Assist. Prof. Dr ISMAIL NEGABI | Embedded Cryptographic Systems and Artificial Intelligence | Best Researcher Award

Education 🎓

Ismail Negabi earned his Doctorate in 2023 from Université Abdelmalek Essaâdi, specializing in Embedded Cryptographic Systems and Artificial Intelligence. He completed his Master’s in Signal Processing and Automatic Learning in 2019 and his Bachelor’s in Physical Sciences in 2017, both from Moroccan universities. His academic journey began with a Baccalaureate in Physical and Chemical Sciences in 2011. Throughout his education, Ismail has consistently achieved high honors, including a “Très Honorable” distinction for his doctoral thesis.

Experience 💼

Ismail has a diverse professional background, including roles as a primary school teacher since 2020 and a reviewer for the International Journal of Computer Systems and Digital Systems. He has also worked as a trainer for the General Population and Housing Census in Morocco and completed internships in algorithm development and FPGA-based cryptographic systems. His experience spans teaching, research, and practical applications of AI and cryptography.

Awards and Honors 🏆

Ismail Negabi has received numerous accolades, including the “Très Honorable avec Félicitations” distinction for his doctoral thesis. His research has been published in prestigious journals indexed in Scopus, and he has presented at international conferences. Ismail’s contributions to AI and cryptography have earned him recognition as a promising researcher in his field.

Research Focus 🔍

Ismail’s research focuses on enhancing the security of cryptographic systems using deep learning techniques, particularly against side-channel attacks. He explores the integration of AI with encryption methods like AES to improve embedded system security. His work also extends to applications of deep learning in remote sensing, such as building extraction from satellite images, and anomaly detection for connected objects.

Publication Top Notes 📚

1. “Building extraction from remote sensing imagery: advanced squeeze-and-excitation residual network based methodology”
2. “Deep Learning-based Power Analysis Attack for Extracting Advanced Encryption Standard Keys on Microcontroller”
3. “Convolutional neural network based key generation for security of data through encryption with AES”
4. “Enhancing Building Extraction from Remote Sensing Images through UNet and Transfer Learning”
5. “A Modular System based on U-Net for Automatic Building Extraction from very high-resolution satellite images”
6. “Enhancing Cryptosystem Security with a CNN-based Countermeasure against Power Analysis Attacks”
7. “Beyond Encryption: How DL Can Break Microcontroller Security Through Power Analysis”
8. “Encryption of Sensitive Data by Intelligent Cryptosystem based on AES and CNN for Embedded Systems Security”
9. “Combining Convolutional Neural Network with Feature Extraction for Automatic Building Extraction”
10. “Towards an Intelligent Cryptosystem Design: Deep Learning Cryptography for Embedded Systems Security”

Conclusion 🌟

Ismail Negabi is a highly accomplished researcher and educator with a passion for advancing the fields of AI and cryptography. His innovative work on deep learning-based cryptographic security and remote sensing applications has made significant contributions to the academic and professional communities. With a strong foundation in education, diverse professional experience, and a commitment to excellence, Ismail continues to inspire and lead in his field.