Huda Ahmed | Networks Security | Innovations in Mobile Security

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Dr. Huda Ahmed | Networks Security | Innovations in Mobile Security

Lecturer at University of Basrah/ College of Computer Science and Information Technology, Iraq.

Dr. Huda Ahmed is an accomplished computer scientist specializing in artificial neural networks, with a focus on mobile and network security innovations. She earned her B.Sc. in Computer Science in 1997 and her M.Sc. in 2001 from Basrah University, Iraq, before completing her Ph.D. in 2024 at Kufa University, Iraq. Dr. Ahmed’s research spans image processing, multi-wavelet techniques, and the development of efficient routing protocols for mobile ad hoc networks (MANETs). Her work includes impactful contributions to the fields of image segmentation, video packet transmission, and blockchain-based trust management for secure routing. In addition to her research, Dr. Ahmed has extensive teaching experience in undergraduate computer science, delivering courses on computer networks, computer graphics, and image processing. She has been recognized for her innovative approaches to mobile security and her commitment to advancing network efficiency and data security.

Profile:

Education:

Dr. Huda Ahmed has an extensive academic background in computer science, with a focus on neural networks and their applications. She obtained her Bachelor of Science (B.Sc.) degree in Computer Science from Basrah University, Iraq, in 1997. She continued her studies at the same institution, completing her Master of Science (M.Sc.) degree in 2001, further deepening her knowledge in the field. Dr. Ahmed is currently finalizing her doctoral studies, having earned her Ph.D. from Kufa University, Iraq, in 2024, specializing in artificial neural networks. Her rigorous academic journey has laid a strong foundation for her research and teaching roles in advanced computing and network security.

Professional Experience:

Dr. Huda Ahmed has extensive professional experience in academia and research, particularly in computer science and network security. With a teaching career spanning several years, she has delivered undergraduate courses in core computer science subjects, including Computer Networks, Computer Simulation, Graphics, and Image Processing. Dr. Ahmed’s research expertise is focused on developing innovative solutions in artificial neural networks and mobile security, where she has conducted influential studies on secure and efficient data transmission in mobile ad hoc networks (MANETs). She is also actively involved in applying blockchain frameworks to improve trust management in network protocols, contributing to enhanced mobile and network security. Dr. Ahmed’s dedication to both research and teaching has established her as a leader in her field, recognized for bridging academic theory with practical applications in mobile security.

Research Interests:

Dr. Huda Ahmed’s research interests lie at the intersection of artificial intelligence, network security, and mobile communication systems. Her primary focus is on the development and optimization of artificial neural networks, with particular applications in image processing, biometric recognition, and mobile ad hoc networks (MANETs). She is dedicated to enhancing mobile security frameworks, utilizing blockchain-enabled trust management systems for secure and energy-efficient data transmission. Additionally, Dr. Ahmed explores routing protocol performance within wireless multimedia sensor networks, aiming to improve data integrity and transmission efficiency across mobile networks. Her work also encompasses advanced image compression techniques and the application of multi-wavelet approaches, making significant strides in both computational efficiency and data security.

Skills:

Dr. Huda Ahmed possesses a diverse set of skills across multiple domains in computer science and network security. She is proficient in artificial neural network design and optimization, with a strong command of genetic algorithms for pattern recognition and classification tasks. Dr. Ahmed has expertise in mobile and wireless network protocols, specifically in mobile ad hoc networks (MANETs) and wireless multimedia sensor networks, where she excels in evaluating and enhancing routing protocols for efficient data transmission. Her skill set also includes blockchain frameworks for secure and energy-efficient routing, as well as advanced image processing techniques such as multi-wavelet compression and image segmentation. Additionally, Dr. Ahmed is well-versed in biometric systems, particularly in fingerprint documentation and matching, and has a solid foundation in computer graphics and simulation. Her programming capabilities support her extensive research in developing innovative solutions for mobile security and network resilience.

Conclusion:

Dr. Huda Ahmed’s expertise in neural networks, image processing, secure routing protocols, and blockchain-based trust management frameworks is aligned with the evolving needs in mobile security research. Her contributions in secure network architecture and data integrity frameworks position her as an ideal candidate for the Research for Innovations in Mobile Security Award.

Publication Top Noted:

An Optimized Link State Routing Protocol with a Blockchain Framework for Efficient Video-Packet Transmission and Security over Mobile Ad-Hoc Networks

  • Authors: HA Ahmed, HAA Al-Asadi
  • Journal: Journal of Sensor and Actuator Networks
  • Volume: 13 (2), Article 22
  • Cited by: 5
  • Year: 2024

Compression of Image Using Multi-Wavelet Techniques

  • Authors: HAA Saba Abdul-Wahed, Marwah Kamil Hussien
  • Journal: International Journal of Nonlinear Analysis and Applications
  • Volume: 13 (1), Pages 1519-1535
  • Cited by: 2
  • Year: 2022

An Overview of Routing Protocols Performance in Wireless Multimedia Sensor Networks

  • Authors: HA Ahmed, HAA Al-Asadi
  • Conference: 3rd Information Technology to Enhance e-Learning and Other Applications
  • Cited by: 1
  • Year: 2022

A Blockchain-Enabled Trust Management Framework for Energy-Efficient and Secure Routing in Mobile Ad-Hoc Networks

  • Authors: HA Ahmed, HA Abed Al-Asadi
  • Journal: TEM Journal
  • Volume: 13 (2)
  • Year: 2024

Performance Evaluation of MANETs Routing Protocols for Transmitting Video

  • Authors: HA Ahmed, HAA Al-Asadi
  • Journal: Journal of Basrah Research (Science)
  • Volume: 49 (2), Pages 124-139
  • Year: 2023

A Tri-Classes Method for Studying the Impact of Nodes and Sinks Number on Received Packets Ratio of MANETs Routing Protocols

  • Authors: HA Ahmed, HAA Al-Asadi
  • Conference: 15th International Conference on Developments in eSystems Engineering
  • Year: 2023

A Novel and Enhanced Routing Protocol for Large Scale Disruption Tolerant Mobile Ad Hoc Networks

  • Authors: Halia Al-Asadi, HA Ahmed, AH Al-Hassani, Nama Hambali
  • Year: 2022

A Novel and Enhanced Routing Protocol for Large Scale Disruption Mobile Ad Hoc Networks

  • Authors: Namah Hamid Ali Abed Al-Asadi, Huda A. Ahmed, Abdul-Hadi Al-Hassani
  • Journal: International Journal of Computing
  • Year: 2022

Boquan Li | Cyber Threat | Best Researcher Award

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Dr. Boquan Li | Cyber Threat | Best Researcher Award

Assistant Professor at College of Computer Science and Technology, Harbin Engineering University, China

Dr. Boquan Li is a Tenure Track Associate Professor at the College of Computer Science and Technology, Harbin Engineering University, where he has served since January 2024. Prior to this, he was a Research Scientist at the School of Computing and Information Systems, Singapore Management University, from April 2022 to December 2023. Dr. Li holds a Ph.D. in Information Engineering from the University of Chinese Academy of Sciences and a Bachelor of Engineering from Harbin Engineering University. His research interests focus on artificial intelligence, cybersecurity, deepfake detection, and speaker recognition, with numerous publications in leading international conferences and journals. Dr. Li is also an active peer reviewer for prestigious journals like IEEE Transactions on Software Engineering.

Profile:

Education:

Dr. Boquan Li holds a Doctor of Philosophy (Ph.D.) from the University of Chinese Academy of Sciences, where he specialized in Information Engineering at the Institute of Information Engineering. He completed his Ph.D. in January 2022, building a strong foundation in artificial intelligence, cybersecurity, and data science. Prior to his doctoral studies, Dr. Li earned a Bachelor of Engineering degree from the School of Software at Harbin Engineering University in June 2016. His comprehensive academic background has equipped him with expertise in cutting-edge technologies, enabling him to contribute significantly to research in AI and cybersecurity.

Professional Experience:

Dr. Boquan Li has a diverse professional background in both academia and research. Since January 2024, he has been serving as a Tenure Track Associate Professor at the College of Computer Science and Technology, Harbin Engineering University, where he contributes to teaching and research in artificial intelligence and cybersecurity. Prior to this role, Dr. Li worked as a Research Scientist at the School of Computing and Information Systems, Singapore Management University, from April 2022 to December 2023. In this capacity, he was involved in cutting-edge research on deepfake detection, speaker recognition, and digital forensics. His professional experience highlights his expertise in developing innovative solutions to cybersecurity challenges and advancing research in AI-driven technologies.

Research Interests:

Dr. Boquan Li’s research interests focus on cutting-edge areas of artificial intelligence, cybersecurity, and multimedia forensics. He is particularly interested in deepfake detection, where he explores the vulnerabilities and robustness of detection systems across various domains. His work also covers speaker recognition, digital forensics, and adversarial attacks, aiming to develop defense mechanisms against cyber threats. Additionally, Dr. Li has a strong interest in cross-modal fusion techniques, particularly in audio-visual speech recognition, and domain adaptation methods for enhancing the accuracy of AI models across diverse datasets. His research contributes to advancing secure and reliable AI systems.

Skills:

Dr. Boquan Li possesses a diverse skill set that encompasses advanced computational techniques and a robust understanding of artificial intelligence and machine learning algorithms. He is proficient in developing and implementing deep learning models, particularly for applications in image and audio processing. His expertise extends to cybersecurity measures, with a focus on identifying vulnerabilities in AI systems and creating effective defense strategies against adversarial attacks. Additionally, Dr. Li is skilled in data analysis and statistical methods, enabling him to interpret complex datasets and derive meaningful insights. His strong programming skills in languages such as Python and proficiency with machine learning frameworks like TensorFlow and PyTorch further enhance his research capabilities in the field of computer science and technology.

Conclusion:

Dr. Boquan Li’s research addresses critical issues in AI security, deepfake detection, and adversarial defenses, areas of increasing importance in today’s technological landscape. His innovative work, combined with his academic and research experience, positions him as a strong candidate for the Best Researcher Award. His contributions have practical applications in cybersecurity and AI ethics, demonstrating both academic excellence and real-world impact.

Publication Top Noted:

  • How Generalizable are Deepfake Image Detectors? An Empirical Study
  • Two-stage Semi-supervised Speaker Recognition with Gated Label Learning
    • Authors: Xingmei Wang, Jiaxiang Meng, Kong Aik Lee, Boquan Li, Jinghan Liu
    • Year: 2024
    • Conference: International Joint Conference on Artificial Intelligence
    • Type: Conference paper
  • Assessing Backdoor Risk in Deepfake Detectors
    • Authors: Jiawen Wang, Boquan Li, Min Yu, Kam-Pui Chow, Jianguo Jiang, Fuqiang Du, Xiang Meng, Weiqing Huang
    • Year: 2024
    • Conference: IFIP WG 11.9 International Conference on Digital Forensics
    • Type: Conference paper
  • CATNet: Cross-Modal Fusion for Audio–Visual Speech Recognition
    • Authors: Xingmei Wang, Jiachen Mi, Boquan Li, Yixu Zhao, Jiaxiang Meng
    • Year: 2024
    • Journal: Pattern Recognition Letters
    • DOI: 10.1016/j.patrec.2024.01.002
  • A Residual Fingerprint-Based Defense Against Adversarial Deepfakes
  • FakeFilter: A Cross-Distribution Deepfake Detection System with Domain Adaptation
    • Authors: Jianguo Jiang, Boquan Li, Baole Wei, Gang Li, Chao Liu, Weiqing Huang, Meimei Li, Min Yu
    • Year: 2021
    • Journal: Journal of Computer Security
    • DOI: 10.3233/jcs-200124
  • Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection
    • Authors: Jianguo Jiang, Boquan Li, Min Yu, Chao Liu, Weiqing Huang, Lejun Fan, Jianfeng Xia
    • Year: 2019
    • Conference: International Conference on Artificial Neural Networks
    • DOI: 10.1007/978-3-030-30508-6_56