Yi Sun | Information Security | Best Researcher Award

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Dr. Yi Sun | Information Security | Best Researcher Award

Associate Professor at Beijing University of Posts and Telecommunications, China

Dr. Yi Sun is an Associate Professor at the School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, China. She is a prominent researcher in the field of information security with a focus on cryptography, secure multi-party computation, blockchain, and big data security. With a strong presence in international collaborations, over 150 journal publications, 170+ patent applications, and numerous honors, Dr. Sun has established herself as a global leader in cybersecurity and privacy computing.

Publication Profile 

Scopus

Educational Background 🎓

  • Ph.D. in Computer Science
    Institution: State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications
    Graduation Date: June 2015

Professional Experience 💼

  • Associate Professor
    School of Computer Science, Beijing University of Posts and Telecommunications (Since July 2015)

    • Oversees national and international research and industry projects (100+ including National 863, Key R&D, and enterprise-funded).

    • Editorial roles in reputed journals and reviewer for SCI-indexed publications.

    • Active participation in organizing and chairing major international conferences such as IEEE GLOBECOM, ICME, DSC, CAIML, CNCIS, ICAIIC, etc.

Research Interests 🔬

  • Cryptography

  • Network Security

  • Secure Multi-party Computation

  • Blockchain Technology

  • Big Data Security

  • Privacy-Preserving Computation

  • RISC-V Processor Security

Awards and Honors🏆✨

  • Top 10 Pioneers of Privacy Computing (2022)

  • Excellent Scientific Research Teacher, Ministry of Education’s Class B Laboratory

  • Excellent Instructor, 7th China International “Internet+” Innovation and Entrepreneurship Competition (2021)

  • Outstanding Instructor, C4 Network Technology Challenge (2023)

  • Excellent Paper, 2023 “Academic Achievements in Frontier IoT in the Capital”

  • 4 ESI Highly Cited Papers

  • Multiple honors for academic and research excellence

Conclusion🌟

Dr. Yi Sun’s career is marked by groundbreaking research, high-impact publications, and international collaboration. Her work in information security has led to real-world applications in secure computing and microarchitecture protection. Through her extensive publication record, editorial contributions, and successful leadership in over 100 research and industrial projects, she has played a critical role in shaping the future of cybersecurity technologies. Dr. Sun’s achievements and accolades make her a highly deserving candidate for the Best Researcher Award.

Publications 📚

📄 Article:
Title: ADPF: Anti-inference differentially private protocol for federated learning
👨‍🔬 Authors: Zhao, Zirun; Lin, Zhaowen; Sun, Yi
📚 Journal: Computer Networks
📅 Year: 2025
🔢 Citations: 0

📄 Article:
Title: A combined side-channel and transient execution attack scheme on RISC-V processors
👨‍🔬 Authors: Dong, Renhai; Cui, Baojiang; Sun, Yi; Yang, Jun
📚 Journal: Computers and Security
📅 Year: 2025
🔢 Citations: 0

📄 Article:
Title: Improved twin support vector machine algorithm and applications in classification problems
👨‍🔬 Authors: Sun, Yi; Wang, Zhouyang
📚 Journal: China Communications
📅 Year: 2024
🔢 Citations: 0

📄 Article:
Title: AdaDpFed: A Differentially Private Federated Learning Algorithm with Adaptive Noise on Non-IID Data
👨‍🔬 Authors: Zhao, Zirun; Sun, Yi; Bashir, Ali Kashif; Lin, Zhaowen
📚 Journal: IEEE Transactions on Consumer Electronics
📅 Year: 2024
🔢 Citations: 7

📄 Article:
Title: BTIDEC: A Novel Detection Scheme for CPU Security of Consumer Electronics
👨‍🔬 Authors: Dong, Renhai; Cui, Baojiang; Sun, Yi; Yang, Jun
📚 Journal: IEEE Transactions on Consumer Electronics
📅 Year: 2024
🔢 Citations: 0

📄 Article:
Title: Ins Finder: A Practical CPU Undocumented Instruction Detection Framework
👨‍🔬 Authors: Dong, Renhai; Cui, Baojiang; Sun, Yi; Yang, Jun
📚 Journal: Journal of Circuits, Systems and Computers
📅 Year: 2024
🔢 Citations: 0

📄 Article:
Title: KeyLight: Intelligent Traffic Signal Control Method Based on Improved Graph Neural Network
👨‍🔬 Authors: Sun, Yi; Lin, Kaixiang; Bashir, Ali Kashif
📚 Journal: IEEE Transactions on Consumer Electronics
📅 Year: 2024
🔢 Citations: 6

Xin Fan | Physical Layer Security | Best Researcher Award

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Assist. Prof. Dr. Xin Fan | Physical Layer Security | Best Researcher Award

Assistant Professor at Beijing Forestry University, China

Dr. Xin Fan is an Assistant Professor at the School of Information Science and Technology, Beijing Forestry University, Beijing, China. He has a strong academic background, having completed his B.E., M.E., and Ph.D. in Electronic and Information Engineering at Beijing Jiaotong University. His expertise spans wireless communications, machine learning, security and privacy, optimization, statistical signal processing, and blockchain technologies. With a visiting research experience at George Mason University, Dr. Fan has made significant contributions to edge intelligence through innovative joint optimization methods for wireless communication and airborne federated learning.

Publication Profile 

Scopus

Educational Background 🎓

  • Bachelor’s Degree (B.E.): Electronic and Information Engineering, Beijing Jiaotong University, China (2016)
  • Master’s Degree (M.E.): Electronic and Information Engineering, Beijing Jiaotong University, China (2018)
  • Doctoral Degree (Ph.D.): Electronic and Information Engineering, Beijing Jiaotong University, China (2023)
  • Visiting Ph.D. Student: Electrical and Computer Engineering, George Mason University, USA (2020-2022)

Professional Experience 💼

  • Assistant Professor: School of Information Science and Technology, Beijing Forestry University, Beijing, China.
  • Guest Editor: Electronics Journal.
  • IEEE MILCOM Technical Program Committee (TPC) Member.
  • Participated in five consultancy/industry projects and completed nine research projects.

Research Interests 🔬

  • Wireless communications
  • Machine learning
  • Security and privacy
  • Optimization
  • Statistical signal processing
  • Blockchain

Awards and Honors🏆✨

  • Nominee for the “Best Researcher Award” at the Global Innovation Technologist Awards.
  • IEEE Member and IEEE Communication Society Member.
  • CIC (China Institute of Communications) Member and CIE (Chinese Institute of Electronics) Member.

Contributions

Dr. Xin Fan has significantly advanced the field of wireless communications and machine learning. His work focuses on proposing joint optimization methods for wireless communication and airborne federated learning, promoting edge intelligence. He has authored 19 SCI-indexed journal papers and numerous conference publications in prestigious platforms such as IEEE IoT-J, IEEE TWC, IEEE TCCN, IEEE ICC, and IEEE Globecom. He has also contributed to nine patents, demonstrating his commitment to innovation.

Conclusion🌟

Dr. Xin Fan is a dedicated researcher and academic with a robust background in wireless communications and machine learning. His innovations in joint optimization and edge intelligence highlight his contributions to advancing technology in wireless communication systems. With 445 citations, editorial appointments, and memberships in prestigious professional organizations, Dr. Fan continues to make a lasting impact in his field.

Publications 📚

📄 Article in Press
Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and Networks
Li, X., Jing, T., Li, R., … Huo, Y., Yu, F.R.
📕 IEEE Transactions on Vehicular Technology, 2025
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

📘 Conference Paper
3D Physical Layer Secure Transmission for UAV-Assisted Mobile Communications Without Locations of Eavesdroppers
Yu, W., Li, J., Fan, X., … Hong, Y., Chen, T.
📕 Lecture Notes in Computer Science (LNCS), 2025, 14998 LNCS, pp. 355–366
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

🌐 Open Access Article
CB-DSL: Communication-Efficient and Byzantine-Robust Distributed Swarm Learning on Non-i.i.d. Data
Fan, X., Wang, Y., Huo, Y., Tian, Z.
📕 IEEE Transactions on Cognitive Communications and Networking, 2024, 10(1), pp. 322–334
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 4

📄 Article
UAV-Assisted Multi-Object Computing Offloading for Blockchain-Enabled Vehicle-to-Everything Systems
Chen, T., Wang, S., Fan, X., … Luo, C., Hong, Y.
📕 Computers, Materials and Continua, 2024, 81(3), pp. 3927–3950
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

📄 Article in Press
DT-Driven Computation Offloading for Edge Computing in IIoT with RIS-Assisted Multi-UAVs
Luo, C., Zhao, S., Sun, Q., … Sun, G., Zhang, L.
📕 IEEE Internet of Things Journal, 2024
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

🔒 Secure Transmission Article
Secure Transmission Scheme for Blocks in Blockchain-Based Unmanned Aerial Vehicle Communication Systems
Chen, T., Jiang, S., Fan, X., … Luo, C., Hong, Y.
📕 Computers, Materials and Continua, 2024, 81(2), pp. 2195–2217
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

📑 Article
Two-Stage Offloading for Enhancing Distributed Vehicular Edge Computing and Networks: Model and Algorithm
Li, X., Jing, T., Wang, X., … Li, X., Richard Yu, F.
📕 IEEE Transactions on Intelligent Transportation Systems, 2024, 25(11), pp. 17744–17761
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 2

🎓 Conference Paper
GANFed: GAN-Based Federated Learning with Non-IID Datasets in Edge IoTs
Fan, X., Wang, Y., Zhang, W., … Cai, Z., Tian, Z.
📕 IEEE International Conference on Communications, 2024, pp. 5443–5448
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 0

🌟 Open Access Article
Distributed Swarm Learning for Edge Internet of Things
Wang, Y., Tian, Z., Fan, X., … Nowzari, C., Zeng, K.
📕 IEEE Communications Magazine, 2024, 62(11), pp. 160–166
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 2

📚 Conference Paper
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air
Fan, X., Wang, Y., Huo, Y., Tian, Z.
📕 IEEE Transactions on Wireless Communications, 2023, 22(3), pp. 2139–2155
🔗 Abstract: [Unavailable]
🔗 Related Documents: [Unavailable]
🔢 Citations: 16

 

 

 

Zhu Yongsheng | Data Privacy Protection | Best Researcher Award

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Mr. Zhu Yongsheng | Data Privacy Protection | Best Researcher Award

Research Fellow at China Academy of Railway Sciences Corporation Limited, China

Yongsheng Zhu is a Research Fellow at China Academy of Railway Sciences Corporation Limited and a Ph.D. candidate in Engineering at Beijing Jiaotong University. With a robust background in railway information network architecture and cybersecurity defense technology, he is at the forefront of research focused on data security governance and personal information protection. He is a member of the cybersecurity expert database of China Railway Group. Yongsheng has contributed significantly to the field through various national and industry-specific projects, publications, and patents, earning recognition for his work in intelligent railway transportation and cybersecurity. 🚆🔐📚

Publication Profile : 

Scopus

Educational Background 🎓

  • Ph.D. in Engineering (Pursuing) at Beijing Jiaotong University, focusing on railway information networks and cybersecurity.
  • Master’s in Engineering (Details pending)
  • Bachelor’s in Engineering (Details pending)

Professional Experience 💼

Yongsheng Zhu currently serves as a Research Fellow at China Academy of Railway Sciences Corporation Limited. In this role, he has presided over and participated in more than 10 national and China Railway Group scientific research projects. His work primarily revolves around the security and privacy of railway communication networks. He has also contributed to major industry projects, including a systematic major project of China State Railway Group Corporation Limited. His expertise extends to guiding and advancing national cybersecurity policies, particularly in the context of IoT, federated learning, and AI-powered railway systems. 🚆💻🛡️

Research Interests 🔬

Yongsheng’s research interests focus on network data security, privacy protection, and cybersecurity defenses within the railway industry. His work involves cutting-edge topics such as federated learning, AI model security, and blockchain-enabled trust mechanisms. He is particularly interested in the development of privacy-preserving AI models, secure communication protocols for intelligent transportation systems, and the governance of data privacy in large-scale networks. 🔒📡🤖

Publications 📚

  • Zhu, Y., Liu, C., et al. (2024). Privacy-Preserving Large-Scale AI Models for Intelligent Railway Transportation Systems: Hierarchical Poisoning Attacks and Defenses in Federated Learning. Computer Modeling in Engineering & Sciences, 141(2), 1305-1325.
  • Zhu, Y., Cao, H., Cai, B., Wang, W., et al. (2023). Defending Privacy Inference Attacks to Federated Learning for Intelligent IoT with Parameter Compression. Security and Communication Networks, Vol. 2023.
  • Feng, Y., Zhong, Z., Sun, X., Wang, L., Lu, Y., & Zhu, Y. (2023). Blockchain-Enabled Zero Trust-Based Authentication Scheme for Railway Communication Networks. Journal of Cloud Computing, 12.

 

 

 

Touhid Bhuiyan | Cybersecurity | Outstanding Scientist Award

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Prof. Dr. Touhid Bhuiyan | Cybersecurity | Outstanding Scientist Award

Professor at Washington University of Science and Technology, United States

Dr. T. Bhuiyan is a distinguished academic and consultant in Cyber Security, with extensive experience in teaching, research, and industry. His contributions span several continents and include a wealth of publications and keynotes, focusing on the transformative role of technology in education and security. 🌍🔐📖

Publication Profile : 

Orcid

 

🎓 Educational Background :

Dr. T. Bhuiyan is a seasoned professional in Cyber Security, Software Engineering, and Databases, with over 27 years of experience in teaching and research across the USA, Australia, and Bangladesh. He holds a PhD in Computer Science from Queensland University of Technology, Australia, along with an MSc from The American University in London and a BSc (Hons) in Computing & Information Systems from the University of London. His education is complemented by numerous certifications, including Certified Information System Auditor (CISA) and Certified Ethical Hacker (CEH).

💼 Professional Experience :

In his professional journey, Dr. Bhuiyan has served as a Professor of Cyber Security at Washington University of Science and Technology, and previously held leadership roles at Daffodil International University and Polytechnic Institute Australia. His consulting experience includes a significant role as a National Consultant for Cyber Security with the UNDP and the Government of Bangladesh, where he led initiatives to enhance cyber security measures for government portals. Dr. Bhuiyan has delivered keynote speeches at international conferences and has authored multiple influential publications, including books on intelligent recommendation systems and cyber security. His research interests lie at the intersection of information security, trust management, and e-Learning, exploring how technology can enhance educational practices and health informatics.

📚 Research Interests : 

🔍 Information Security
🌐 Social Networks
🔒 Trust Management
💻 Database Management
🏥 e-Health
📚 e-Learning

📝 Publication Top Notes :

  1. Hossain, M.A., Rahman, M.Z., Bhuiyan, T., Moni, M.A. (2024). Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches. International Journal of Environmental Research and Public Health, 21(11), 1392.
  2. Hanip, A., Sarower, A.H., Bhuiyan, T. (2024). The Transformative Role of Generative AI in Education: Challenges and Opportunities for Enhancing Student Learning and Assessment Through Mass Integration. International Journal of Advanced Research in Engineering and Technology, 15(5), 161-175.
  3. Mahmud, A., Sarower, A.H., Sohel, A., Assaduzzaman, M., Bhuiyan, T. (2024). Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach. Array, 21, 100339.
  4. Zannah, T.B., Abdulla-Hil-Kafi, M., Sheakh, M.A., Hasan, M.Z., Shuva, T.F., Bhuiyan, T., Rahman, M.T., Khan, R.T., Kaiser, M.S., Whaiduzzaman, M. (2024). Bayesian Optimized Machine Learning Model for Automated Eye Disease Classification from Fundus Images. Computation, 12(190).
  5. Bishshash, P., Nirob, M.A.S., Shikder, M.H., Sarower, M.A.H., Bhuiyan, T., Noori, S.R.H. (2024). A Comprehensive Cotton Leaf Disease Dataset for Enhanced Detection and Classification. Data in Brief, 57, 110913.
  6. Tamal, M.A., Islam, M.K., Bhuiyan, T., Sattar, A., Prince, N.U. (2024). Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning. Frontiers in Computer Science, 6, 1428013.
  7. Tamal, M.A., Islam, M.K., Bhuiyan, T., Sattar, A. (2024). Dataset of suspicious phishing URL detection. Frontiers in Computer Science, 6, 1308634.
  8. Sholi, R.T., Sarker, …, Bhuiyan, T., Shakil, S.M.K.A., Ahmed, M.F. (2024). Application of Computer Vision and Mobile Systems in Education: A Systematic Review. International Journal of Interactive Mobile Technologies, 18(1), 168-187.
  9. Ahad, M.T., Li, Y., Song, B., Bhuiyan, T. (2023). Comparison of CNN-based deep learning architectures for rice diseases classification. Artificial Intelligence in Agriculture, 9(1), 22-35.
  10. Basar, M.A., Hosen, M.F., Paul, B.K., …, Bhuiyan, T. (2023). Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach. Informatics in Medicine Unlocked, 37, 101174.
  11. Paul, S.G., Saha, A., Arefin, M.S., Bhuiyan, T., …, Moni, M.A. (2023). A Comprehensive Review of Green Computing: Past, Present, and Future Research. IEEE Access, 11, 87445-87494.
  12. Sarker, S., Arefin, M.S., Kowsher, M., Bhuiyan, T., Dhar, P.K., Kwon, O.J. (2022). A Comprehensive Review on Big Data for Industries: Challenges and Opportunities. IEEE Access, 11, 744-769.
  13. Asaduzzaman, S., Rehana, H., Bhuiyan, T., Eid, M.M.A., Rashed, A.N.Z. (2022). Extremely high birefringent slotted core umbrella-shaped photonic crystal fiber in terahertz regime. Applied Physics B, 128(148).
  14. Bhuiyan, T. (2022). Transitioning from education to work during the 4th Industrial Revolution. AIB Review, 7, Adelaide, Australia.
  15. Ali, M.N.B., Saudi, M.M., Bhuiyan, T., Ahmad, A.B., Islam, M.N. (2021). NIPSA Intrusion Classification. Journal of Engineering Science and Technology, 16(4), 3534-3547.

 

 

 

 

Qasem Abu Al-Haija | Defensive Security | Best Researcher Award

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Dr. Qasem Abu Al-Haija | Defensive Security | Best Researcher Award

Department of Cybersecurity at Jordan University of Science and Technology, Jordan

🔍 Dr. Qasem S. Abu Al-Haija is an accomplished educator and researcher in the field of cybersecurity and IoT. With a passion for developing intelligent detection systems and efficient cryptographic methods, he strives to advance the landscape of technology. 🌐✨ His commitment to teaching and mentoring the next generation of engineers is matched only by his dedication to impactful research. 📚🔒

Publication Profile : 

Scopus

🎓 Educational Background :

Dr. Qasem S. Abu Al-Haija holds a PhD in Computer and Information Systems Engineering from Tennessee State University (2020) with a perfect GPA of 4.00. His dissertation focused on intelligent IoT attack detection using non-traditional machine learning methods. He also earned a Master’s degree in Computer Engineering from Jordan University of Science and Technology (2009), specializing in efficient algorithms for ECC cryptography, and a Bachelor’s degree in Electrical and Computer Engineering from Mu’tah University (2005).

💼 Professional Experience :

Dr. Abu Al-Haija is currently an Assistant Professor in the Department of Cybersecurity at Jordan University of Science and Technology, where he conducts research in AI and cybersecurity. Previously, he held similar positions at Princess Sumaya University for Technology and the University of Petra, teaching various cybersecurity and data science courses. His extensive experience includes postdoctoral research at Tennessee State University and a lecturer role at King Faisal University, where he taught numerous engineering courses and supervised many capstone projects. Throughout his career, he has contributed to various funded research projects and has actively engaged in academic committees and training programs.

📚 Research Interests : 

His research interests encompass machine learning, cybersecurity, IoT/CPS modeling, and embedded systems, aiming to enhance the security and efficiency of interconnected systems.

📝 Publication Top Notes :

  • Q. Abu Al-Haija, M. Al Fayoumi, “An intelligent identification and classification system for malicious uniform resource locators (URLs),” Neural Computing and Applications (NCAA), Springer, 2023.
  • Q. Abu Al-Haija, M. AlOhaly, M. Odeh, “A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach,” Sensors, MDPI, 2023.
  • Q. Abu Al-Haija, A. Al Badawi, “High-performance intrusion detection system for networked UAVs via deep learning,” Neural Computing and Applications (NCAA), Springer, 2022.
  • Q. Abu Al-Haija, A. Al Badawi, “Boost-Defense for Resilient IoT Networks: A Head-to-Toe Approach,” Expert Systems, Wiley, 2022.
  • Q. Abu Al-Haija, “Top-Down Machine Learning Based Architecture for Cyberattacks Identification and Classification in IoT Communication Networks,” Frontiers in Big Data: Cybersecurity and Privacy, Frontiers, 2022.
  • Q. Abu Al-Haija, M. Krichen, W. Abu Elhaija, “Machine-Learning-Based Darknet Traffic Detection System for IoT Applications,” Electronics, MDPI, Vol. 11(4), 2022.
  • S. Zidi, A. Mihoub, S. Qaisar, M. Krichen, Q. Abu Al-Haija, “Theft detection dataset for benchmarking and machine learning based classification in a smart grid environment,” Journal of King Saud University-Computer and Information Sciences, Elsevier, In Press, 2022.
  • Altamimi, S., Abu Al-Haija, Q. “Maximizing intrusion detection efficiency for IoT networks using extreme learning machine,” Discover Internet of Things, 2024, 4(1), 5. [Open access]
  • Alsulami, A.A., Abu Al-Haija, Q., Alturki, B., Alghamdi, B., Alsemmeari, R.A. “Exploring the efficacy of GRU model in classifying the signal to noise ratio of microgrid model,” Scientific Reports, 2024, 14(1), 15591. [Open access]
  • Abu Al-Haija, Q., Altamimi, S., AlWadi, M. “Analysis of Extreme Learning Machines (ELMs) for intelligent intrusion detection systems: A survey,” Expert Systems with Applications, 2024, 253, 124317. [Review]
  • Al-Fayoumi, M., Alhijawi, B., Al-Haija, Q.A., Armoush, R. “XAI-PhD: Fortifying Trust of Phishing URL Detection Empowered by Shapley Additive Explanations,” International Journal of Online and Biomedical Engineering, 2024, 20(11), pp. 80–101.
  • Khalil, M., Al-Haija, Q.A. “Ethical machine learning for internet of things network,” in Ethical Artificial Intelligence in Power Electronics, 2024, pp. 12–20. [Book Chapter]
  • Al-Haija, Q.A. “Preface,” in Smart and Agile Cybersecurity for IoT and IIoT Environments, 2024, pp. viii–xii. [Editorial]
  • Ayyad, W.R., Al-Haija, Q.A., Al-Masri, H.M.K. “Human factors in cybersecurity,” in Smart and Agile Cybersecurity for IoT and IIoT Environments, 2024, pp. 235–256. [Book Chapter]
  • Al-Haija, Q.A. Smart and Agile Cybersecurity for IoT and IIoT Environments, 2024, pp. 1–397. [Book]
  • Al-Tamimi, S.A., Al-Haija, Q.A. “Supply chain security, technological advancements, and future trends,” in Smart and Agile Cybersecurity for IoT and IIoT Environments, 2024, pp. 211–234. [Book Chapter]
  • Saif, A., Al-Haija, Q.A. “Artificial Intelligence (AI)-powered internet of things (IoT): Smartening Up IoT,” in Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science, 2024, pp. 18–29. [Book Chapter]

 

 

 

 

Negalign Wake Hundera | Cybersecurity | Best Researcher Award

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Assist. Prof. Dr. Negalign Wake Hundera | Cybersecurity | Best Researcher Award

Postdoctoral Researcher of Zhejiang Normal University, China

Dr. Negalign Wake Hundera is a seasoned researcher and educator with a Ph.D. in Software Engineering and extensive experience in network security and IoT technologies. His career reflects a strong commitment to advancing research and teaching in technology-driven fields. With a proven track record in publishing, guiding students, and leading technical teams, he is now seeking opportunities to further his research and contribute to innovative projects in academia or industry. 🌍🔐💻📚

Publication Profile : 

Scopus

🎓 Educational Background :

Negalign Wake Hundera completed his Ph.D. in Software Engineering at the University of Electronic Science and Technology of China (UESTC), Chengdu, in June 2021, under the guidance of Prof. Hu Xiong. Prior to this, he earned an M.Sc. in Computer Science and Technology from UESTC in 2016 and a Bachelor’s degree in Information Technology from Jimma University, Ethiopia, in 2009.

💼 Professional Experience :

Negalign’s professional journey spans roles in academia and industry. He has served as a Postdoctoral Research Fellow at Zhejiang Normal University, Jinhua, China, from August 2022 to August 2024, contributing to high-impact journals and guiding students through their research projects. Before this, he was an Assistant Professor and Lecturer at Wolkite University, Ethiopia, where he not only taught but also led significant projects related to ICT infrastructure and network security. He has also held a leadership position as the Information Communication and Network Infrastructure Team Leader at Wolkite University, overseeing the development and management of the institution’s network infrastructure.

📚 Research Interests : 

Negalign’s research focuses on several cutting-edge areas including network security, public key cryptography, information security, IoT, wireless sensor networks, cloud computing, deep learning, real-time object detection, vehicular networks, and UAV networks. His work aims to apply these technologies in practical fields such as healthcare, intelligent transportation systems, smart agriculture, and cybersecurity.

📝 Publication Top Notes :

  1. Hundera, N. W., Aftab, M. U., Mesfin, D., Xu, H., & Zhu, X. (2024). An efficient heterogeneous online/offline anonymous certificateless signcryption with proxy re-encryption for Internet of Vehicles. Vehicular Communications, 49, 100811. [Open Access]
  2. Hundera, N. W., Shumeng, W., Mesfin, D., Xu, H., & Zhu, X. (2024). An efficient online/offline heterogeneous proxy signcryption for secure communication in UAV networks. Journal of King Saud University – Computer and Information Sciences, 36(5), 102044. [Open Access]
  3. Leka, H. L., Fengli, Z., Kenea, A. T., Tohye, T. G., Tegene, A. T., & Hundera, N. W. (2023). PSO-Based Ensemble Meta-Learning Approach for Cloud Virtual Machine Resource Usage Prediction. Symmetry, 15(3), 613. [Open Access]
  4. Tohye, T. G., Qin, Z., Leka, H. L., & Hundera, N. W. (2023). Glaucoma Detection Using Convolutional Neural Network (CNN). In Proceedings of the 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP 2023).
  5. Assefa, A. A., Tian, W., Hundera, N. W., & Aftab, M. U. (2022). Crowd Density Estimation in Spatial and Temporal Distortion Environment Using Parallel Multi-Size Receptive Fields and Stack Ensemble Meta-Learning. Symmetry, 14(10), 2159. [Open Access]
  6. Hundera, N. W., Jin, C., Geressu, D. M., Olanrewaju, O. A., & Xiong, H. (2022). Proxy-based public-key cryptosystem for secure and efficient IoT-based cloud data sharing in the smart city. Multimedia Tools and Applications, 81(21), 29673–29697.
  7. Hundera, N. W., Jin, C., Aftab, M. U., Mesfin, D., & Kumar, S. (2021). Secure outsourced attribute-based signcryption for cloud-based Internet of Vehicles in a smart city. Annales des Telecommunications/Annals of Telecommunications, 76(9-10), 605–616.
  8. Leka, H. L., Fengli, Z., Kenea, A. T., Atandoh, P., & Hundera, N. W. (2021). A Hybrid CNN-LSTM Model for Virtual Machine Workload Forecasting in Cloud Data Center. In Proceedings of the 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP 2021), 474–478.
  9. Hundera, N. W., Mei, Q., Xiong, H., & Geressu, D. M. (2020). A secure and efficient identity-based proxy signcryption in cloud data sharing. KSII Transactions on Internet and Information Systems, 14(1), 455–472.
  10. Oluwasanmi, A., Akeem, S., Jehoaida, J., Baagere, E., Qin, Z., & Hundera, N. W. (2019). Sequential multi-kernel convolutional recurrent network for sentiment classification. In Proceedings of the IEEE International Conference on Software Engineering and Service Sciences (ICSESS), 129–133.