Michael Adekunle Adelere | Computer Science | Best Researcher Award

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Best Researcher Award

Michael Adekunle Adelere
University of New Brunswick, Canada

Michael Adekunle Adelere
Affiliation University of New Brunswick
Country Canada
Google Scholar ID QTQZo5wAAAAJ
Documents 7
Citations 61
h-index 3
Subject area Computer Science
Event Global Innovation Technologist Awards
ORCID 0000-0001-6389-7909

The Best Researcher Award recognizes research contributions, scholarly productivity, and potential impact within a defined academic field. This profile presents Michael Adekunle Adelere in connection with computer science and research addressing cybersecurity knowledge representation, threat modeling, and risk reasoning. The information combines the supplied bibliometric indicators with two identified RiskOnto research outputs. Publication details and metrics should be interpreted according to their source and verification date. [1]

Abstract

This article outlines a research profile associated with Michael Adekunle Adelere and the Best Researcher Award. The supplied profile records seven documents, 61 citations, and an h-index of 3. The identified RiskOnto works concern ontology-driven cybersecurity frameworks for organizing threat models, reasoning about risk, and integrating security knowledge. These themes connect knowledge engineering with practical cybersecurity analysis. The profile is descriptive rather than an independent audit of research performance. [2]

Keywords

Computer science; cybersecurity; ontology engineering; threat modeling; risk reasoning; NIST Cybersecurity Framework; knowledge integration; research impact; scholarly evaluation.

Introduction

Cybersecurity research increasingly requires structured methods for connecting threats, vulnerabilities, risks, and defensive controls. Ontologies can represent relationships among these concepts and support consistent interpretation across security tools and workflows. RiskOnto is presented in the supplied publication information as a framework addressing these challenges. The award context provides an opportunity to consider the relevance, scholarly documentation, and potential practical value of this research direction. [3]

Research Profile

The supplied information associates Adelere with the University of New Brunswick in Canada and identifies computer science as the principal subject area. The reported bibliometric indicators offer a limited quantitative snapshot: seven documents, 61 citations, and an h-index of 3. These figures may vary across scholarly databases and over time. The supplied identifier and profile URL should be checked against authoritative researcher records before formal publication.

Research Contributions

The RiskOnto research direction focuses on representing cybersecurity concepts in a structured form that can support threat modeling and risk analysis. Its stated objectives include connecting security knowledge and aligning risk reasoning with recognized cybersecurity practices. Potential applications include documenting threats, examining relationships among risks and controls, and improving consistency in security assessments. These are research objectives, not evidence that every proposed capability has been independently validated in operational settings. [4]

Publications

  1. “RiskOnto: An ontology-driven framework for threat modeling, risk reasoning and cyber defense knowledge integration.” Computers & Security, listed publication date February 2027. [4]
  2. “RiskOnto 1.0: An Ontology-Driven Framework for Threat Modeling and Risk Reasoning Using the NIST Cybersecurity Framework.” Preprint, 2026. [5]

Research Impact

The reported 61 citations suggest that the indexed work has received scholarly attention, while an h-index of 3 describes a particular distribution of cited publications. Neither measure alone establishes research quality, originality, reproducibility, or real-world adoption. A fuller assessment would examine publication venues, independent validation, subsequent research uptake, and contributions to cybersecurity practice.

Award Suitability

The profile provides material relevant to consideration for the Best Researcher Award under the Global Innovation Technologist Awards. Relevant assessment areas include:

  • Research relevance to current cybersecurity challenges.
  • Documented scholarly output and citation indicators.
  • Potential contribution of ontology-based risk reasoning.
  • Verification of authorship, publication status, and research outcomes.

These considerations support an evidence-based review but do not independently establish an award decision.

Conclusion

Michael Adekunle Adelere’s supplied research profile highlights computer science scholarship and RiskOnto-related work in cybersecurity knowledge integration. The bibliometric figures and publication records provide starting points for evaluating scholarly activity. Verification of the underlying records and assessment of research quality remain important for a balanced award review.

References

  1. ORCID. Michael Adekunle Adelere researcher identifier.
    https://orcid.org/0000-0001-6389-7909
  2. National Institute of Standards and Technology. Cybersecurity Framework.
  3. “RiskOnto: An ontology-driven framework for threat modeling, risk reasoning and cyber defense knowledge integration.” Computers & Security.
    https://doi.org/10.1016/j.cose.2026.105223.
  4. “RiskOnto 1.0: An Ontology-Driven Framework for Threat Modeling and Risk Reasoning Using the NIST Cybersecurity Framework.” Preprint, 2026.
    https://doi.org/10.2139/ssrn.6465624.
  5. Google Scholar. Research profile supplied for Michael Adekunle Adelere.
    https://scholar.google.com/citations?user=QTQZo5wAAAAJ&hl=en&oi=ao
  6. Global Innovation Technologist Awards. Official website.
    innovationtechnologist.com

Ying Tan | Computer Science | Distinguished Scientist Award

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Distinguished Scientist Award

Ying Tan
Peking University, China

                      Ying Tan
Affiliation Peking University
Country China
Scopus ID 56098810800
Documents 359
Citations 8,578
h-index 46
Subject Area Computer Science
Event Global Innovation Technologist Awards
ORCID 0000-0001-8243-4731

The Distinguished Scientist Award profile recognizes Ying Tan’s reported scholarly record in computer science and its relevance to computational intelligence, optimization, and machine learning. Associated publication information includes research on fireworks algorithms, neural network optimization, and time-series prediction. The profile presents bibliometric indicators alongside selected publications to support an informed assessment of research activity. Award recognition remains subject to the event’s applicable evaluation and verification procedures.

Abstract

This article summarizes the supplied research profile of Ying Tan, affiliated with Peking University, China. The reported Scopus metrics comprise 359 documents, 8,578 citations, and an h-index of 46. Selected publications address optimization algorithms, neural networks, and sequence-based prediction. These topics connect computational methods with challenges in efficient model training and data-driven forecasting. The profile provides a structured basis for reviewing publication activity and research visibility, while acknowledging that bibliometric indicators alone cannot establish the originality or practical value of individual contributions. [1]

Keywords

Computer science; computational intelligence; swarm intelligence; fireworks algorithm; neural network optimization; contrastive learning; time-series prediction; bibliometrics; research evaluation.

Introduction

Computer science research increasingly combines optimization strategies, machine learning architectures, and intelligent computational systems. Research evaluation in this environment benefits from considering both measurable scholarly activity and the substance of published work. Ying Tan’s supplied profile and publication records provide examples of research addressing algorithmic search and predictive modeling. The Distinguished Scientist Award context offers an opportunity to assess this work through transparent criteria rather than relying exclusively on citation totals. [2]

Research Profile

The supplied profile identifies Peking University as the institutional affiliation and computer science as the subject area. Reported bibliometric indicators are 359 documents, 8,578 citations, and an h-index of 46. These figures describe the provided record and should be checked against the live Scopus profile before formal publication, since indexing and citation counts can change over time. [1]

Research Contributions

The supplied publication titles indicate several related research directions:

  • Developing fireworks-based optimization methods and investigating their enhancement through multimodal large language models.
  • Exploring contrastive learning to improve selectivity in Mamba-based time-series prediction.
  • Implementing multi-guiding spark fireworks algorithms on GPUs for black-box neural network optimization.

Together, these topics concern search efficiency, predictive modeling, and computational implementation. Their specific contributions should be assessed from the full papers and experimental results. [3]

Publications

  1. “Visual-Enhanced Fireworks Algorithm via multimodal large language models.” Swarm and Evolutionary Computation, listed publication date November 2026. [3]
  2. “Repetitive contrastive learning enhances Mamba’s selectivity in time series prediction.” Neural Networks, April 2026. [4]
  3. “A GPU Implementation of Multi-guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network Optimization.” Book chapter, 2026.. [5]

The first item’s supplied publication date is November 2026, which is later than the current date of 27 October 2026; it should therefore be treated as forthcoming or scheduled unless the publisher confirms otherwise.

Research Impact

The reported citation count and h-index suggest an established indexed publication record. However, citation indicators vary by discipline, publication age, co-authorship, and database coverage. A balanced assessment should also examine methodological soundness, reproducibility, independent uptake, and the significance of individual findings. DOI records provide stable routes for verifying publication metadata and locating the associated works. [6]

Award Suitability

The profile presents evidence relevant to consideration for a Distinguished Scientist Award, including sustained publication activity, citation visibility, and research spanning optimization and machine learning. A complete award assessment should independently verify affiliation, authorship, publication status, and bibliometric data, and evaluate originality and research quality against the award’s published criteria. This profile does not itself establish an award decision.

Conclusion

Ying Tan’s supplied research profile combines substantial reported bibliometric indicators with publications addressing computational optimization and predictive learning. These details provide a useful starting point for scholarly recognition, provided that publication records and metrics are verified and qualitative research assessment remains central.

References

  1. Elsevier. (n.d.). Scopus author details: Ying Tan, Author ID 56098810800. Scopus. Profile metrics are reported from the supplied input and require current verification.
    https://www.scopus.com/authid/detail.uri?authorId=56098810800
  2. ORCID. (n.d.). Ying Tan researcher identifier. ORCID record.
    https://orcid.org/0000-0001-8243-4731
  3. Elsevier. (2026). Visual-Enhanced Fireworks Algorithm via multimodal large language models. Swarm and Evolutionary Computation. DOI: 10.1016/j.swevo.2026.102577.
    https://doi.org/10.1016/j.swevo.2026.102577
  4. Elsevier. (2026). Repetitive contrastive learning enhances Mamba’s selectivity in time series prediction. Neural Networks. DOI: 10.1016/j.neunet.2025.108290.
    https://doi.org/10.1016/j.neunet.2025.108290
  5. Springer Nature. (2026). A GPU Implementation of Multi-guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network Optimization. Book chapter. DOI: 10.1007/978-981-95-0982-9_5.
    https://doi.org/10.1007/978-981-95-0982-9_5
  6. Global Innovation Technologist Awards. (n.d.). Official awards website. Award information and applicable recognition criteria should be consulted directly.
    innovationtechnologist.com

Jingpei Wang | Network Security | Research Excellence Award

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Dr. Jingpei Wang | Network Security | Research Excellence Award

Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences | China

Dr. Jingpei Wang is a researcher in space and industrial cyber-physical security with a focus on trusted computing, satellite network security, and industrial network protection. According to Scopus, he has authored 27 publications, received 98 citations, and holds an h-index of 6. His scholarly output includes journal articles and conference papers addressing secure data access, trusted transmission mechanisms, and defenses against false data injection attacks in smart grids and space information networks. He is also an inventor on nine patents, reflecting strong translational impact from theory to engineering practice. His research integrates security architecture design, threat modeling, and applied cryptographic and trusted-platform techniques for next-generation networked systems.

                            Citation Metrics (Scopus)

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98
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Documents

h-index

View Scopus Profile  View ACM Digital Library

Featured Publications

Andrei Kojukhov | Cybersecurity | Research Excellence Award

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Dr. Andrei Kojukhov | Cybersecurity | Research Excellence Award

Senior Lecturer | Holon Institute of Technology | Israel

Dr. Andrei Kojukhov is a computer science researcher specializing in artificial intelligence, AI-driven education, advanced networking, and intelligent system architectures. His scholarly work integrates generative AI, personalized and ubiquitous learning, error correction in memory systems, and cybersecurity for next-generation digital infrastructures. He has published peer-reviewed research that bridges theoretical innovation and applied technology, particularly at the intersection of AI and education. His research impact is reflected in 114 total citations (85 since 2020), an h-index of 6 (5 since 2020), and an i10-index of 5, demonstrating sustained academic influence and relevance in contemporary AI, networking, and educational technology research domains.

Citation Metrics (Google Scholar)

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h-index 6

i10-index 5
                   🟦 Citations        🟩 h-index        🟥 i10-index


View Google Scholar Profile

Featured Publications

Kavuri Satheesh | Cryptography | Best Researcher Award

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Dr. Kavuri Satheesh | Cryptography | Best Researcher Award

Vasireddy Venkatadri International Technological University | India 

Dr. Kavuri K. S. V. A. Satheesh is an accomplished academician and researcher with over two decades of experience in the field of computer science and engineering. His passion for teaching and research has led to significant contributions in cloud computing, blockchain technology, and network security. He has consistently demonstrated excellence in both teaching and research, publishing numerous papers in reputed journals and participating in conferences.

Publication Profile 

Scopus

Google Scholar

Educational Background 

He holds a Ph.D. in Computer Science and Engineering, along with postgraduate and undergraduate degrees in computer science. Additionally, he completed an M.Tech. in Computer Science and Engineering, establishing a strong academic foundation that supports his teaching and research expertise.

Professional Experience 

With a career spanning more than 25 years, he has served in various academic roles ranging from Lecturer to Professor in reputed engineering institutions. His responsibilities have included mentoring postgraduate and engineering students, delivering lectures on advanced computer science topics, and guiding research projects.

Research Interests 

His primary research interests lie in cloud computing, blockchain technology, and network security. He has explored innovative solutions to enhance security in cloud environments and developed models for authentication, encryption, and secure data management. His work also extends to artificial intelligence, machine learning, and their integration with secure computing models.

Awards and Honors 

He has published extensively in SCI and Scopus-indexed journals, authored a book on network and system security, and contributed chapters to reputed publications. Additionally, he holds patents related to cryptographic systems and data security techniques, reflecting his commitment to technological innovation and applied research.

Research Skills 

Dr. Satheesh possesses strong expertise in encryption models, secure authentication systems, data access control, and cryptographic techniques for cloud environments. His technical skills span programming languages, algorithm design, and security protocol development. He has also participated in several faculty development programs and advanced workshops on machine learning, blockchain, and cloud computing.

Publications 

  1. Title: Data authentication and integrity verification techniques for trusted/untrusted cloud servers
    Citations: 20
    Year: 2014

  2. Title: Resource allocation method using scheduling methods for parallel data processing in cloud
    Citations: 16
    Year: 2012

  3. Title: An improved integrated hash and attributed based encryption model on high dimensional data in cloud environment
    Citations: 9
    Year: 2017

  4. Title: A Novel Hardware Parameters Based Cloud Data Encryption and Decryption Against Un-Authorized Users
    Citations: 5
    Year: 2016

  5. Title: Cryptographic access control schemes in cloud storage services
    Citations: 5
    Year: 2015

Conclusion 

Dr. Kavuri K. S. V. A. Satheesh is a dedicated educator and researcher committed to advancing knowledge in computer science and engineering. His extensive teaching experience, impactful research, and innovative contributions to security in cloud and distributed systems underscore his suitability for academic leadership and research-driven roles in higher education and industry collaborations.

Kyung-Hyune Rhee | Information Security | Best Researcher Award

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Prof. Dr. Kyung-Hyune Rhee | Information Security | Best Researcher Award

Professor Emeritus at Pukyong National University, South Sudan

Professor Kyung-Hyune Rhee is a renowned academic and researcher in computer engineering, specializing in information security and blockchain applications. As Professor Emeritus at Pukyong National University, he has led critical advancements in cybersecurity, policy development, and technology education in South Korea. With a strong foundation in scientific research and government advisory roles, Professor Rhee has made substantial contributions to both academia and national cyber policy.

Publication Profile 

Scopus

Educational Background 🎓

  • Master of Science (M.S.), KAIST (Korea Advanced Institute of Science and Technology)

  • Doctor of Philosophy (Ph.D.), KAIST (Korea Advanced Institute of Science and Technology)

Professional Experience 💼

Current Positions

  • Professor, Department of IT Convergence and Application Engineering, Pukyong National University

  • Committee Member, ISMS/ISMS-P Certification

  • Evaluation Member, National Cyber Security Center

  • Advisory Member, Southeast Information Security Support Center

  • Member, Busan Metropolitan City Special Committee on the 4th Industrial Revolution

  • Advisory Member, Defense Security Research Institute

  • Policy Advisor, Korea Multimedia Society

  • Expert Member, Future Security Forum

Former Positions

  • President, Korea Institute of Information Security and Cryptology

  • Non-Executive Director, Korea Education and Research Information Service

  • Committee Member, Busan Metropolitan City Information Committee

  • Dean of Academic Affairs, CPSC (Philippine-based Intergovernmental Organization)

  • Director, Information & Computing Center, Pukyong National University

  • Visiting Professor, University of California, Irvine

  • Senior Researcher, Electronics and Telecommunications Research Institute (ETRI)

Research Interests 🔬

  • Computer Engineering

  • Information Security

  • Blockchain Applications

  • Cybersecurity Policy and Certification

  • Cryptographic Protocols and Network Security

Awards and Honors🏆✨

  • July 2015: Commendation from the Minister of Science, ICT and Future Planning (for strengthening cybersecurity capabilities)

  • November 2018: Commendation from the Minister of the Interior and Safety (for contributions to security certification)

  • July 2021: Order of Service Merit (Geunjeong Medal) – awarded on Information Security Day

Conclusion🌟

Professor Kyung-Hyune Rhee has established himself as a pivotal figure in South Korea’s digital transformation, particularly in the development of national cybersecurity strategies and the integration of blockchain technologies. His extensive academic output—over 450 research papers, 25+ books, and 20 patents—reflects a career dedicated to both scientific excellence and public service. With ongoing influence through advisory roles and policy development, he continues to contribute significantly to the global information security community.

Publications 📚

  1. 🧩 Hyperledger Fabric-Powered Network Slicing Handover Authentication
     I.T., Etienne Igugu Tshisekedi · M., Firdaus Muhammad · C.N.Z., Latt Cho Nwe Zin · S., Noh Siwan · K., Rhee Kyunghyune
     📚 Information Systems Frontiers, 2024
     🔗 0 Citations

  2. 🧠 Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems
     S., Noh Siwan · K., Rhee Kyunghyune
     📚 Computers, Materials and Continua, 2024
     🔗 0 Citations

  3. 🔒 A Blockchain-Based Auditable Semi-Asynchronous Federated Learning for Heterogeneous Clients
     Q., Zhuohao Qian · M., Firdaus Muhammad · S., Noh Siwan · K., Rhee Kyunghyune
     📚 IEEE Access, 2023
     🔗 3 Citations

  4. 🚗 A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks
     M., Firdaus Muhammad · H.T., Larasati Harashta Tatimma · K., Rhee Kyunghyune
     📚 Computers, Materials and Continua, 2023
     🔗 4 Citations

  5. 🤖 Personalized Federated Learning for Heterogeneous Data: A Distributed Edge Clustering Approach
     M., Firdaus Muhammad · S., Noh Siwan · Z., Qian Zhuohao · H.T., Larasati Harashta Tatimma · K., Rhee Kyunghyune
     📚 Mathematical Biosciences and Engineering, 2023
     🔗 7 Citations

  6. 💬 Commentary: Integrated Blockchain–Deep Learning Approach for Analyzing the Electronic Health Records Recommender System
     S., Noh Siwan · M., Firdaus Muhammad · K., Rhee Kyunghyune
     📚 Note (Open Access)
     🔗 0 Citations

  7. 🏥 Secure Federated Learning with Blockchain and Homomorphic Encryption for Healthcare Data Sharing
     M., Firdaus Muhammad · K., Rhee Kyunghyune
     🔗 0 Citations

  8. 👥 Towards Trustworthy Collaborative Healthcare Data Sharing
     M., Firdaus Muhammad · K., Rhee Kyunghyune
     🔗 3 Citations

  9. 🧾 A Blockchain-Based CCP Data Integrity Auditing Protocol for Smart HACCP
     S., Noh Siwan · M., Firdaus Muhammad · Z., Qian Zhuohao · K., Rhee Kyunghyune
     🔗 1 Citation

  10. 🧠 BPFL: Blockchain-Enabled Distributed Edge Cluster for Personalized Federated Learning
     M., Firdaus Muhammad · S., Noh Siwan · Z., Qian Zhuohao · K., Rhee Kyunghyune
     🔗 6 Citations

 

 

 

 

Stefano Galantucci | Cybersecurity | Best Researcher Award

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Dr. Stefano Galantucci | Cybersecurity | Best Researcher Award

Adjunct Professor (Lecturer) at University of Bari Aldo Moro, Italy

Dr. Stefano Galantucci is a cybersecurity researcher and academic affiliated with the University of Bari Aldo Moro, Italy. Born on October 28, 1996, he holds a Ph.D. in Computer Science and specializes in network security, cryptography, malware detection, and firewall optimization. With extensive experience in teaching, research, and publication, Dr. Galantucci is an active contributor to both academic and applied cybersecurity communities.

Publication Profile 

Scopus

Orcid

Educational Background 🎓

  • 2024 – Ph.D. in Computer Science,
    University of Bari Aldo Moro
    – XXXVIth cycle, focus on cybersecurity and computer science research.

  • 2020 – Master’s Degree (MSc) in Cybersecurity,
    University of Bari “Aldo Moro”
    – Graduated cum laude with honors and special mention by the commission.

  • 2018 – Bachelor’s Degree (BSc) in Computer Science,
    University of Bari “Aldo Moro”
    – Graduated cum laude.

Professional Experience 💼

  • 2024–Present – Research Fellow,
    University of Bari Aldo Moro
    – Working on the FAIR project.

  • 2023–Present – Examination Board Member,
    University of Bari Aldo Moro
    – Courses: Biometric Systems, Application Security, Computer Architecture & OS.

  • 2023 – Adjunct Professor (CyberChallenge Course),
    University of Bari Aldo Moro
    – Taught Cryptography and Hardware Security (24 hours, 2 ECTS).

  • 2022–Present – Adjunct Professor of Cryptography,
    MSc in Cybersecurity, University of Bari Aldo Moro
    – 6 ECTS (62 hours).

  • 2021–Present – Member, ISLab Research Group,
    University of Bari Aldo Moro
    – Cybersecurity contact person.

Research Interests 🔬

  • Cryptography and hardware security

  • Malware detection and classification

  • Network intrusion detection (NIDS)

  • Firewall policy optimization

  • Anomaly detection and AI-driven threat detection

  • DevSecOps and secure software development practices

  • Blockchain analysis

  • Adversarial neural networks in security (e.g., GANs)

Awards and Honors🏆✨

  • Special mention by the MSc degree commission (2020)

  • Full marks and honors in both MSc and BSc degrees

Conclusion🌟

Dr. Stefano Galantucci is a dynamic and accomplished researcher in the field of cybersecurity, with a strong academic background, diverse teaching experience, and a rich portfolio of high-impact publications. His work bridges theoretical foundations and real-world applications, particularly in cryptography, malware detection, and network defense systems. A respected member of the research community, Dr. Galantucci actively contributes to scientific progress through both his scholarly output and his involvement in peer review for top-tier journals. With his growing influence and commitment to innovation, he stands out as a promising leader in the evolving landscape of cybersecurity research.

Publications 📚

  • 🔐 CARIOCA: Prioritizing the Use of IoC by Threats Assessment Shared on the MISP Platform
    📘 International Journal of Information Security, 2025-04
    [DOI: 10.1007/s10207-025-01006-2]
    👥 Piero Delvecchio, Stefano Galantucci, Andrea Iannacone, Giuseppe Pirlo

  • 🧮 FOBICS: Assessing Project Security Level through a Metrics Framework that Evaluates DevSecOps Performance
    📘 Information and Software Technology, 2025-02
    [DOI: 10.1016/j.infsof.2024.107605]
    👥 Alessandro Caniglia, Vincenzo Dentamaro, Stefano Galantucci, Donato Impedovo

  • 🛡️ PROGESI: A PROxy Grammar to Enhance Web Application Firewall for SQL Injection Prevention
    📘 IEEE Access, 2024
    [DOI: 10.1109/ACCESS.2024.3438092]
    👥 Antonio Coscia, Vincenzo Dentamaro, Stefano Galantucci, Antonio Maci, Giuseppe Pirlo

  • 🌐 Automatic Decision Tree-Based NIDPS Ruleset Generation for DoS/DDoS Attacks
    📘 Journal of Information Security and Applications, 2024-05
    [DOI: 10.1016/j.jisa.2024.103736]
    👥 Antonio Coscia, Vincenzo Dentamaro, Stefano Galantucci, Antonio Maci, Giuseppe Pirlo

  • 🧬 YAMME: A YAra-byte-signatures Metamorphic Mutation Engine
    📘 IEEE Transactions on Information Forensics and Security, 2023
    [DOI: 10.1109/TIFS.2023.3294059]
    👥 Antonio Coscia, Vincenzo Dentamaro, Stefano Galantucci, Antonio Maci, Giuseppe Pirlo

  • 🧠 Comparing Deep Learning and Shallow Learning Techniques for API Calls Malware Prediction
    📘 Applied Sciences, 2022-02
    [DOI: 10.3390/app12031645]
    👥 Angelo Cannarile, Vincenzo Dentamaro, Stefano Galantucci, Andrea Iannacone, Donato Impedovo, Giuseppe Pirlo

  • 💰 BACH: A Tool for Analyzing Blockchain Transactions Using Address Clustering Heuristics
    📘 Information, 2024-09-26
    [DOI: 10.3390/info15100589]
    👥 Michele Caringella, Francesco Violante, Francesco De Lucci, Stefano Galantucci, Matteo Costantini

  • 🔑 One Time User Key: A User-Based Secret Sharing XOR-ed Model for Multiple User Cryptography
    📘 IEEE Access, 2021
    [DOI: 10.1109/ACCESS.2021.3124637]
    👥 Stefano Galantucci, Donato Impedovo, Giuseppe Pirlo

  • 📉 Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection
    📘 Applied Sciences, 2022-02
    [DOI: 10.3390/app12031759]
    👥 Francesco Carrera, Vincenzo Dentamaro, Stefano Galantucci, Andrea Iannacone, Donato Impedovo, Giuseppe Pirlo

  • 📈 Ensemble Consensus: An Unsupervised Algorithm for Anomaly Detection in Network Security Data
    📄 CEUR Workshop Proceedings, 2021
    👥 Vincenzo Dentamaro, V.N. Convertini, Stefano Galantucci, P. Giglio, T. Palmisano, Giuseppe Pirlo

  • 🧭 A Case Study of Navigation System Assistance with Safety Purposes in the Context of Covid-19
    📘 Lecture Notes in Computer Science, 2021
    [DOI: 10.1007/978-3-030-85607-6_35]
    👥 Galantucci, S.; Giglio, P.; Dentamaro, V.; Pirlo, G.

  • 🔥 Firewall Optimization: An Innovative Two-Stage Algorithm to Optimize Firewall Rule Ordering
    📘 Computers & Security, 2023-11
    [DOI: 10.1016/j.cose.2023.103423]
    👥 Antonio Coscia, Vincenzo Dentamaro, Stefano Galantucci, Antonio Maci, Giuseppe Pirlo

Yi Sun | Information Security | Best Researcher Award

Published on by Innovation Technologist

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

Zhu Yongsheng | Data Privacy Protection | Best Researcher Award

Published on by Innovation Technologist

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.

 

 

 

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

Published on by Innovation Technologist

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]