Publications | Adel Samir ElZemity

2025

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

@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

@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},
}

CHARIOT
Ransomware in Resource-Constrained Industrial IoT Networks: There Actually is a Threat

Yuxiang Huang , Calvin Brierley , Adel ElZemity, and 5 more authors

In 2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) , Apr 2025

Abs Bib PDF

The threat of ransomware attacks against Industrial Internet of Things (IIoT) networks, particularly networks of resource-constrained devices, is starting to become a reality. In this paper, we contend that the threat of ransomware infection of an IIoT environment is not only plausible, but that it also exhibits different properties compared to ransomware attacks against traditional desktop systems, necessitating a new and more appropriate approach to deal with this threat. In particular, we articulate the unique characteristics of ransomware behaviour in IIoT networks considering the distinctive characteristics, such as computationally-constrained devices and low-power wireless communication protocols. Furthermore, we outline the necessary attributes for ransomware to effectively compromise and propagate within IIoT networks. To back our argument, we present a proof-of-concept (PoC) IIoT ransomware prototype. To highlight the generality of our work, we have developed the prototype for two different hardware platforms, powered by two different open source embedded operating systems: Contiki-NG and Zephyr.
@inproceedings{6c6894a4b8d349f092d614bc08395b3b, title = {Ransomware in Resource-Constrained Industrial IoT Networks: There Actually is a Threat}, author = {Huang, Yuxiang and Brierley, Calvin and ElZemity, Adel and Pope, James and Ma, Jiteng and {Di Buono}, Antonio and Arief, Budi and Oikonomou, George}, year = {2025}, month = apr, day = {30}, language = {English}, series = {International Conference on Distributed Computing in Sensor Systems (DCOSS)}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, booktitle = {2025 21st International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)}, address = {United States}, url = {https://dcoss.org/}, }

2024

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 , Apr 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}
}

2023

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 , Apr 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} }

IEEE Xplore

A Transformer-Based Deep Learning Architecture for Accurate Intracranial Hemorrhage Detection and Classification

Adel ElZemity, Maryam ElFdaly , Shorouk Abdelfattah , and 6 more authors

In 2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) , Apr 2023

Bib Website

@inproceedings{10391388,
  author = {ElZemity, Adel and ElFdaly, Maryam and Abdelfattah, Shorouk and Abdelwahab, Ahmed and Ramadan, Mohamed and Zakzouk, Salma and Ameen, Ahmed and Elkhishen, Rawan and Darweesh, M. Saeed},
  booktitle = {2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)},
  title = {A Transformer-Based Deep Learning Architecture for Accurate Intracranial Hemorrhage Detection and Classification},
  year = {2023},
  volume = {},
  number = {},
  pages = {215-220},
  keywords = {Technological innovation;Deep architecture;Computer architecture;Streaming media;Transformers;Convolutional neural networks;Hemorrhaging;Intracranial Hemorrhage;Transformer;Swin Transformer},
  doi = {10.1109/3ICT60104.2023.10391388},
}

2020

IEEE Xplore

Wastewater treatment model with smart irrigation utilizing PID control

Adel ElZemity, Ahmed Ali Gaafar , Ahmed Khaled Ahmed , and 4 more authors

In 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) , Apr 2020

Bib Website

@inproceedings{el2020wastewater,
  title = {Wastewater treatment model with smart irrigation utilizing PID control},
  author = {ElZemity, Adel and Gaafar, Ahmed Ali and Ahmed, Ahmed Khaled and Abdelwahab, Ahmed Sayed and Saad, Hatim Mohamed and Elboushi, Mostafa Khaled and Ibraheem, Amira Mofreh},
  booktitle = {2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)},
  pages = {374--379},
  year = {2020},
  organization = {IEEE},
  doi = {10.1109/NILES50944.2020.9257882},
}

2019

IEEE Xplore

Interfacial Modification of Perovskite Solar Cell Using ZnO Electron Injection Layer with PDMS as Antireflective Coating

Mohamed K. Othman , Adel ElZemity, Mohamed K. Rawash , and 4 more authors

In 2019 Novel Intelligent and Leading Emerging Sciences Conference (NILES) , Apr 2019

Bib Website

@inproceedings{8909336,
  author = {Othman, Mohamed K. and ElZemity, Adel and Rawash, Mohamed K. and Taha, Hazem A. and Alalem, Shorouk and El-Fdaly, Maryam and El-Batawy, Yasser M.},
  booktitle = {2019 Novel Intelligent and Leading Emerging Sciences Conference (NILES)},
  title = {Interfacial Modification of Perovskite Solar Cell Using ZnO Electron Injection Layer with PDMS as Antireflective Coating},
  year = {2019},
  volume = {1},
  number = {},
  pages = {209-213},
  keywords = {Conferences;Perovskite solar cell;photovoltaics;Polydimethylsiloxane (PDMS);Pyramids Structure;Electron Injection;Multipathing},
  doi = {10.1109/NILES.2019.8909336},
}