Hajar Moudoud

739 total citations
28 papers, 349 citations indexed

About

Hajar Moudoud is a scholar working on Computer Networks and Communications, Artificial Intelligence and Information Systems. According to data from OpenAlex, Hajar Moudoud has authored 28 papers receiving a total of 349 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Networks and Communications, 14 papers in Artificial Intelligence and 10 papers in Information Systems. Recurrent topics in Hajar Moudoud's work include Network Security and Intrusion Detection (10 papers), Privacy-Preserving Technologies in Data (10 papers) and Blockchain Technology Applications and Security (8 papers). Hajar Moudoud is often cited by papers focused on Network Security and Intrusion Detection (10 papers), Privacy-Preserving Technologies in Data (10 papers) and Blockchain Technology Applications and Security (8 papers). Hajar Moudoud collaborates with scholars based in Canada, France and United Arab Emirates. Hajar Moudoud's co-authors include Soumaya Cherkaoui, Lyes Khoukhi, Zakaria Abou El Houda, Bouziane Brik, Zoubeir Mlika, Muhammad Adil, Sohail Abbas, Saad Harous and Mian Ahmad Jan and has published in prestigious journals such as IEEE Transactions on Vehicular Technology, IEEE Transactions on Intelligent Transportation Systems and IEEE Internet of Things Journal.

In The Last Decade

Hajar Moudoud

24 papers receiving 339 citations

Peers

Hajar Moudoud

CNC

EEE

Kallol Krishna Karmakar Australia

Anni Peng China

H. Hakan Kılınç Türkiye

Haomeng Xie China

Zahid Ghaffar Taiwan

Imane Aly Saroit Egypt

Zina Chkirbene Qatar

Sangwon Hyun South Korea

Wafa Ben Jaballah France

Mohammed Mujib Alshahrani Saudi Arabia

Kallol Krishna Karmakar Australia

Hajar Moudoud

248 ×1.2

CNC

84 ×0.5

62 ×0.5

54 ×0.8

EEE

60 ×1.1

Citations per year, relative to Hajar Moudoud Hajar Moudoud (= 1×) peers Kallol Krishna Karmakar

Countries citing papers authored by Hajar Moudoud

Since Specialization

Citations

This map shows the geographic impact of Hajar Moudoud'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 Hajar Moudoud with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hajar Moudoud more than expected).

Fields of papers citing papers by Hajar Moudoud

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hajar Moudoud. 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 Hajar Moudoud. The network helps show where Hajar Moudoud may publish in the future.

Co-authorship network of co-authors of Hajar Moudoud

This figure shows the co-authorship network connecting the top 25 collaborators of Hajar Moudoud. A scholar is included among the top collaborators of Hajar Moudoud 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 Hajar Moudoud. Hajar Moudoud is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Houda, Zakaria Abou El, et al.. (2025). An In-Depth Comparative Study of Quantum-Classical Encoding Methods for Network Intrusion Detection. IEEE Open Journal of the Communications Society. 6. 1129–1148. 3 indexed citations

Moudoud, Hajar, Zakaria Abou El Houda, & Bouziane Brik. (2025). Advancing Privacy and Fairness in Healthcare Using Federated Edge Learning and Blockchain. IEEE Internet of Things Journal. 12(22). 46129–46137.

Houda, Zakaria Abou El, Hajar Moudoud, & Bouziane Brik. (2025). When Federated Learning Meets Knowledge Distillation to Secure Consumer Edge Network. IEEE Transactions on Consumer Electronics. 71(2). 7192–7200.

Houda, Zakaria Abou El, et al.. (2025). Securing O-RAN Equipment Using Blockchain-Based Supply Chain Verification. SPIRE - Sciences Po Institutional REpository. 1570–1575.

Houda, Zakaria Abou El, Hajar Moudoud, & Lyes Khoukhi. (2024). Blockchain Meets O-RAN: A Decentralized Zero-Trust Framework for Secure and Resilient O-RAN in 6G and Beyond. SPIRE - Sciences Po Institutional REpository. 1–6. 8 indexed citations

Moudoud, Hajar, et al.. (2024). Reputation-Aware Scheduling for Secure Internet of Drones: A Federated Multi-Agent Deep Reinforcement Learning Approach. 1–6. 3 indexed citations

Houda, Zakaria Abou El, Hajar Moudoud, Bouziane Brik, & Lyes Khoukhi. (2024). Blockchain-Enabled Federated Learning for Enhanced Collaborative Intrusion Detection in Vehicular Edge Computing. IEEE Transactions on Intelligent Transportation Systems. 25(7). 7661–7672. 29 indexed citations

Moudoud, Hajar, Zakaria Abou El Houda, & Bouziane Brik. (2024). Advancing Security and Trust in WSNs: A Federated Multi-Agent Deep Reinforcement Learning Approach. IEEE Transactions on Consumer Electronics. 70(4). 6909–6918. 12 indexed citations

Moudoud, Hajar, Zakaria Abou El Houda, & Bouziane Brik. (2024). Zero Trust Security Architecture for 6G Open Radio Access Networks (ORAN). IEEE Networking Letters. 6(4). 272–275. 5 indexed citations

10.

Moudoud, Hajar, Zakaria Abou El Houda, & Bouziane Brik. (2024). Securing the Metaverse: The Intersection of ML-Based Oracles and Blockchain Technology. 238–245. 1 indexed citations

11.

Houda, Zakaria Abou El, Hajar Moudoud, & Bouziane Brik. (2024). Federated Deep Reinforcement Learning for Efficient Jamming Attack Mitigation in O-RAN. IEEE Transactions on Vehicular Technology. 73(7). 9334–9343. 32 indexed citations

12.

Houda, Zakaria Abou El, Hajar Moudoud, Bouziane Brik, & Muhammad Adil. (2024). A Privacy-Preserving Framework for Efficient Network Intrusion Detection in Consumer Network Using Quantum Federated Learning. IEEE Transactions on Consumer Electronics. 70(4). 7121–7128. 14 indexed citations

13.

Moudoud, Hajar & Soumaya Cherkaoui. (2023). Multi-tasking Federated Learning meets Blockchain to Foster Trust and Security in the Metaverse. Ad Hoc Networks. 150. 103264–103264. 13 indexed citations

14.

Moudoud, Hajar & Soumaya Cherkaoui. (2023). Empowering Security and Trust in 5G and Beyond: A Deep Reinforcement Learning Approach. IEEE Open Journal of the Communications Society. 4. 2410–2420. 14 indexed citations

15.

Houda, Zakaria Abou El, Hajar Moudoud, Bouziane Brik, & Lyes Khoukhi. (2023). Securing Federated Learning through Blockchain and Explainable AI for Robust Intrusion Detection in IoT Networks. SPIRE - Sciences Po Institutional REpository. 1–6. 24 indexed citations

16.

Moudoud, Hajar, et al.. (2023). Strengthening Open Radio Access Networks: Advancing Safeguards Through ZTA and Deep Learning. PolyPublie (École Polytechnique de Montréal). 80–85. 5 indexed citations

17.

Moudoud, Hajar & Soumaya Cherkaoui. (2023). Federated Learning Meets Blockchain to Secure the Metaverse. PolyPublie (École Polytechnique de Montréal). 339–344. 6 indexed citations

18.

Moudoud, Hajar, Zoubeir Mlika, Lyes Khoukhi, & Soumaya Cherkaoui. (2022). Detection and Prediction of FDI Attacks in IoT Systems via Hidden Markov Model. IEEE Transactions on Network Science and Engineering. 9(5). 2978–2990. 38 indexed citations

19.

Moudoud, Hajar, Soumaya Cherkaoui, & Lyes Khoukhi. (2021). Towards a Scalable and Trustworthy Blockchain: IoT Use Case. arXiv (Cornell University). 1–6. 19 indexed citations

20.

Moudoud, Hajar, Lyes Khoukhi, & Soumaya Cherkaoui. (2020). Prediction and Detection of FDIA and DDoS Attacks in 5G Enabled IoT. IEEE Network. 35(2). 194–201. 69 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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