Fatima Salahdine

1.5k total citations · 1 hit paper
22 papers, 830 citations indexed

About

Fatima Salahdine is a scholar working on Computer Networks and Communications, Signal Processing and Computational Mechanics. According to data from OpenAlex, Fatima Salahdine has authored 22 papers receiving a total of 830 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Networks and Communications, 10 papers in Signal Processing and 9 papers in Computational Mechanics. Recurrent topics in Fatima Salahdine's work include Sparse and Compressive Sensing Techniques (9 papers), Blind Source Separation Techniques (7 papers) and Cognitive Radio Networks and Spectrum Sensing (6 papers). Fatima Salahdine is often cited by papers focused on Sparse and Compressive Sensing Techniques (9 papers), Blind Source Separation Techniques (7 papers) and Cognitive Radio Networks and Spectrum Sensing (6 papers). Fatima Salahdine collaborates with scholars based in United States, Morocco and Canada. Fatima Salahdine's co-authors include Naima Kaabouch, Tao Han, Ning Zhang, Hassan El Ghazi, Mohammed Ridouani, Qiang Liu, Aawatif Hayar, Shaohua Wu and Asis Nasipuri and has published in prestigious journals such as SHILAP Revista de lepidopterología, Sensors and Computer Networks.

In The Last Decade

Fatima Salahdine

21 papers receiving 779 citations

Hit Papers

align trajectories sort by overperformance

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.

Social Engineering Attacks: A Survey

2019 280 citations Fatima Salahdine, Naima Kaabouch Future Internet profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fatima Salahdine United States	14	402	268	261	207	156	22	830
Souhwan Jung South Korea	14	340 0.8×	176 0.7×	180 0.7×	224 1.1×	173 1.1×	88	586
Chia-Mu Yu Taiwan	17	473 1.2×	189 0.7×	229 0.9×	72 0.3×	576 3.7×	83	1.1k
Ying Zhu China	15	651 1.6×	229 0.9×	144 0.6×	75 0.4×	83 0.5×	70	1.0k
Yonghong Kuo China	17	495 1.2×	435 1.6×	83 0.3×	50 0.2×	72 0.5×	61	875
Samer Atawneh Saudi Arabia	13	155 0.4×	66 0.2×	230 0.9×	124 0.6×	195 1.3×	25	638
Zengbin Zhang United States	16	585 1.5×	783 2.9×	229 0.9×	115 0.6×	163 1.0×	33	1.3k
Yuan Zhang China	14	325 0.8×	138 0.5×	158 0.6×	170 0.8×	169 1.1×	84	740
Yuqing Li China	12	339 0.8×	122 0.5×	235 0.9×	47 0.2×	300 1.9×	46	759
Jongtae Lim South Korea	12	221 0.5×	166 0.6×	112 0.4×	44 0.2×	79 0.5×	84	514

Countries citing papers authored by Fatima Salahdine

Since Specialization

Citations

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

Fields of papers citing papers by Fatima Salahdine

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fatima Salahdine

This figure shows the co-authorship network connecting the top 25 collaborators of Fatima Salahdine. A scholar is included among the top collaborators of Fatima Salahdine 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 Fatima Salahdine. Fatima Salahdine 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

Ridouani, Mohammed, et al.. (2025). Hybrid Deep Learning and Boosting Approach for Iot Intrusion Detection Using Autoencoder and Xgboost. 1–6.

Ridouani, Mohammed, et al.. (2024). A novel spectrogram based lightweight deep learning for IoT spectrum monitoring. Physical Communication. 64. 102364–102364. 2 indexed citations

Ridouani, Mohammed, et al.. (2024). A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks. Future Internet. 16(1). 32–32. 26 indexed citations

Ridouani, Mohammed, et al.. (2023). Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions. Computers. 12(10). 216–216. 44 indexed citations

Salahdine, Fatima, Tao Han, & Ning Zhang. (2023). 5G, 6G, and Beyond: Recent advances and future challenges. Annals of Telecommunications. 78(9-10). 525–549. 78 indexed citations

Ridouani, Mohammed, et al.. (2022). A novel secure cooperative cognitive radio network based on Chebyshev map. Digital Signal Processing. 126. 103482–103482. 9 indexed citations

Salahdine, Fatima, Qiang Liu, & Tao Han. (2022). Towards Secure and Intelligent Network Slicing for 5G Networks. SHILAP Revista de lepidopterología. 3. 23–38. 26 indexed citations

Salahdine, Fatima, et al.. (2022). Short-Term Traffic Congestion Prediction with Deep Learning for LoRa Networks. 261–268. 5 indexed citations

Salahdine, Fatima, Tao Han, & Ning Zhang. (2022). Security in5Gand beyond recent advances and future challenges. Security and Privacy. 6(1). 46 indexed citations

10.

Ridouani, Mohammed, et al.. (2022). A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning. Sensors. 22(17). 6477–6477. 9 indexed citations

11.

Salahdine, Fatima, et al.. (2021). A survey on sleep mode techniques for ultra-dense networks in 5G and beyond. Computer Networks. 201. 108567–108567. 48 indexed citations

12.

Ridouani, Mohammed, et al.. (2021). Chaotic Compressive Spectrum Sensing Based on Chebyshev Map for Cognitive Radio Networks. Symmetry. 13(3). 429–429. 14 indexed citations

13.

Salahdine, Fatima & Naima Kaabouch. (2020). Security threats, detection, and countermeasures for physical layer in cognitive radio networks: A survey. Physical Communication. 39. 101001–101001. 53 indexed citations

14.

Salahdine, Fatima, et al.. (2020). Social Engineering Attacks A Reconnaissance Synthesis Analysis. 843–848. 12 indexed citations

15.

Salahdine, Fatima, et al.. (2020). Metrics for Evaluating the Efficiency of Compressing Sensing Techniques. arXiv (Cornell University). 562–567. 16 indexed citations

16.

Salahdine, Fatima & Naima Kaabouch. (2019). Social Engineering Attacks: A Survey. Future Internet. 11(4). 89–89. 280 indexed citations breakdown →

17.

Ridouani, Mohammed, et al.. (2019). A Survey on Compressive Spectrum Sensing for Cognitive Radio Networks. 22 indexed citations

18.

Salahdine, Fatima, Naima Kaabouch, & Hassan El Ghazi. (2018). One-bit compressive sensing vs. multi-bit compressive sensing for cognitive radio networks. 1610–1615. 13 indexed citations

19.

Salahdine, Fatima, Naima Kaabouch, & Hassan El Ghazi. (2017). A Bayesian recovery technique with Toeplitz matrix for compressive spectrum sensing in cognitive radio networks. International Journal of Communication Systems. 30(15). 14 indexed citations

20.

Salahdine, Fatima & Hassan El Ghazi. (2017). A real time spectrum scanning technique based on compressive sensing for cognitive radio networks. 506–511. 19 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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