Page 321 - 《软件学报》2021年第5期

马钰锡 等:面向智能攻击的行为预测研究 1545

[45] Hernández A, Sanchez V, Sánchez G, et al. Security attack prediction based on user sentiment analysis of Twitter data. In: Proc. of
the 2016 IEEE Int’l Conf. on Industrial Technology (ICIT). IEEE, 2016. 610−617.
[46] Banerjee M, Agarwal B, Samantaray SD. An integrated approach for botnet detection and prediction using honeynet and socialnet
data. In: Proc. of the Int’l Conf. on Intelligent Computing and Smart Communication 2019. Singapore: Springer-Verlag, 2020.
423−431.
[47] Lim H, Kim W, Noh H, et al. Research on malware classification with network activity for classification and attack prediction of
attack groups. Journal of the Korean Institute of Communication Sciences, 2017,42(1):193−204.
[48] Vaishnavi N, Thiyagarajan K. A study on prediction of malicious program using classification based approches. Int’l Journal of
Computer Science and Mobile Computing, 2018,7(5):38−46.
[49] Wang Z, Gao HZ, Zhang YM, et al. Fortifying botnet classification based on venn-abers prediction. In: Proc. of the 2017 2nd Int’l
Conf. on Computer Science and Technology (CST 2017). 2017.
[50] Mursleen M, Bist AS, Kishore J. A support vector machine water wave optimization algorithm based prediction model for
metamorphic malware detection. Int’l Journal of Recent Technology and Engineering, 2019,7:1−8.
[51] Roseline SA, Sasisri AD, Geetha S, et al. Towards efficient malware detection and classification using multilayered random forest
ensemble technique. In: Proc. of the 2019 Int’l Carnahan Conf. on Security Technology (ICCST). IEEE, 2019. 1−6.
[52] Kou G, Wang S, Tang G. Research on key technologies of network security situational awareness for attack tracking prediction.
Chinese Journal of Electronics, 2019,28(1):162−171.
[53] Hopfield JJ. Artificial neural networks. IEEE Circuits and Devices Magazine, 1988,4(5):3−10.
[54] Khashei M, Bijari M. An artificial neural network (p,d,q) model for timeseries forecasting. Expert Systems with applications,
2010,37(1):479−489.
[55] Rhode M, Burnap P, Jones K. Early-stage malware prediction using recurrent neural networks. Computers & Security, 2018,77:
578−594.
[56] Sundermeyer M, Schlüter R, Ney H. LSTM neural networks for language modeling. In: Proc. of the Interspeech. 2012. 601−608.
[57] Fang X, Xu M, Xu S, et al. A deep learning framework for predicting cyber attacks rates. EURASIP Journal on Information
Security, 2019,2019(1):Article No.5. [doi: 10.1186/s13635-019-0090-6]
[58] Fan S, Wu S, Wang Z, et al. ALEAP: Attention-based LSTM with event embedding for attack projection. In: Proc. of the 2019
IEEE 38th Int’l Performance Computing and Communications Conf. (IPCCC). IEEE, 2019. 1−8.
[59] Hasan KMZ, Hasan MZ, Zahan N. Automated prediction of phishing websites using deep convolutional neural network. In: Proc.
of the 2019 Int’l Conf. on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2). IEEE, 2019.
1−4.
[60] Roughgarden T. Algorithmic game theory. Communications of the ACM, 2010,53(7):78−86.
[61] Zhang Y, Liu J. Optimal decision-making approach for cyber security defense using game theory and intelligent learning. Security
and Communication Networks, 2019,2019(2):1−16.
[62] Phillips C, Swiler LP. A graph-based system for network-vulnerability analysis. In: Proc. of the ’98 Workshop on New Security
Paradigms (NSPW’98). New York: Association for Computing Machinery, 1998. 71−79.
[63] Abaid Z, Sarkar D, Kaafar MA, et al. The early bird gets the botnet: A Markov chain based early warning system for botnet attacks.
In: Proc. of the 2016 IEEE 41st Conf. on Local Computer Networks (LCN). IEEE, 2016. 61−68.
[64] Han J, Kamber M, Pei J. Data mining: Concepts and techniques third edition. The Morgan Kaufmann Series in Data Management
Systems, 2011,5(4):83−124.
[65] Mohammad RM, Thabtah F, McCluskey L. Intelligent rule-based phishing websites classification. IET Information Security, 2014,
8(3):153−160.
[66] Al-diabat M. Detection and prediction of phishing websites using classification mining techniques. Int’l Journal of Computer
Applications, 2016,147(5):5−11.
[67] Lin YS, Jiang JY, Lee SJ. A similarity measure for text classification and clustering. IEEE Trans. on Knowledge and Data
Engineering, 2013,26(7):1575−1590.