[2]
A.K. Sood, and R.J. Enbody, "Targeted cyberattacks: A superset of advanced persistent threats", IEEE Secur. Priv., vol. 11, no. 1, pp. 54-61, 2012.
[7]
Y. Su, Y. Zhao, and C. Niu, "Robust anomaly detection for multivariate time series through stochastic recurrent neural network",. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining., 2019, pp. 2848-2856
[8]
K. Hundman, V. Constantinou, and C. Laporte, "Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding",. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining., 2018, pp. 387-395
[9]
J. Audibert, P. Michiardi, and F. Guyard, "Unsupervised anomaly detection on multivariate time series",. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 3395-3404
[10]
D. Li, D. Chen, and B. Jin, "Text and Time Series",. In 28th International Conference on Artificial Neural Networks, Munich, Germany, 2019, pp. 703-716
[11]
C. Do Xuan, "Detecting APT attacks based on network traffic using machine learning", J. Web Eng., vol. 20, no. 1, 2021.
[13]
T. Schindler, "Anomaly detection in log data using graph databases and machine learning to defend advanced persistent threats", arXiv:1802.00259, vol. 2018, 2018.
[14]
W.U. Hassan, A. Bates, and D. Marino, "Tactical provenance analysis for endpoint detection and response systems",. 2020 IEEE Symposium on Security and Privacy (SP). IEEE., 2020, pp. 1172-1189. San Francisco
[16]
Z. Zhang, and C. Kang, "Challenges and prospects for building a new power system under the goal of carbon neutrality", Zhongguo Dianji Gongcheng Xuebao, vol. 42, no. 8, p. 13, 2022.
[18]
G. Draper-Gil, A.H. Lashkari, and M.S.I. Mamun, "Characterization of encrypted and vpn traffic using time-related", In Proceedings of the 2nd international conference on information systems security and privacy (ICISSP), Italy, 2016, pp. 407-414
[19]
A. Dijk, "Detection of Advanced Persistent Threats using Artificial Intelligence for Deep Packet Inspection", In 2021 IEEE International Conference on Big Data (Big Data). IEEE., Orlando, FL, 2021, pp. 2092-2097
[20]
P. Malhotra, A. Ramakrishnan, and G. Anand, "LSTM-based encoder-decoder for multi-sensor anomaly detection", arXiv:1607.00148, 2016.
[23]
S. Myneni, A. Chowdhary, and A. Sabur, "DAPT 2020 - Constructing a Benchmark Dataset for Advanced Persistent Threats",. In International Workshop on Deployable Machine Learning for Security Defense., 2020, pp. 138-163 San Diego, CASpringer International Publishing,
[24]
N. Moustafa, and J. Slay, "UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)",. In Military Communications and Information Systems Conference MilCIS), 2015. IEEE., 2015. Canberra
[25]
A Saha, and A. Das, "A detailed analysis of the issues and solutions for securing data", Iosrjournals Org., vol. 4, no. 5, 2012.
[26]
L. Dhanabal, and S P Shantharajah, "A study on NSL-KDD dataset for intrusion detection system based on classification algorithms", Int. J. adv. res. comput. commun. eng., vol. 4, no. 6, 2015.
[29]
R K Cunningham, R P Lippmann, and D J Fried, "Evaluating intrusion detection systems without attacking your friends: The 1998 darpa intrusion detection evaluation",. Defense Technical Information Center, vol. 1999. 1999.
[30]
R. Wagner, M. Fredrikson, and D. Garlan, "An Advanced Persistent Threat Exemplar", Defense Technical Information Center, vol. 2017, 2017.