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A Cybersecurity Threat Recognition Framework Combining GAN Networks and Semi-Supervised Learning

  
03 set 2024
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Xu, G., Cao, Y., Ren, Y., Li, X., & Feng, Z. (2017). Network security situation awareness based on semantic ontology and user-defined rules for Internet of Things. IEEE Access, 5, 21046-21056. Search in Google Scholar

Ferdiana, R. (2020, November). A systematic literature review of intrusion detection system for network security: Research trends, datasets and methods. In 2020 4th International Conference on Informatics and Computational Sciences (ICICoS) (pp. 1-6). IEEE. Search in Google Scholar

Qamar, S., Anwar, Z., Rahman, M. A., Al-Shaer, E., & Chu, B. T. (2017). Data-driven analytics for cyber-threat intelligence and information sharing. Computers & Security, 67, 35-58. Search in Google Scholar

Yuan, F., Cao, Y., Shang, Y., Liu, Y., Tan, J., & Fang, B. (2018). Insider threat detection with deep neural network. In Computational Science–ICCS 2018: 18th International Conference, Wuxi, China, June 11– 13, 2018, Proceedings, Part I 18 (pp. 43-54). Springer International Publishing. Search in Google Scholar

Gupta, R., Tanwar, S., Tyagi, S., & Kumar, N. (2020). Machine learning models for secure data analytics: A taxonomy and threat model. Computer Communications, 153, 406-440. Search in Google Scholar

Yuan, S., & Wu, X. (2021). Deep learning for insider threat detection: Review, challenges and opportunities. Computers & Security, 104, 102221. Search in Google Scholar

Hindy, H., Brosset, D., Bayne, E., Seeam, A. K., Tachtatzis, C., Atkinson, R., & Bellekens, X. (2020). A taxonomy of network threats and the effect of current datasets on intrusion detection systems. IEEE Access, 8, 104650-104675. Search in Google Scholar

Hu, T., Niu, W., Zhang, X., Liu, X., Lu, J., & Liu, Y. (2019). An insider threat detection approach based on mouse dynamics and deep learning. Security and communication networks, 2019(1), 3898951. Search in Google Scholar

Gupta, S., Sabitha, A. S., & Punhani, R. (2019). Cyber security threat intelligence using data mining techniques and artificial intelligence. Int. J. Recent Technol. Eng, 8, 6133-6140. Search in Google Scholar

Lin, W. H., Lin, H. C., Wang, P., Wu, B. H., & Tsai, J. Y. (2018, April). Using convolutional neural networks to network intrusion detection for cyber threats. In 2018 IEEE International Conference on Applied System Invention (ICASI) (pp. 1107-1110). IEEE. Search in Google Scholar

Joshi, C., & Singh, U. K. (2017). Information security risks management framework–A step towards mitigating security risks in university network. Journal of Information Security and Applications, 35, 128-137. Search in Google Scholar

Rahim, R., Nguyen, P. T., & Shankar, K. (2019). Green data science in cyber security: network security threat detection and prevention techniques. Opción: Revista de Ciencias Humanas y Sociales, (20), 808-822. Search in Google Scholar

Rathore, S., Sharma, P. K., Loia, V., Jeong, Y. S., & Park, J. H. (2017). Social network security: Issues, challenges, threats, and solutions. Information sciences, 421, 43-69. Search in Google Scholar

Gu, Z., Nazir, S., Hong, C., & Khan, S. (2020). Convolution Neural NetworkBased Higher Accurate Intrusion Identification System for the Network Security and Communication. Security and Communication Networks, 2020(1), 8830903. Search in Google Scholar

Gao, Y., Li, X., Peng, H., Fang, B., & Philip, S. Y. (2020). Hincti: A cyber threat intelligence modeling and identification system based on heterogeneous information network. IEEE Transactions on Knowledge and Data Engineering, 34(2), 708-722. Search in Google Scholar

Lee, J., Kim, J., Kim, I., & Han, K. (2019). Cyber threat detection based on artificial neural networks using event profiles. Ieee Access, 7, 165607-165626. Search in Google Scholar

Ullah, F., Naeem, H., Jabbar, S., Khalid, S., Latif, M. A., Al-Turjman, F., & Mostarda, L. (2019). Cyber security threats detection in internet of things using deep learning approach. IEEE access, 7, 124379-124389. Search in Google Scholar

Cui, A. J., & WANG, X. M. (2019, October). Real-time early warning of network security threats based on improved ant colony algorithm. In 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA) (pp. 309-316). IEEE. Search in Google Scholar

Ge Huilin,Dai Yuewei,Zhu Zhiyu & Wang Biao.(2021).A Robust Face Recognition Algorithm Based on an Improved Generative Confrontation Network.Applied Sciences(24),11588-11588. Search in Google Scholar

Hu Jiaxin,Ge Zhaohui & Wang Xiaohua.(2022).The Psychological Education Strategy of Music Generation and Creation by Generative Confrontation Network under Deep Learning..Computational intelligence and neuroscience3847415-3847415. Search in Google Scholar

Dongliang Ma,Jine Wei,Likai Zhu,Fang Zhao,Hao Wu,Xi Chen... & Min Liu.(2024).Semi-supervised learning advances species recognition for aquatic biodiversity monitoring.Frontiers in Marine Science Search in Google Scholar

Linxuan Song,Wenxuan Tu,Sihang Zhou & En Zhu.(2024).GANN: Graph Alignment Neural Network for semi-supervised learning.Pattern Recognition110484-. Search in Google Scholar

Yin Jiao,Chen Guihong,Hong Wei,Cao Jinli,Wang Hua & Miao Yuan.(2024).A heterogeneous graph-based semi-supervised learning framework for access control decision-making.World Wide Web(4), Search in Google Scholar

Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro