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Archive Issue – Vol.5, Issue.4 (Oct-Dec 2025)

Archive Issue – Vol.5, Issue.4 (October-December 2025)


DDQN-Based Adaptive Lightweight Honeypot Framework for Intelligent Cyber Threat Detection in Small and Medium Enterprises

Arshit Rawat, Devansh Namdev, Aditya Sharma, Anant Pratap Singh Sachan and Shivank Kumar Soni

Research Paper | Journal Paper

Vol.5, Issue.4, pp.1-07, Dec-2025

DOI: 10.5281/zenodo.17802728

Abstract References Citation Download

Abstract

Honeypots serve as deceptive cybersecurity systems that attract and engage attackers, providing valuable insights into their methods within controlled environments. However, traditional honeypots are largely static and passive, making them easily identifiable and ineffective against modern, adaptive cyber threats. Existing adaptive models offer incremental improvements but remain limited by predefined rules or simplified learning mechanisms, restricting their responsiveness to complex and evolving attacks. This paper introduces an RL-Enhanced Adaptive Honeypot that integrates a Dueling Double Deep Q-Network (DDQN)-based decision engine to enable autonomous behavioural adaptation. The system dynamically adjusts its defence posture by analysing attacker activity and environmental metrics represented in a structured state model. Through continuous learning and policy optimization, the honeypot transitions between observation, deception, and mitigation strategies, maintaining an average accuracy of approximately 96% across behavioural prediction and threat intelligence classification tasks. Future work aims to employ simulated multi-stage attack environments to pre-train reinforcement learning agents, fostering the development of self-evolving honeypots capable of real-time, intelligent cyber defence.

Key-Words / Index Term: Adaptive Cyber Defence, DDQN, Honeypot, Network Security, Reinforcement Learning.

References

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      2. Z. Aradi and A. Bánáti, "The Role of Honeypots in Modern Cybersecurity Strategies," 2025 IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics (SAMI), Stará Lesná, Slovakia, 2025, pp. 000189-000196, 10.1109/SAMI63904.2025.10883300
      3. T. T. Nguyen and V. J. Reddi, "Deep Reinforcement Learning for Cyber Security," in IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 8, pp. 3779-3795, Aug. 2023, 10.1109/TNNLS.2021.3121870.
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      5. P. Holgado, V. A. Villagrá and L. Vázquez, "Real-Time Multistep Attack Prediction Based on Hidden Markov Models," in IEEE Transactions on Dependable and Secure Computing, vol. 17, no. 1, pp. 134-147, 1 Jan.-Feb. 2020, 10.1109/TDSC.2017.2751478.
      6. Pashaei, Abbasgholi, et al. "Early Intrusion Detection System using honeypot for industrial control networks." Results in Engineering 16 (2022): 100576. https://doi.org/10.1016/j.rineng.2022.100576
      7. B. Hu and J. Li, "Shifting Deep Reinforcement Learning Algorithm Toward Training Directly in Transient Real-World Environment: A Case Study in Powertrain Control," in IEEE Transactions on Industrial Informatics, vol. 17, no. 12, pp. 8198-8206, Dec. 2021, 10.1109/TII.2021.3063489.
      8. Caminero, Guillermo, Manuel Lopez-Martin, and Belen Carro. "Adversarial environment reinforcement learning algorithm for intrusion detection." Computer Networks 159 (2019): 96-109. https://doi.org/10.1016/j.comnet.2019.05.013
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      15. Veluchamy, Selvakumar, and Ruba Soundar Kathavarayan. "Deep reinforcement learning for building honeypots against runtime DoS attack." International Journal of Intelligent Systems 37.7 (2022): 3981-4007. https://doi.org/10.1002/int.22708

Citation

Arshit Rawat, Devansh Namdev, Aditya Sharma, Anant Pratap Singh Sachan and Shivank Kumar Soni, "DDQN-BASED ADAPTIVE LIGHTWEIGHT HONEYPOT FRAMEWORK FOR INTELLIGENT CYBER THREAT DETECTION IN SMALL AND MEDIUM ENTERPRISES" International Journal of Scientific Research in Technology & Management, Vol.5, Issue.4, pp.1-07, 2025. DOI: 10.5281/zenodo.17802728

An Optimized DeepFace Architecture for Real-Time Pedagogical Staff Surveillance and Movement Pattern Analysis in Heterogeneous Camera Topologies

Nikhil Kushwaha, Mayur Bansal, Pradyumna Tripathi and Shivank Kumar Soni

Research Paper | Journal Paper

Vol.5, Issue.4, pp.08-14, Dec-2025

DOI: 10.5281/zenodo.17862281

Abstract References Citation Download

Abstract

To keep the workplace safe, accountable, and running smoothly, it's important to verify staff presence and follow their movements in a dynamic environment and changing conditions. However, traditional attendance and surveillance systems—often rely on manual validation or RFID-based tracking remain static, error-prone, and incapable of adapting to heterogeneous camera networks or various environmental conditions. Preexisting computer vision approaches offer limited scalability and struggle with real-time multi-camera synchronization which reduces accuracy and responsiveness in critical applications. This paper introduces a smart staff monitoring system that uses Optimized DeepFace–OpenCV–based surveillance framework that autonomously detects, verifies, and records staff presence across distributed IP camera feeds. The proposed system integrates facial embedding extraction, automated timetable mapping, and absence alert system to notify supervisors when assigned personnel are not detected within a set-time duration. A dual-interface Flask-based portal offers separate access modes for employees and supervisors, enabling attendance history, and absence alerts, live location visibility without compromising the privacy of the staff. Tests with different camera setups showed that the system is highly reliable, correctly recognizing faces nearly 97.8% (using SQLite) with a mean response latency of 1.3 seconds per frame even with changing lighting or movement scenarios. Looking forward, the farmwork aims to expand in the future toward edge-enabled analytics and IoT-integrated workforce management, fostering scalable deployment across educational, corporate, and industrial domains.

Key-Words / Index Term: DeepFace, OpenCV, Real-Time Surveillance, Staff Monitoring, Facial Recognition, Smart Automation, IP Camera Networks.

References

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      5. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L.-C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 4510-4520, doi: 10.1109/CVPR.2018.00474.
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      7. Z. Cao, G. Hidalgo, T. Simon, S.-E. Wei and Y. Sheikh, "OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 1, pp. 172-186, Jan. 2021, doi: 10.1109/TPAMI.2019.2929257.
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      11. M. L. Tran and T. T. Nguyen, “A Real-Time Face Recognition Attendance System Using Deep Learning,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 9, 2020.
      12. A. Khan, R. Ahmad, and S. Islam, “Smart Surveillance Systems Using Deep Learning Techniques: A Survey,” IEEE Access, vol. 9, pp. 17307–17337, 2021.
      13. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv:1804.02767, 2018. DOI: https://doi.org/10.48550/arXiv.1804.02767
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      15. Flask Documentation, “Flask — Lightweight WSGI Web Application Framework,” Available: https://flask.palletsprojects.com/. [Accessed: Jan. 2025].
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      17. A. R. Chowdhury, H. J. Choi, and J. Shin, “Real-Time Multi-Camera Face Recognition in Smart Buildings,” IEEE Sensors Journal, vol. 20, no. 18, pp. 10856–10865, 2020.
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Citation

Nikhil Kushwaha, Mayur Bansal, Pradyumna Tripathi and Shivank Kumar Soni, "An Optimized DeepFace Architecture for Real-Time Pedagogical Staff Surveillance and Movement Pattern Analysis in Heterogeneous Camera Topologies" International Journal of Scientific Research in Technology & Management, Vol.5, Issue.4, pp.08-14, 2025. DOI: 10.5281/zenodo.17862281

YOLOv8-FaceEmbedding: Real Time Missing Person Detection and Recognition

Priyanshu Singh, Riya Bisen, Prakhar Bisen, Riya Kaushik, Vinita Shivastava

Research Paper | Journal Paper

Vol.5, Issue.4, pp.15-21, Dec-2025

DOI: 10.5281/zenodo.18086738

Abstract References Citation Download

Abstract

Every year, individuals go missing or remain unidentified due to accidents, displacement, or incident. Even though artificial intelligence and computer vision technology has come a long way to enhance surveillance systems, they are still constrained by the traditional single- camera and monolithic design when it comes to scalability and real time performance. This paper proposes a modular multi-camera face recognition surveillance system that uses a face-recognition library used for encoding and identifying faces along with YOLOv8 for object detection. The design separates camera management from the various recognition tasks and backend services for secure data management with encryption and role-based access management. The experiments demonstrated 95.3% accuracy for recognition, an average latency of 0.78 seconds, and an increase of 20% in efficiency and scalability compared to conventional systems. In conclusion, modern surveillance systems and applications provide a reliable, scalable and secure architecture. As a future improvement, our system utilizes AI-based facial de-aging and age-progression technology. The system can mimic the probable current facial structure of the individual by generating age-progressed images, resulting in more accurate matching between the newly lost or live-captured faces.

Key-Words / Index Term: Face Recognition, YOLOv8, Deep Learning, Facial Encoding, Multi-camera Surveillance, Missing Per- son Identification.

References

      1. B. Vinavatani, M. R. Panna, P. H. Singha and G. J. W. Kathrine, “AI for Detection of Missing Person,” International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, 2022, pp. 66–73.
      2. S. Ayyappan and S. Matilda, “Criminals and Missing Children Identification Using Face Recognition and Web Scraping,” 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–5.
      3. N. Ahirrao, S. Jade, S. Jagtap, N. Ghuse, M. Ghonge and A. Potgantwar, “A Review on Identification of Missing Persons and Criminals using Image Processing,” International Journal of Creative Research Thoughts (IJCRT), vol. 10, no. 7, July 2022.
      4. A. Ponmalar et al., “Finding Missing Person Using Artificial Intelligence,” 2022 International Conference on Computer, Power and Communications (ICCPC), Chennai, India, 2022, pp. 562–565.
      5. B. Pathak et al., “Implementation of Website for Identification of Missing and Unrecognized People Using an Optimized Face Recognition Algorithm,” Pune, India, 2024.
      6. V. Shelke et al., “Searchious: Locating Missing People Using an Optimized Face Recognition Algorithm,” Fifth International Conference on Computing Methodologies and Communication (ICCMC 2021), Vasai, India, 2021.
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      12. K. Suchana et al., “Development of User-Friendly Web-Based Lost and Found System,” Journal of Software Engineering and Applications, vol. 14, pp. 575–590, 2021.

Citation

Priyanshu Singh, Riya Bisen, Prakhar Bisen, Riya Kaushik, Vinita Shivastava, "YOLOv8-FaceEmbedding: Real Time Missing Person Detection and Recognition" International Journal of Scientific Research in Technology & Management, Vol.5, Issue.4, pp.15-21, 2025. DOI: 10.5281/zenodo.18086738

HERBLEDGER: A HYPERLEDGER-based BLOCKCHAIN framework for Ayurvedic Herb Quality, Traceability, and Consumer Transparency

Anshika Tiwari, Aditi Vishwakarma, Akshita Bhatia, Imran Ali Khan

Research Paper | Journal Paper

Vol.5, Issue.4, pp.22-27, Dec-2025

DOI: 10.5281/zenodo.18100297

Abstract References Citation Download

Abstract

Ayurvedic practice uses a wide range of herbs used in traditional medicine for centuries. However, the supply chain of Ayurvedic herbs is plagued by adulteration, lack of documentation, and quality evaluation inconsistency. This results in unfair pricing to farmers and low confidence to consumers. Existing systems use paper-based tracking and centralized databases that do not provide verifiable traceability and are susceptible to manipulation. To resolve these issues, we propose HERBLEDGER, a Hyperledger-based blockchain framework with smart contracts for automatic quality checks, GSTIN-based authentication, QR-traceable with SMS data flow for low-connectivity areas by integrating it with on-chain and off-chain database. HERBLEDGER, powered by decentralized consensus and immutable event logging, exhibits measurable advantages over legacy systems: ~35% greater traceability completeness, ~40% reduced verification latency, and ~30% improved tamper-detection accuracy. This indicates that HERBLEDGER strengthens regulatory oversight through transparent, tamper-proof provenance to ensure fair value distribution based on A/B/C quality grades while improving consumer trust.

Key-Words / Index Term: Blockchain, Hyperledger, Ayurvedic herbs, traceability, GSTIN authentication, smart contracts, quality grading.

References

      1. Mavis Hong-Yu Yik, Vivian Chi-Woon Taam Wong, Tin-Hang Wong, Pang-Chui Shaw, HerBChain, a blockchain-based informative platform for quality assurance and quality control of herbal products.
      2. Patelli, N. and Mandrioli, M. (2020), Blockchain technology and traceability in the agrifood industry.
      3. Y. Yang, “A blockchain and IPFS-based system for monitoring the geographical authenticity of herbs.”
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      20. Saroj Kumar Rout. Medic ledger: Blockchain-Based Supply Chain Architecture for Ayurvedic Product Authentication and Compliance.
      21. G. N. Schroeder, C. Steinmetz, R. N. Rodrigues, R. V. B. Henriques, A. Rettberg and C. E. Pereira, "A Methodology for Digital Twin Modeling and Deployment for Industry 4.0."
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Citation

Anshika Tiwari, Aditi Vishwakarma, Akshita Bhatia, Imran Ali Khan, "HERBLEDGER: A HYPERLEDGER-based BLOCKCHAIN framework for Ayurvedic Herb Quality, Traceability, and Consumer Transparency" International Journal of Scientific Research in Technology & Management, Vol.5, Issue.4, pp.22-27, 2025. DOI: 10.5281/zenodo.18100297

AYURSYNC AI:An Intelligent Health Manager using Decision Tree

Palak Rohra, Nikhil Kalawat, Harshita Patle, Kushagra Kumar Pathak, Sanjay Kumar Sharma

Research Paper | Journal Paper

Vol.5, Issue.4, pp.28-34, Dec-2025

DOI: 10.5281/zenodo.18135727

Abstract References Citation Download

Abstract

AyurSync AI: It is an ayurvedic health management platform that combines the knowledge of ayurveda with the knowledge of allopathy. The platform follows a decision tree structure for symptom parsing, mapping them with possible Ayurvedic diseases using Ayurveda disease codes (Namaste Portal), as well as WHO ICD-10 codes. In contrast to present-day decision-support systems that focus on a particular system, AyurSync AI provides explanations for a disease and customized lifestyle advice such as dietary, exercise, and yogic guidance. Some of its features include multi-language symptom input, search for Ayurveda/other practitioners in your neighborhood (up to 10 km), and appointment booking. Future updates include machine learning predictions, support for more languages, telemedicine consultation, and linkage with the electronic health record system.

Key-Words / Index Term: Digital Health, Healthcare Information Systems, Ayurveda Integration, ICD Classification, Symptom Analysis, Natural Language Processing, Appointment Scheduling, Health Informatics, Location-Based Healthcare Services.

References

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Citation

Palak Rohra, Nikhil Kalawat, Harshita Patle, Kushagra Kumar Pathak, Sanjay Kumar Sharma, "AYURSYNC AI:An Intelligent Health Manager using Decision Tree " International Journal of Scientific Research in Technology & Management, Vol.5, Issue.4, pp.28-34, 2025. DOI: 10.5281/zenodo.18135727