Nashid Shahriar

2.6k total citations · 1 hit paper
52 papers, 1.7k citations indexed

About

Nashid Shahriar is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Nashid Shahriar has authored 52 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Computer Networks and Communications, 29 papers in Electrical and Electronic Engineering and 11 papers in Artificial Intelligence. Recurrent topics in Nashid Shahriar's work include Software-Defined Networks and 5G (38 papers), Advanced Optical Network Technologies (25 papers) and Network Security and Intrusion Detection (14 papers). Nashid Shahriar is often cited by papers focused on Software-Defined Networks and 5G (38 papers), Advanced Optical Network Technologies (25 papers) and Network Security and Intrusion Detection (14 papers). Nashid Shahriar collaborates with scholars based in Canada, Italy and United Kingdom. Nashid Shahriar's co-authors include Raouf Boutaba, Sara Ayoubi, Mohammad A. Salahuddin, Felipe Estrada‐Solano, Oscar Maurício Caicedo Rendón, Noura Limam, Reaz Ahmed, Shihabur Rahman Chowdhury, Jeebak Mitra and Milad Ghaznavi and has published in prestigious journals such as IEEE Journal on Selected Areas in Communications, IEEE Communications Magazine and IEEE Transactions on Communications.

In The Last Decade

Nashid Shahriar

51 papers receiving 1.7k citations

Hit Papers

A comprehensive survey on machine learning for networking... 2018 2026 2020 2023 2018 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A comprehensive survey on machine learning for networking: evolution, applications and research opportunities
    2018 651 citations Raouf Boutaba, Noura Limam et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nashid Shahriar Canada 17 1.4k 721 415 222 97 52 1.7k
Oscar Maurício Caicedo Rendón Colombia 15 1.1k 0.8× 427 0.6× 463 1.1× 155 0.7× 83 0.9× 61 1.4k
Sara Ayoubi Canada 13 1.1k 0.8× 485 0.7× 410 1.0× 244 1.1× 85 0.9× 33 1.4k
Andreas Kassler Sweden 24 1.9k 1.3× 1.1k 1.6× 243 0.6× 290 1.3× 98 1.0× 207 2.3k
Noura Limam Canada 14 964 0.7× 247 0.3× 540 1.3× 256 1.2× 182 1.9× 38 1.3k
Dimitrios P. Pezaros United Kingdom 23 1.4k 1.0× 421 0.6× 204 0.5× 545 2.5× 77 0.8× 117 1.6k
Jude Okwuibe Finland 8 1000 0.7× 553 0.8× 223 0.5× 334 1.5× 94 1.0× 14 1.3k
Weisheng Si Australia 15 1.0k 0.7× 323 0.4× 153 0.4× 349 1.6× 71 0.7× 48 1.3k
Raffaele Bolla Italy 20 1.8k 1.3× 1.2k 1.6× 221 0.5× 332 1.5× 96 1.0× 186 2.1k
Guochu Shou China 14 876 0.6× 496 0.7× 218 0.5× 215 1.0× 24 0.2× 130 1.2k
Guoming Tang China 20 656 0.5× 442 0.6× 206 0.5× 258 1.2× 35 0.4× 84 1.1k

Countries citing papers authored by Nashid Shahriar

Since Specialization
Citations

This map shows the geographic impact of Nashid Shahriar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nashid Shahriar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nashid Shahriar more than expected).

Fields of papers citing papers by Nashid Shahriar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nashid Shahriar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nashid Shahriar. The network helps show where Nashid Shahriar may publish in the future.

Co-authorship network of co-authors of Nashid Shahriar

This figure shows the co-authorship network connecting the top 25 collaborators of Nashid Shahriar. A scholar is included among the top collaborators of Nashid Shahriar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nashid Shahriar. Nashid Shahriar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Shahriar, Nashid, et al.. (2024). Automated orchestration of virtualized deployments of mobile core networks. Computer Networks. 255. 110888–110888. 1 indexed citations
2.
Shahriar, Nashid, et al.. (2024). DTL-5G: Deep transfer learning-based DDoS attack detection in 5G and beyond networks. Computer Communications. 228. 107927–107927. 12 indexed citations
3.
Shahriar, Nashid, et al.. (2024). Towards Effective Network Intrusion Detection in Imbalanced Datasets: A Hierarchical Approach. 254–258. 1 indexed citations
4.
Saha, Niloy, et al.. (2024). Monarch: Monitoring Architecture for 5G and Beyond Network Slices. IEEE Transactions on Network and Service Management. 22(1). 777–790.
5.
6.
Shahriar, Nashid, et al.. (2023). 5G Network Slice Type Classification using Traditional and Incremental Learning. 1–5. 4 indexed citations
7.
Shahriar, Nashid, et al.. (2022). SliceSecure: Impact and Detection of DoS/DDoS Attacks on 5G Network Slices. 639–642. 15 indexed citations
8.
Shahriar, Nashid, et al.. (2022). Encrypted Network Traffic Classification in SDN using Self-supervised Learning. 243–245. 2 indexed citations
9.
Shahriar, Nashid, et al.. (2022). Bandwidth Prediction in 5G Mobile Networks Using Informer. 1–9. 6 indexed citations
10.
Shahriar, Nashid, et al.. (2022). Anomaly Detection and Localization in NFV Systems: an Unsupervised Learning Approach. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1–9. 10 indexed citations
11.
Shahriar, Nashid, et al.. (2019). Virtual Network Embedding with Path-based Latency Guarantees in Elastic Optical Networks. 1–12. 15 indexed citations
12.
Shahriar, Nashid, Reaz Ahmed, Shihabur Rahman Chowdhury, et al.. (2019). Virtual Network Embedding With Guaranteed Connectivity Under Multiple Substrate Link Failures. IEEE Transactions on Communications. 68(2). 1025–1043. 20 indexed citations
13.
Harutyunyan, Davit, et al.. (2019). Orchestrating End-to-end Slices in 5G Networks. Università Politecnica delle Marche (Università Politecnica delle Marche). 1–9. 21 indexed citations
14.
Shahriar, Nashid, et al.. (2019). Achieving a Fully-Flexible Virtual Network Embedding in Elastic Optical Networks. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 1756–1764. 33 indexed citations
15.
Chowdhury, Shihabur Rahman, Sara Ayoubi, Reaz Ahmed, et al.. (2018). Multi-Layer Virtual Network Embedding. IEEE Transactions on Network and Service Management. 15(3). 1132–1145. 12 indexed citations
16.
Shahriar, Nashid, Shihabur Rahman Chowdhury, Reaz Ahmed, et al.. (2017). Joint backup capacity allocation and embedding for survivable virtual networks. 1–9. 4 indexed citations
17.
Shahriar, Nashid, Reaz Ahmed, Shihabur Rahman Chowdhury, et al.. (2017). Generalized Recovery From Node Failure in Virtual Network Embedding. IEEE Transactions on Network and Service Management. 14(2). 261–274. 38 indexed citations
18.
Shahriar, Nashid, Reaz Ahmed, Shihabur Rahman Chowdhury, et al.. (2016). Connectivity-aware virtual network embedding. 46–54. 13 indexed citations
19.
Ghaznavi, Milad, Aimal Khan, Nashid Shahriar, et al.. (2015). Elastic virtual network function placement. 255–260. 156 indexed citations
20.
Shahriar, Nashid, Mahfuza Sharmin, Reaz Ahmed, & Raouf Boutaba. (2011). Diurnal Availability for Peer-to-Peer Systems. arXiv (Cornell University). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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