Adel Samir ElZemity

Room 239

Kennedy Building

School of Computing

University of Kent

Canterbury, Kent, UK CT2 7FS

Currently

Pursuing a Ph.D. in Computer Science at University of Kent, UK, under the supervision of Dr. Budi Arief and Prof. Shujun Li, conducting research on the safety and security of Large Language Models (LLMs) and LLM-powered applications.
Working as a Graduate Teaching Assistant (GTA) at the University of Kent, teaching various undergraduate and master’s courses (see teaching experience)

Previously

Bachelor’s degree in Computer Engineering from Nile University
International exchange programs in Computer Science at Riga Technical University, Latvia and Fayetteville State University, USA
Machine Learning Engineer at Spanish National Cancer Research Center (CNIO), Spain, focusing on Natural Language Processing

News

Oct 28, 2025	Presenting at iCSS Seminar Series
Oct 17, 2025	Presenting at AISec (co-located with ACM CCS 2025)
Sep 16, 2025	Successfully passed my PhD 2nd Year review after 24 months! 🎉
Aug 14, 2025	Third PhD paper accepted at SECAI (co-located with ESORICS)
Aug 11, 2025	Second PhD paper accepted at AISec (co-located with ACM CCS)

Selected Publications

SECAI

Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data

Adel ElZemity, Budi Arief , and Shujun Li

In Proceedings of the 2025 International Workshop on Security and Artificial Intelligence (SECAI 2025) , 2025

arXiv Bib PDF Video Slides

@inproceedings{analysing_llm_risks,
  title = {Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data},
  author = {ElZemity, Adel and Arief, Budi and Li, Shujun},
  booktitle = {Proceedings of the 2025 International Workshop on Security and Artificial Intelligence (SECAI 2025)},
  year = {2025},
  eprint = {2505.09974},
  archiveprefix = {arXiv},
  primaryclass = {cs.CR},
}

AISec

CyberLLMInstruct: A Pseudo-malicious Dataset Revealing Safety-performance Trade-offs in Cyber Security LLM Fine-tuning

Adel ElZemity, Budi Arief , and Shujun Li

In Proceedings of the 2025 Workshop on Artificial Intelligence and Security (AISec 2025) , 2025

arXiv Bib PDF Code Poster

@inproceedings{elzemity2025cyberllminstruct,
  title = {CyberLLMInstruct: A Pseudo-malicious Dataset Revealing Safety-performance Trade-offs in Cyber Security LLM Fine-tuning},
  author = {ElZemity, Adel and Arief, Budi and Li, Shujun},
  booktitle = {Proceedings of the 2025 Workshop on Artificial Intelligence and Security (AISec 2025)},
  year = {2025},
  publisher = {ACM},
  doi = {10.1145/3733799.3762968},
  eprint = {2503.09334},
  archiveprefix = {arXiv},
  primaryclass = {cs.CR},
}

IEEE Xplore

Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review

Adel ElZemity, and Budi Arief

In 2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics , 2024

Bib PDF Website

@inproceedings{10731741,
  author = {ElZemity, Adel and Arief, Budi},
  booktitle = {2024 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics},
  title = {Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review},
  year = {2024},
  pages = {331-338},
  keywords = {Privacy;Differential privacy;Social computing;Computational modeling;Multi-party computation;Robustness;Blockchains;Internet of Things;Time factors;Security;Federated Learning;Internet of Things;Privacy Threats;Defensive Measures;Systematic Literature Review},
  doi = {10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics62450.2024.00072}
}

Springer
A Comparative Analysis of Time Series Transformers and Alternative Deep Learning Models for SSVEP Classification

Heba Ali , Adel ElZemity, Amir E Oghostinos , and 1 more author

In International Conference on Model and Data Engineering , 2023

Abs Bib Website

Steady State Visually Evoked Potentials (SSVEPs) are intrinsic responses to specific visual stimulus frequencies. When the retina is activated by a frequency ranging from 3.5 to 75 Hz, the brain produces electrical activity at the same frequency as the visual signal, or its multiples. Identifying the preferred frequencies of neurocortical dynamic processes is a benefit of SSVEPs. However, the time consumed during calibration sessions limits the number of training trials and gives rise to visual fatigue since there is significant human variation across and within individuals over time, which weakens the effectiveness of the individual training data. To address this issue, we propose a novel cross-subject-based classification method to enhance the robustness of SSVEP classification by employing cross-subject similarity and variability. Through an efficient time-series transformer, we compared Time Series Transformers (TST) with different deep learning approaches in the literature. We utilized the TST to speed up calibration processes and improve classification precision for new users. Then we compare this technique to other techniques: EEGNet, FBtCNN, and C-CNN. Our suggested framework’s outcomes are validated using two datasets with two different time window lengths. The experimental results suggest that cross-subject time-series transformers and EEGNet achieve better performance with specific subjects than state-of-the-art techniques when compared to other techniques that have high potential for building high-speed BCIs.
@inproceedings{ali2023comparative, title = {A Comparative Analysis of Time Series Transformers and Alternative Deep Learning Models for SSVEP Classification}, author = {Ali, Heba and ElZemity, Adel and Oghostinos, Amir E and Selim, Sahar}, booktitle = {International Conference on Model and Data Engineering}, pages = {3--16}, year = {2023}, organization = {Springer}, isbn = {978-3-031-55729-3}, publisher = {Springer Nature Switzerland}, doi = {10.1007/978-3-031-55729-3_2} }