462 工程科学学报,第42卷,第4期 Communication and Network Security.Qingdao,2018:74 Melbourne,2003:20 [8]Mnih V.Kavukcuoglu K.Silver D.et al.Human-level control [13]Willems C,Holz T,Freiling F.Toward automated dynamic through deep reinforcement leaming.Nature,2015,518(7540): malware analysis using CWSandbox.IEEE Secur Privacy,2007, 529 5(2):32 [9]Schultz M,Eskin E,Zadok F,et al.Data mining methods for [14]Rieck K,Trinius P,Willems C,et al.Automatic analysis of detection of new malicious executables Proceedings of the IEEE malware behavior using machine learning.J Comput Secur,2011, Symposium on Securiry and Privacy.Oakland,2001:38 19(4):639 [10]Santos I,Brezo F,Ugarte-Pedrero X,et al.Opcode sequences as [15]Ki Y,Kim E,Kim H K.A novel approach to detect malware based representation of executables for data-mining-based unknown on API call sequence analysis.Int J Distrib Sens Nen,2015, malware detection.InfSci,2013,231:64 11(6):659101 [11]Zhang J X,Qin Z,Yin H,et al.IRMD:Malware variant detection [16]Busoniu L,Babuska R,De Schutter B.A comprehensive survey of using opcode image recognition Proceedings of the IEEE 22nd multiagent reinforcement learning.IEEE Trans Syst Man Cybern International Conference on Parallel and Distributed Systems Part CAppl Rev,2008,38(2):156 Wuhan,2016:1175 [17]Zhang T Y,Huang M L,Zhao L,et al.Learning structured [12]Tandon G,Chan P.Learning rules from system call arguments and representation for text classification via reinforcement learning / sequences for anomaly detection /Proceedings of the Proceedings of the Thirty-Second AAAl Conference on Artificial International Workshop on Data Mining for Computer Security. Intelligence.New Orleans,2018:6053Communication and Network Security. Qingdao, 2018: 74 Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning. Nature, 2015, 518(7540): 529 [8] Schultz M, Eskin E, Zadok F, et al. Data mining methods for detection of new malicious executables // Proceedings of the IEEE Symposium on Security and Privacy. Oakland, 2001: 38 [9] Santos I, Brezo F, Ugarte-Pedrero X, et al. Opcode sequences as representation of executables for data-mining-based unknown malware detection. Inf Sci, 2013, 231: 64 [10] Zhang J X, Qin Z, Yin H, et al. IRMD: Malware variant detection using opcode image recognition // Proceedings of the IEEE 22nd International Conference on Parallel and Distributed Systems. Wuhan, 2016: 1175 [11] Tandon G, Chan P. Learning rules from system call arguments and sequences for anomaly detection // Proceedings of the International Workshop on Data Mining for Computer Security. [12] Melbourne, 2003: 20 Willems C, Holz T, Freiling F. Toward automated dynamic malware analysis using CWSandbox. IEEE Secur Privacy, 2007, 5(2): 32 [13] Rieck K, Trinius P, Willems C, et al. Automatic analysis of malware behavior using machine learning. J Comput Secur, 2011, 19(4): 639 [14] Ki Y, Kim E, Kim H K. A novel approach to detect malware based on API call sequence analysis. Int J Distrib Sens Netw, 2015, 11(6): 659101 [15] Busoniu L, Babuška R, De Schutter B. A comprehensive survey of multiagent reinforcement learning. IEEE Trans Syst Man Cybern Part C Appl Rev, 2008, 38(2): 156 [16] Zhang T Y, Huang M L, Zhao L, et al. Learning structured representation for text classification via reinforcement learning // Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. New Orleans, 2018: 6053 [17] · 462 · 工程科学学报,第 42 卷,第 4 期