: In this article, the important sentences perturbed and Encoder/Decoder (ISPED) attack algorithm was proposed for natural language classification models on the sentence-level, which selects sentences that have more influence on the results to disturb while keeping the semantics unchanged.
Proceedings ArticleDOI
Online Internet Traffic Monitoring and DDoS Attack Detection Using Big Data Frameworks
TL;DR: This paper treats network traffic as a streaming data, and proposes an online Internet traffic monitoring framework based on Spark Streaming and Flink, respectively, which could be used for real-time TCP performance monitoring and DDoS detection.
Proceedings ArticleDOI
A Blockchain-based Conditional Privacy-Preserving Traffic Data Sharing in Cloud
TL;DR: Through theoretical and simulation analysis in terms of security, complexity and time consumption, the proposed traffic data sharing protocol is proved to meet the security requirements of data tamper-proofing, anonymity and traceability with higher efficiency.
Journal ArticleDOI
Multicast at Edge: An Edge Network Architecture for Service-Less Crowdsourced Live Video Multicast
TL;DR: In this paper, the authors proposed an overlay network architecture to augment the standard eMBMS architecture and enable service-less multicast for crowdsourced live video providers, which provides significant benefits in bandwidth saving at backhaul, transit, and RAN links.
Journal ArticleDOI
Implementation and Analysis of Reward Functions Under Different Traffic Models for Distributed DSA Systems
TL;DR: This paper implements and analyzes a resource allocation protocol for distributed dynamic spectrum allocation (DSA) systems and shows the importance of the objective function's choice; used as a utility to be maximized in the learning.