［1］Neuschmied H, Winter M, Stojanovic B, et al. APTattack detection based on multistage autoencoders［J］. Applied Sciences, 2022, 12(13): 68166816［2］Wang Li, Li Zhitang, Wang Qihong, et al. A novel technique of recognising multistage attack behaviour［J］. International Journal of High Performance Computing & Networking, 2010, 6(34): 174180［3］Haas, S, Fischer, M. GAC: Graphbased alert correlation for the detection of distributed multistep attacks［C］ Proc of the 33rd Annual ACM Symp on Applied Computing. New York: ACM, 2018: 979988［4］Tao Xiaoling, Jia Fei, YuYuelin, et al. An intrusion alarm data association analysis method［C］ Proc of the 19th IEEE Int Conf on Mobile Ad Hoc and Smart Systems (MASS). Piscataway, NJ: IEEE, 2022: 99107［5］Ramaki A A, Amini M, Atani R E. RTECA: Real time episode correlation algorithm for multistep attack scenarios detection［J］. Computers & Security, 2015, 49: 206219［6］王文娟, 杜学绘, 单棣斌. 基于动态概率攻击图的云环境攻击场景构建方法［J］. 通信学报, 2021, 42(1): 117［7］Yang Ju, Zhou Liang, Wang Le, et al. A multistep attack detection framework for the power system network［C］ Proc of the 7th IEEE Int Conf on Data Science in Cyberspace (DSC). Piscataway, NJ: IEEE, 2022: 18［8］Holgado P, Villagrá V, Vázquez L, et al. Realtime multistep attack prediction based on hidden markov models［J］. IEEE Trans on Dependable and Secure Computing, 2017, 17(1): 134147［9］Zhang Xu, Wu Ting, Zheng Qiuhua, et al. Multistep attack detection based on pretrained hidden Markov models［J］. Sensors, 2022, 22(8): 28742874［10］Huang Yonghui, Sun Yi, Lin Kaixiang, et al. An effective reconstruction method of the APT attack based on hidden Markov model［J］. Journal of Circuits, Systems and Computers, 2022, 31(6): 22501082250108［11］Ramaki Ali Ahmadian, KhosraviFarmad Masoud, Bafghi Abbas Ghaemi. Real time alert correlation and prediction using Bayesian networks［C］ Proc of the 12th Int Iranian Society of Cryptology Conf on Information Security and Cryptology (ISCISC). Piscataway, NJ: IEEE, 2015: 99103［12］Liu Jianyi, Liu Bowen, Zhang Ru, et al. Multistep attack scenarios mining based on neural network and Bayesian network attack graph［C］ Proc of the 5th Int Conf on Artificial Intelligence and Security. Piscataway, NJ: IEEE, 2019: 6274［13］Zhou Peng, Zhou Gongyan, Wu Dakui, et al. Detecting multistage attacks using sequencetosequence model［J］. Computers & Security, 2021, 105: 102203102203［14］Mao Beifeng, Liu Jing, Lai Yingxu, et al. MIF: A multistep attack scenario reconstruction and attack chains extraction method based on multiinformation fusion［J］. Computer Networks, 2021, 198: 108340108340［15］Cheng Qiumei, Wu Chunming, Zhou Shiying. Discovering attack scenarios via intrusion alert correlation using graph convolutional networks［J］. IEEE Communications Letters, 2021, 25(5): 15641567［16］李洪成, 吴晓平. 支持告警序列差分隐私保护的网络入侵关联方法［J］. 计算机工程, 2018, 44(5): 128132［17］Li Zitong, Chen Jiale, Zhang Jiale, et al. Detecting advanced persistent threat in edge computing via federated learning［C］ Proc of the 1st Int Conf on Security and Privacy in Digital Economy. Berlin: Springer, 2020: 518523［18］Sun Xiaoyan, Dai Jun, Liu Peng, et al. Using Bayesian networks for probabilistic identification of zeroday attack paths［J］. IEEE Trans on Information Forensics and Security, 2018, 13(10): 25062521［19］Milajerdi S M, Gjomemo R, Eshete B, et al. Holmes: Realtime APT detection through correlation of suspicious information flows［C］ Proc of 2019 IEEE Symp on Security and Privacy (SP). San Francisco, CA: IEEE, 2019: 11371152
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