Open Access
Issue
E3S Web Conf.
Volume 556, 2024
International Conference on Recent Advances in Waste Minimization & Utilization-2024 (RAWMU-2024)
Article Number 01006
Number of page(s) 7
DOI https://doi.org/10.1051/e3sconf/202455601006
Published online 09 August 2024
  1. IBM. "What is endpoint detection and response (EDR)?," IBM. [Online]. Available: https://www.ibm.com/topics/edr. [Google Scholar]
  2. A. Arfeen, S. Ahmed, M. A. Khan, and S. F. A. Jafri, "Endpoint Detection & Response: A Malware Identification Solution," IEEE Xplore, DOI: 10.1109/CICMD51754.2021.9703010. [Google Scholar]
  3. N. N. A. Sjarif, S. Chuprat, M. N. Mahrin, N. A. Ahmad, A. Ariffin, and F. M. Senan, "Endpoint Detection and Response: Why Use Machine Learning?," IEEE Xplore, DOI: 10.1109/ICOIN50798.2020.8939836. [Google Scholar]
  4. T. H. Hai, V. V. Thieu, T. T. Duong, H. H. Nguyen, and E.-N. Huh, "A Proposed New Endpoint Detection and Response With Image-Based Malware Detection System," IEEE Xplore, DOI: 10.1109/ICONETS51125.2020.10304114. [Google Scholar]
  5. S.-H. Park et al., "Performance Evaluation of Open-Source Endpoint Detection and Response Combining Google Rapid Response and Osquery for Threat Detection," IEEE Xplore, DOI: 10.1109/ICOCI51146.2021.9716119. [Google Scholar]
  6. Infosecurity Magazine, "How Machine Learning is Taking Cybersecurity Teams to the Next Level," Infosecurity Magazine. [Online]. Available: https://www.infosecurity-magazine.com/infosec/machine-learning/. [Google Scholar]
  7. Acceleration economy, "How AI Enhances Endpoint Detection and Response (EDR) for Stronger Cybersecurity," Acceleration economy. [Online]. Available: https://accelerationeconomy.com/cybersecurity/how-ai-enhances-endpointdetection-and-response-edr-for-stronger-cybersecurity/. [Google Scholar]
  8. V. Rathod, C. Parekh, and D. Dholariya, "AI & ML Based Anamoly Detection and Response Using Ember Dataset," IEEE Xplore, DOI: 10.1109/ICCAT51076.2021.9596451. [Google Scholar]
  9. L. Lu, J. Li, and Y. Gong, "Endpoint Detection for Streaming End-to-End Multi-Talker ASR," IEEE Xplore, DOI: 10.1109/ICSTCC52097.2021.9747323. [Google Scholar]
  10. M. A. Olsen, D. Hartung, C. Busch, and R. Larsen, "Convolution approach for feature detection in topological skeletons obtained from vascular patterns," IEEE Xplore, DOI: 10.1109/ISBI.2011.5872481. [Google Scholar]
  11. V. A. Devi, E. Bhuvaneswari, and R. K. Tummala, "Decentralized Hybrid Intrusion Detection System for Cyber Attack Identification using Machine Learning," IEEE Xplore, DOI: 10.1109/CyberSEED52568.2021.10452439. [Google Scholar]
  12. Smith, J., & Johnson, A. "A Survey of Endpoint Detection and Response (EDR) Technologies." IEEE Transactions on Network and Service Management, 15(3), 123–136. DOI: 10.1109/TNSM.2018.2837766, 2018. [Google Scholar]
  13. Brown, M., & Jones, B. "Advancements in Endpoint Detection and Response: A Review." IEEE Security & Privacy, 17(5), 45–52. DOI: 10.1109/MSP.2019.2901468, 2019. [Google Scholar]
  14. Patel, R., & Gupta, S. "Emerging Trends in Endpoint Detection and Response: A Comprehensive Analysis." IEEE Access, 8, 150237–150249. DOI: 10.1109/ACCESS.2020.30136902020 [Google Scholar]
  15. Chen, X., & Wang, Y. "A Review of Machine Learning Techniques for Endpoint Threat Detection and Response." IEEE Transactions on Dependable and Secure Computing, 14(2), 150–163. DOI: 10.1109/TDSC.2015.2494420,2017. [Google Scholar]
  16. Lee, C., & Kim, D. "Endpoint Detection and Response (EDR): Past, Present, and Future Directions." IEEE Communications Magazine, 56(8), 78–84. DOI: 10.1109/MCOM.2018.17010802018. [CrossRef] [Google Scholar]
  17. Zhang, H., & Li, X. "Deep Learning Approaches for Endpoint Detection and Response: A Survey." IEEE Transactions on Information Forensics and Security, 14(6), 1609–1623. DOI: 10.1109/TIFS.2018.28728792019 [Google Scholar]
  18. Wang, L., & Zhang, Q. "Evolution of Endpoint Detection and Response Systems: Challenges and Opportunities." IEEE Internet of Things Journal, 5(3), 2109–2118. DOI: 10.1109/JIOT.2018.28105782018. [Google Scholar]
  19. Gupta, A., & Sharma, S. "Next-Generation Endpoint Detection and Response: A Review." IEEE Transactions on Emerging Topics in Computing, 8(1), 120–133. DOI: 10.1109/TETC.2018.28751432020. [Google Scholar]
  20. Park, J., & Lee, S. "Enhancing Endpoint Detection and Response through Artificial Intelligence: A Review." IEEE Transactions on Emerging Topics in Computing, 7(4), 512–525. DOI: 10.1109/TETC.2018.2864762, 2019. [Google Scholar]
  21. Chen, Z., & Liu, W. "A Comprehensive Review of Endpoint Detection and Response (EDR) Technologies and Their Applications." IEEE Journal on Selected Areas in Communications, 35(7), 1630–1642. DOI: 10.1109/JSAC.2017.2715202,2017. [Google Scholar]
  22. A. Chaudhary and S. S. Singh, "Lung cancer detection on CT images by using image processing," 2012, pp. 142–146, DOI: 10.1109/ICCS.2012.43. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872514395&doi=10.1109%2fICCS.2012.43&partnerID=40&md5=2ea72bd2b70a8c1a88d17329baf39993 [Google Scholar]
  23. A. Khamparia, D. Gupta, V. H. C. de Albuquerque, A. K. Sangaiah, and R. H. Jhaveri, "Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning," Journal of Supercomputing, Article vol. 76, no. 11, pp. 8590–8608, 2020, DOI: 10.1007/s11227-020-03159-4. [CrossRef] [Google Scholar]
  24. A. Khamparia, P. K. Singh, P. Rani, D. Samanta, A. Khanna, and B. Bhushan, "An internet of health things-driven deep learning framework for detection and classification of skin cancer using transfer learning," Transactions on Emerging Telecommunications Technologies, Article vol. 32, no. 7, 2021, Art no. e3963, DOI: 10.1002/ett.3963. [CrossRef] [Google Scholar]
  25. S. I. Manzoor, J. Singla, and Nikita, "Fake news detection using machine learning approaches: A systematic review," 2019: Institute of Electrical and Electronics Engineers Inc., pp. 230–234, DOI: 10.1109/ICOEI.2019.8862770. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074097965&doi=10.1109%2fICOEI.2019.8862770&partnerID=40&md5=ff6d3d201ac780d0a58f35f13d8d7948 [Google Scholar]
  26. M. Nagaraju and P. Chawla, "Systematic review of deep learning techniques in plant disease detection," International Journal of System Assurance Engineering and Management, Article vol. 11, no. 3, pp. 547–560, 2020, DOI: 10.1007/s13198-020-00972-1. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.