Skip to content
DraconiticResearch
Search Sources API
203,952 studies31 sourcesUpdated 22 min ago
Peer-reviewedCC BY-NC-ND 4.0Scientific Reports

Multi-service honeypot framework for high-fidelity cyber-attack data collection and machine learning-based threat detection in cloud environments

Related

  1. SeaTrack-DeepSORT: A Vision-Based Ship Detection and Multi-Object Tracking Framework for Complex Maritime Environments
    Sensors (Basel, Switzerland)Peer-reviewedCC BYFull textEurope PMC
  2. A provenance-aware hybrid deepfake detection framework integrating perceptual hashing and explainable CNNs for robust multimedia forensics in social IoT environments
    Reem Alshenaifi, Shailendra Mishra, Shrouq Nahar ALmutairi
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textCrossref
  3. YOLO-CBNet: A robust attention-enhanced detection framework for underwater fish recognition in aquaculture environments
    Mahdi Hamzaoui, Leila Bousbia, Mohamed Ould-Elhassen Aoueileyine +2
    PLOS OnePeer-reviewedCC BY 4.0Full textNeuroscienceZoology & animal behaviourPLOS
  4. A unified RT-DETR–Mask2Former framework for segmentation-guided plant leaf disease detection and classification in real-world agricultural environments
    C H. Mohan Sai Kumar, Golthu Anusha, V. Dhilip Kumar +3
    Frontiers in Plant SciencePeer-reviewedCC BY 4.0Full textPlant biologyCrossrefEurope PMCEurope PMCPMC
  5. Synthetic Apple Collection (SyntAC): render-based synthetic datasets for apple detection.
    Jhonny Hueller, Massimo Vecchio, Fabio Antonelli
    Data in briefPeer-reviewedCC BY-NCFull textPlant biologyEurope PMC
BibTeX
@article{sridhar2026multiservice,
  title = {{Multi-service honeypot framework for high-fidelity cyber-attack data collection and machine learning-based threat detection in cloud environments}},
  author = {G. Sridhar and J. B. Simha and Rashmi Agarwal},
  journal = {Scientific Reports},
  year = {2026},
  month = oct,
  doi = {10.1038/s41598-026-72812-4},
  url = {https://www.nature.com/articles/s41598-026-72812-4},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Multi-service honeypot framework for high-fidelity cyber-attack data collection and machine learning-based threat detection in cloud environments
AU  - G. Sridhar
AU  - J. B. Simha
AU  - Rashmi Agarwal
PY  - 2026
DA  - 2026/10/06
JO  - Scientific Reports
DO  - 10.1038/s41598-026-72812-4
UR  - https://www.nature.com/articles/s41598-026-72812-4
SN  - 2045-2322
ER  -