Mitra Alidoosti | Network Security and Privacy | Editorial Member

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Dr. Mitra Alidoosti | Network Security and Privacy | Editorial Member 

Dr. Mitra Alidoosti | IUST | Iran

Dr. Mitra Alidoosti is a computer engineering researcher specializing in web application security, network security, and business-layer vulnerability analysis, contributing extensively to the advancement of dynamic security testing methods. Her work focuses on detecting complex logical vulnerabilities such as race conditions, session puzzling, and business-layer DoS attacks through innovative black-box and dynamic analysis approaches. She has authored multiple ISI and ISC-indexed publications in reputable journals, addressing web resiliency, semantic security testing, and SIP vulnerability assessment, along with several conference papers on secure protocol design, web penetration testing, and multi-step vulnerability detection. Her expertise spans penetration testing, secure architectural design, and process mining for web applications, supported by deep experience in security frameworks, protocol analysis, and automated testing tools. With 97 citations, an h-index of 5, and an i10-index of 2, she continues to contribute significant research in strengthening the security and reliability of complex web and communication systems.

Profiles: Google Scholar

Featured Publications: 

Mirjalili, M., Nowroozi, A., & Alidoosti, M. (2014). A survey on web penetration test. Advances in Computer Science: An International Journal, 3(6), 107–121.

Alidoosti, M., Nowroozi, A., & Nickabadi, A. (2020). Evaluating the web-application resiliency to business-layer DoS attacks. ETRI Journal, 42(3), 433–445.

Alidoosti, M., & Nowroozi, A. (2020). BLProM: A black-box approach for detecting business-layer processes in the web applications. Journal of Computing and Security (JCS), 6(2), 65–80.

Alidoosti, M., Asgharian, H., & Akbari, A. (2013). Security framework for designing SIP scanner. In 2013 21st Iranian Conference on Electrical Engineering (ICEE) (pp. 1–5).

Alidoosti, M., Nowroozi, A., & Nickabadi, A. (2022). Semantic web Racer: Dynamic security testing of the web application against race condition in the business layer. Expert Systems with Applications, 195, 116569.

Vangelis Lamprou | Network Intrusion Detection | Best Researcher Award

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Mr. Vangelis Lamprou | Network Intrusion Detection | Best Researcher Award

Vangelis Lamprou at National Technical University of Athens | Greece

Mr. Vaggelis Lamprou is a PhD student in the School of Electrical and Computer Engineering at the National Technical University of Athens (NTUA) and a Machine Learning Engineer specializing in deep learning, interpretable AI, and probabilistic modeling. With a strong academic foundation in mathematics and artificial intelligence, he has contributed to European-funded R&D projects in federated learning, generative AI, anomaly detection, and cybersecurity for next-generation networks. His research has been published in leading journals, including Computer Methods and Programs in Biomedicine and the IEEE Open Journal of the Communications Society.

Professional Profile:

Education: 

Mr. Vaggelis Lamprou holds a strong academic background spanning mathematics and artificial intelligence, currently pursuing his PhD in the School of Electrical and Computer Engineering at the National Technical University of Athens (NTUA) with a focus on deep learning, interpretable AI, and probabilistic modeling. He earned his M.Sc. in Artificial Intelligence from NCSR Demokritos and the University of Piraeus,  where his thesis explored the evaluation of deep learning interpretability methods for medical images in terms of faithfulness. Prior to that, he completed an M.Sc. in Mathematics at the University of Bonn, Germany. His academic journey began with a B.Sc. in Mathematics from the National and Kapodistrian University of Athens (NKUA).

Experience:

Mr. Vaggelis Lamprou brings extensive professional expertise in machine learning and data analytics, with a strong track record in both academic and industry-driven innovation. He has been serving as a Machine Learning Engineer at the DSS Lab, EPU-NTUA, where he develops AI-based solutions in federated learning and generative AI for European R&D projects. Previously, as a Machine Learning Engineer at Infili Technologies SA, he designed advanced anomaly detection systems and implemented privacy-preserving mechanisms for federated learning environments. He worked as a Data Analyst at Harbor Lab, where he conducted SQL-based analytics, performed Python-driven exploratory data analysis, and collaborated with the engineering team to build a Port Cost Estimator, optimizing maritime cost assessment processes.

Research Interest:

Mr. Vaggelis Lamprou’s research interests lie at the intersection of artificial intelligence, mathematics, and secure computing, with a focus on advancing both theoretical foundations and practical applications. In AI, he specializes in deep learning architectures, interpretable AI techniques, and probabilistic modeling, aiming to enhance transparency and trust in machine learning systems. His expertise extends to computer vision and natural language processing, particularly in developing interpretability methods for medical imaging and building robust NLP pipelines. He is also engaged in federated learning and cybersecurity research, working on privacy-preserving AI and ensuring trustworthiness in emerging 5G/6G network environments. Additionally, he explores the integration of probability theory and statistical methods into AI, leveraging mathematical rigor to improve model reliability and performance.

Publications Top Noted:

Federated Learning for Enhanced Cybersecurity and Trustworthiness in 5G and 6G Networks: A Comprehensive Survey

  • Year: 2024 | Citations: 16

On the Evaluation of Deep Learning Interpretability Methods for Medical Images Under the Scope of Faithfulness

  • Year: 2024 | Citations: 4

Grad-CAM vs HiResCAM: A Comparative Study via Quantitative Evaluation Metrics

  • Year: 2023 | Citations: 4

Conclusion:

With a solid foundation in mathematics, AI, and cybersecurity, Mr. Vangelis Lamprou exemplifies the qualities of a Best Researcher Award recipient in Network Intrusion Detection. His work addresses some of the most pressing challenges in ensuring trust and transparency in next-generation networks. As he continues to expand his research scope and global engagement, he is poised to play a pivotal role in shaping the future of secure AI-driven systems. His combination of academic rigor, technical innovation, and applied impact makes him a deserving candidate for this recognition.

Dr. Jingwen Wang | Advanced Functional Network | Best Researcher Award

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Dr. Jingwen Wang | Advanced Functional Network | Best Researcher Award

Dr. Jingwen Wang, Anhui University of Science and technology, China

Dr. Jingwen Wang is a Postdoctoral Researcher specializing in Safety Science and Engineering at the University of Science and Technology of China, where she completed her Doctor of Engineering degree (2020-2024). She also holds a Bachelor of Science in Macromolecule Materials and Engineering from Anhui University (2013-2017). Under the supervision of Academician Liang Yuan, Dr. Wang has made significant strides in material science, focusing on the customized design of nanomaterials and high-performance composites with enhanced properties such as toughness, wear-resistance, fire-safety, and multimodal monitoring capabilities. Her contributions to next-generation materials have been supported by prestigious research projects, including those funded by the National Key Research and Development Program and the National Natural Science Foundation of China.

Professional Profile:

Scopus

Suitability for the Award

Dr. Jingwen Wang is an outstanding candidate for the Best Researcher Award, based on the following key points:

  1. Innovative Research:
    • Dr. Wang’s work on advanced polymer and functional materials showcases a deep understanding of material science. Her ability to design and engineer multifunctional materials and high-performance composites is not only innovative but also highly impactful, pushing the boundaries of what is possible in material science.
  2. High-Impact Publications:
    • Her research is published in top-tier journals such as Chemical Engineering Journal and Advanced Functional Materials, demonstrating the high quality and relevance of her work. The topics she addresses, including flame retardancy, toughness, wear-resistance, and electronic device reconfiguration, are crucial for advancing technology in various industries.
  3. Collaborative Research Experience:
    • Participation in nationally funded research projects highlights her capability to contribute significantly to large-scale, collaborative research endeavors. This experience underscores her role as a leading researcher who can drive impactful studies.
  4. Broad Research Impact:
    • Dr. Wang’s research has broad applications in safety science, engineering, electronics, and materials science, making her work relevant to multiple fields and industries. Her contributions are poised to influence future developments in material science and technology.

Summary of Qualifications

  1. Education:

    • Doctor of Engineering (2020 – 2024), Major in Safety Science and Engineering, University of Science and Technology of China.
    • Bachelor of Science (2013 – 2017), Major in Macromolecule Materials and Engineering, Anhui University.

  2. Work Experience:

    • Postdoctoral Researcher under the supervision of Liang Yuan, an Academician.

  3. Research Contributions:

    • Dr. Wang has made significant contributions to the field of material science, particularly in the customized design of nanomaterials to create multifunctional materials and the tailored design of high-performance composites. Her work has resulted in the development of next-generation materials with enhanced properties, including toughness, wear-resistance, fire-safety, and multimodal monitoring capabilities.

  4. Key Publications:

    • “Targeted assembly of hierarchical sea-urchin inspired NiPS@LDH architecture for enhanced toughness, wear-resistance and fire-safety of epoxy composites,” Chemical Engineering Journal, 2024.
    • “Bioinspired ultra-robust ionogels constructed with soft-rigid confinement space for multimodal monitoring electronics,” Advanced Functional Materials, 2023.
    • “Intrinsic ionic confinement dynamic engineering of ionomers with low dielectric-k, high healing and stretchability for electronic device reconfiguration,” Chemical Engineering Journal, 2023.
    • “Ethanol inducing self-assembly of poly-(thioctic acid)/graphene supramolecular ionomers for healable, flame-retardant, shape-memory electronic devices,” Journal of Colloid and Interface Science, 2023.
    • “Synergistic effects of aryl diazonium modified Few-Layer black Phosphorus/Ultrafine rare earth yttrium oxide with enhancing flame retardancy and catalytic smoke toxicity suppression of epoxy resin,” Applied Surface Science, 2022.

  5. Research Projects:

    • Dr. Wang has participated in several prestigious national research projects, including:
      • National Key Research and Development Program funding.
      • Multiple National Natural Science Foundation of China projects.

Conclusion

Dr. Jingwen Wang is highly suitable for the Best Researcher Award. Her innovative research, high-impact publications, and participation in prestigious research projects underscore her exceptional contributions to the field of material science. Dr. Wang’s work on the customized design of nanomaterials and high-performance composites not only advances academic knowledge but also has practical implications for the development of next-generation materials with enhanced properties. These qualities make her a strong candidate for this prestigious award.