Akashdeep Bhardwaj | Cybersecurity | Editorial Board Member

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Dr.  Akashdeep Bhardwaj | Cybersecurity | Editorial Board Member

University of Petroleum and Energy Studies | India

Akashdeep Bhardwaj is a distinguished researcher affiliated with the University of Petroleum and Energy Studies, Dehradun, India, with expertise spanning cybersecurity, intrusion detection, cyber-physical systems security, IoT protection frameworks, and machine-learning-based threat analysis. His research contributions include the development of robust intrusion detection models using statistical feature-selection techniques, elasticsearch-based threat-hunting mechanisms, and advanced security architectures for cyber-physical robotic systems. He has published extensively in high-impact international journals such as Scientific Reports, Eurasip Journal on Information Security, Computers & Security, Egyptian Informatics Journal,Measurement Sensors, and the Journal of Database Management. His interdisciplinary work also includes deep learning applications in medical imaging, particularly diabetic-retinopathy detection using residual networks. In addition to journal publications, he has authored key academic books, including Mastering Cybersecurity: A Practical Guide to Cyber Tools and Techniques (Volume 2) and A Practical Approach to Open Source Intelligence (OSINT) – Volume 1, highlighting his commitment to knowledge dissemination and professional capacity building. With collaborations involving over 100 co-authors worldwide, his work significantly contributes to enhancing digital security, strengthening smart-infrastructure resilience, and advancing next-generation threat-mitigation strategies. His academic influence and research productivity are reflected in his metrics 1,208 citations by 1,097 documents, 110 documents, and an h-index of 21.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

1. Security Algorithms for Cloud Computing Bhardwaj, A., Subrahmanyam, G. V. B., Avasthi, V., & Sastry, H. (2016). Security algorithms for cloud computing. Procedia Computer Science, 85, 535–542. Citations: 162

2. Smart IoT and Machine Learning-Based Framework for Water Quality Assessment and Device Component Monitoring Bhardwaj, A., Dagar, V., Khan, M. O., Aggarwal, A., Alvarado, R., Kumar, M., … (2022). Smart IoT and machine learning-based framework for water quality assessment and device component monitoring. Environmental Science and Pollution Research, 29(30), 46018–46036. Citations: 121

3. Penetration Testing Framework for Smart Contract Blockchain Bhardwaj, A., Shah, S. B. H., Shankar, A., Alazab, M., Kumar, M., & Gadekallu, T. R. (2021). Penetration testing framework for smart contract blockchain. Peer-to-Peer Networking and Applications, 14(5), 2635–2650. Citations: 121

4. Machine Learning-Based Regression Framework to Predict Health Insurance Premiums Kaushik, K., Bhardwaj, A., Dwivedi, A. D., & Singh, R. (2022). Machine learning-based regression framework to predict health insurance premiums. International Journal of Environmental Research and Public Health. Citations: 118

5. Ransomware Digital Extortion: A Rising New Age Threat Bhardwaj, A., Avasthi, V., Sastry, H., & Subrahmanyam, G. V. B. (2016). Ransomware digital extortion: A rising new age threat. Indian Journal of Science and Technology, 9(14), 1–5. Citations: 92

Abdul Razaque | Cybersecurity | Best Researcher Award

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Prof. Dr. Abdul Razaque | Cybersecurity | Best Researcher Award

Professor | Satbayev University | Kazakhstan

Prof. Dr.Abdul Razaque is an accomplished cybersecurity and computer science scholar with more than twenty years of academic, research, and industry experience spanning the United States, Kazakhstan, China, South Korea, France, Morocco, and Pakistan. Currently a Professor of Cybersecurity at Satbayev University and a Postdoctoral Researcher at Gachon University, he has previously served in multiple tenure-track positions, directed international IT projects for UNESCO, and contributed to national ICT initiatives. His research expertise covers cybersecurity, IoT, cloud and edge computing, artificial intelligence, blockchain systems, wireless sensor networks, and smart-city technologies. He has authored more than 200 peer-reviewed publications, including high-impact Q1 journals such as Computer Science Review, IEEE Access, Sensors, Internet of Things, and Future Generation Computer Systems, alongside several books and book chapters. His work has accrued thousands of citations globally, reflecting sustained scholarly influence across interdisciplinary domains. As Principal Investigator or Co-Investigator, he has secured over US$6 million in competitive national and international research funding, leading large-scale projects in intelligent systems, cybersecurity frameworks, image-based monitoring, smart city infrastructures, and privacy-preserving architectures. His collaborative network spans leading institutions across the United States, Korea, Kazakhstan, Saudi Arabia, Europe, and South Asia, with repeated partnerships involving multidisciplinary teams of engineers, computer scientists, medical researchers, and industry stakeholders. Throughout his career, he has demonstrated a commitment to societal impact by developing scalable technologies for public safety, healthcare, digital governance, secure urban infrastructure, and advanced educational systems. His contributions extend to mentoring students, advising theses, shaping curricula, and promoting global research exchange. Recognized with awards including the Best Engineering Student Researcher Award, and supported by extensive professional certifications in cybersecurity, data science, AI, and enterprise technologies, he represents a globally engaged researcher dedicated to advancing secure, intelligent, and equitable digital ecosystems.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

Almiani, M., AbuGhazleh, A., Al-Rahayfeh, A., Atiewi, S., & Razaque, A. (2020). Deep recurrent neural network for IoT intrusion detection system. Simulation Modelling Practice and Theory, 101, 102031.

Razaque, A., Bleakley, C., & Dobson, S. (2013). Compression in wireless sensor networks: A survey and comparative evaluation. ACM Transactions on Sensor Networks (TOSN), 10(1), Article 5.

Adnan, M., Razaque, A., Ahmed, I., & Isnin, I. F. (2014). Bio-mimic optimization strategies in wireless sensor networks: A survey. Sensors, 14(1), 299–345.

Al-lQubaydhi, N., Alenezi, A., Alanazi, T., Senyor, A., Alanezi, N., Alotaibi, B., … Razaque, A. (2024). Deep learning for unmanned aerial vehicles detection: A review. Computer Science Review, 51, 100614.

Razaque, A., & Sallah, M. (2013). The use of mobile phone among farmers for agriculture development. International Journal of Scientific Research, 2, 95–98.

Abdul Razaque’s work advances secure, intelligent, and scalable digital systems that strengthen cybersecurity, smart-city infrastructure, and data-driven decision-making across governments, industry, and academia. By integrating AI, blockchain, and IoT technologies into real-world solutions, he enhances public safety, operational efficiency, and technological resilience. His vision is to build globally accessible, trustworthy, and innovative digital ecosystems that drive sustainable societal and economic progress.

Jnana Ramakrishna Chodisetti | Cybersecurity | Best Researcher Award

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Jnana Ramakrishna Chodisetti | Cybersecurity | Best Researcher Award

Jnana Ramakrishna Chodisetti, Tata Consultancy Services, India

Jnana Ramakrishna Chodisetti is a Cybersecurity Engineer at Tata Consultancy Services, specializing in malware analysis, threat hunting, and incident response. He holds a Bachelor of Technology in Electronics and Communication Engineering from Amrita School of Engineering, Bengaluru, where he graduated with First Class honors. Jnana’s expertise spans digital forensics, cryptography, and machine learning, with significant contributions to image encryption and malicious URL detection. His research includes developing advanced encryption algorithms and analyzing lightweight cryptographic methods for IoT. He has also gained practical experience through internships and research roles, and actively participates in cybersecurity competitions and organizations.

Publication profile

ORCID

Education

Jnana Ramakrishna Chodisetti earned his Bachelor of Technology in Electronics and Communication Engineering from Amrita School of Engineering, Bengaluru, India, graduating in 2023 with First Class honors and a CGPA of 7.71 on a 10.0 scale. His academic coursework covered a broad range of subjects including Computer Networks, Calculus, Probability and Random Processes, Linear Algebra, Matrix Algebra, Computer Programming, and Data Structures and Algorithms.

Experience

Jnana began his professional career as a Cybersecurity Engineer at Tata Consultancy Services in Chennai, India, starting in August 2023. In this role, he works within the DFIR – Threat Hunting unit, focusing on Malware Analysis, Threat Hunting, and Incident Response, utilizing tools such as Splunk, QRadar, and Sentinel. Prior to this, he completed an internship as a Blue Teaming Trainee with Virtual Cyber Labs, where he gained practical knowledge in threat detection and research. He also served as a Research Intern at TIFAC-CORE in Cybersecurity, Amrita Vishwa Vidyapeetham, where he worked on image encryption schemes and cryptanalysis, proposing innovative methods using Elliptic Curve Cryptography.

Research focus

Jnana’s research has primarily concentrated on advancing cybersecurity and encryption technologies. His work includes developing novel image encryption algorithms using a 3D Logistic Map and an improved Chirikov Map, as well as analyzing lightweight cryptographic algorithms for IoT environments. Additionally, his research explores efficient authenticated encryption schemes based on Elliptic Curve Cryptography, aimed at enhancing both security and authentication for digital images.

Publication top notes

Zhiqiang wang | Cyberspace Security | Best Researcher Award

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🌟Assoc Prof Dr. Zhiqiang wang, Cyberspace Security, Best Researcher Award 🏆

  • Associate Professor at Beijing Electronic Science and Technology Institute, China

Zhiqiang Wang is a dedicated researcher specializing in cyberspace security. With a Ph.D. in Information Security from Xidian University, Wang has established himself as a prominent figure in the field. His research interests lie in the intersection of deep learning, blockchain technology, and cybersecurity, with a focus on developing innovative solutions to tackle emerging threats in the digital landscape. Wang’s expertise and contributions have earned him recognition both nationally and internationally.

Author Metrics

Scopus Profile

ORCID Profile

Zhiqiang Wang’s contributions to academia are underscored by his impressive author metrics. With numerous publications in reputable journals and conferences, Wang has demonstrated his prolificacy and impact in the field of cyberspace security. His works have been cited extensively, reflecting their significance and influence within the academic community. Wang’s author metrics serve as a testament to his scholarly contributions and expertise in the domain.

  • Citations: 296 citations from 286 documents
  • Documents: 71 documents
  • h-index: 8

Education

Zhiqiang Wang embarked on his academic journey by obtaining a Bachelor of Science degree in Computer Science and Technology from Beijing Electronic Science and Technology Institute. He further pursued his passion for research by earning a Ph.D. in Information Security from Xidian University. Wang’s academic background equipped him with a strong foundation in computer science and laid the groundwork for his subsequent contributions to cyberspace security research.

Research Focus

Wang’s research focuses on advancing the field of cyberspace security through interdisciplinary approaches. His primary interests encompass deep learning, blockchain technology, malware detection, and network security. Wang is committed to developing novel methodologies and algorithms to address the evolving challenges posed by cyber threats. By leveraging cutting-edge technologies, he aims to enhance the resilience of digital infrastructures and safeguard against malicious activities in cyberspace.

Professional Journey

Zhiqiang Wang’s professional journey is marked by a series of academic appointments and research positions. He began his career as an Assistant Professor at Beijing Electronic Science and Technology Institute, where he conducted groundbreaking research in cyberspace security. Subsequently, Wang assumed roles as an Associate Professor and Postdoctoral Researcher, further solidifying his expertise in the field. His career trajectory reflects his dedication to advancing cybersecurity knowledge and fostering academic excellence.

Honors & Awards

Throughout his career, Zhiqiang Wang has received numerous honors and awards in recognition of his contributions to cyberspace security research. His exemplary achievements have been acknowledged by prestigious institutions and organizations, underscoring his impact on the field. Wang’s accolades serve as a testament to his exceptional talent, dedication, and scholarly accomplishments in advancing cybersecurity knowledge and practices.

Publications Noted & Contributions

Zhiqiang Wang’s publications are notable for their significance and impact in the field of cyberspace security. His research contributions span a wide range of topics, including blockchain technology, malware detection, and network security. Wang’s publications have garnered attention for their innovative methodologies and practical implications, contributing to the advancement of cybersecurity knowledge and practices.

Title: A Method for Generating Geometric Image Sequences for Non-Isomorphic 3D-Mesh Sequence Compression
Authors: Gao, Y., Wang, Z., Wen, J.
Journal: Electronics (Switzerland), 2023, 12(16), 3473
Abstract: The abstract for this article is not available.

Title: Research on Medical Security System Based on Zero Trust
Authors: Wang, Z., Yu, X., Xue, P., Qu, Y., Ju, L.
Journal: Sensors, 2023, 23(7), 3774
Abstract: The abstract for this article is not available.

Title: Multi-step Review Generation Based on Masked Language Model for Cross-Domain Aspect-Based Sentiment Analysis
Authors: Ju, L., Lv, X., Wang, Z., Miao, Z.
Proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, 14302 LNAI, pp. 723–735
Abstract: The abstract for this conference paper is not available.

Title: Review Generation Combined with Feature and Instance-Based Domain Adaptation for Cross-Domain Aspect-Based Sentiment Analysis
Authors: Lv, X., Wang, Z., Ju, L.
Proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, 14303 LNAI, pp. 813–825
Abstract: The abstract for this conference paper is not available.

Title: A Few-Shot Malicious Encrypted Traffic Detection Approach Based on Model-Agnostic Meta-Learning
Authors: Wang, Z., Li, M., Ou, H., Pang, S., Yue, Z.
Journal: Security and Communication Networks, 2023, 2023, 3629831
Abstract: The abstract for this article is not available.

Research Timeline

Wang’s research timeline illustrates the evolution of his scholarly pursuits and contributions over the years. Beginning with his doctoral studies, Wang has consistently engaged in cutting-edge research projects aimed at addressing key challenges in cyberspace security. His research trajectory is characterized by a progression from foundational studies to more specialized investigations, reflecting his growing expertise and dedication to advancing the field.

Collaborations and Projects

Zhiqiang Wang has actively collaborated with peers and experts in academia and industry to tackle complex challenges in cyberspace security. Through collaborative projects, Wang has contributed to the development of innovative solutions and technologies aimed at enhancing cybersecurity resilience and mitigating emerging threats. His collaborative endeavors underscore the importance of interdisciplinary cooperation in addressing the multifaceted nature of cybersecurity challenges.