[1]John A, Isnin B F I, Madni H H S, et al. Clusterbased wireless sensor network framework for denialofservice attack detection based on variable selection ensemble machine learning algorithms[J]. Intelligent Systems with Applications, 2024, 22: 26673053[2]Yan Y, Kunhui Y. Novel cyberphysical architecture for optimal operation of renewablebased smart city considering false data injection attacks: Digital twin technologies for smart city infrastructure management[J]. Sustainable Energy Technologies and Assessments, 2024, 65: 22131388[3]李中伟, 佟为明, 金显吉. 智能电网信息安全防御体系与信息安全测试系统构建乌克兰和以色列国家电网遭受网络攻击事件的思考与启示[J]. 电力系统自动化, 2016, 40(8): 147151[4]曹旭栋, 黄在起, 陈禹劼, 等. 安全漏洞库构建及应用研究综述[J]. 计算机学报, 2024, 47(5): 10821119[5]李佳琳, 王雅哲, 罗吕根, 等. 面向安卓恶意软件检测的对抗攻击技术综述[J]. 信息安全学报, 2021, 6(4): 2843[6]Amer E, ElSappagh S, Hu J W. Contextual identification of windows malware through semantic interpretation of API call sequence[J]. Applied Sciences, 2020, 10(21): 7673[7]Obeis N T, Bhaya W. Malware analysis using APIs pattern mining[J]. International Journal of Engineering & Technology, 2018, 7: 502506[8]段晓云. 基于Windows API调用行为的恶意软件检测研究[D]. 成都: 西南交通大学, 2016[9]金逸灵, 陈兴蜀, 王玉龙. 基于LSTMCNN的容器内恶意软件静态检测[J]. 计算机应用研究, 2020, 37(12): 37043707, 3711[10]Li C, Zheng J. API callbased malware classification using recurrent neural networks[J]. Journal of Cyber Security and Mobility, 2021, 10(3): 617640
|