참고문헌
- Caleb T. Carr & Rebecca A. Hayes (2015) Social Media: Defining, Developing, and Divining, Atlantic Journal of Communication, 23:1, 46-65, DOI: 10.1080/15456870.2015.972282
- Jose Luis Lalueza, Isabel Crespo and Marc Bria, Microcultures, Local Communities, and Virtual Networks, Chapter IX in Handbook of Research on Digital Information Technologies: Innovations, Methods, and Ethical Issues , Copyright: © 2008 |Pages: 14 DOI: 10.4018/978-1-59904-970-0.ch009
- Nyagadza, Brighton, and Brighton Nyagadza. "Search Engine Marketing and Social Media Marketing Predictive Trends." Journal of Digital Media & Policy, 2020. doi:10.1386/jdmp_00027_1.
- Ravneet Singh Bhandari1 , Ajay Bansal2, Sanjeela Mathur3 and Harikishni Nain, Privacy Concern Behaviour on Social Media Sites: A Comparative Analysis of Urban and Rural Users, FIIB Business Review, 1-13, 2022, https://doi.org/10.1177%2F23197145221078106 https://doi.org/10.1177%2F23197145221078106
- A. Badawy, E. Ferrara and K. Lerman, "Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018, pp. 258-265, doi: 10.1109/ASONAM.2018.8508646.
- Mingmin Zhang, Ping Xu, Yinjiao Ye, Trust in social media brands and perceived media values: A survey study in China, Computers in Human Behavior, Volume 127, 2022, 107024, ISSN 0747-5632, https://doi.org/10.1016/j.chb.2021.107024.
- Dwivedi, Yogesh K., Kawaljeet Kaur Kapoor, and Hsin Chen. "Social media marketing and advertising." The Marketing Review 15.3 (2015): 289-309. https://doi.org/10.1362/146934715X14441363377999
- Chris Norval, Heleen Janssen, Jennifer Cobbe and Jatinder Singh, Data protection and tech startups: The needfor attention, support, and scrutiny, Policy Internet. 2021;13:278-299, https://doi.org/10.1002/poi3.255
- Jeffrey A. Hall, Dong Liu, Social media use, social displacement, and well-being, Current Opinion in Psychology, Volume 46, 2022, https://doi.org/10.1016/j.copsyc.2022.101339.
- Chetioui K, Bah B, Alami AO, Bahnasse A. Overview of Social Engineering Attacks on Social Networks. Procedia Computer Science. 2022 Jan 1;198:656-61. https://doi.org/10.1016/j.procs.2021.12.302
- Irshad S, Soomro TR. Identity theft and social media. International Journal of Computer Science and Network Security. 2018 Jan 30;18(1):43-55
- Treyger E, Cheravitch J, Cohen R. Russian Disinformation Efforts on Social Media. RAND CORP SANTA MONICA CA; 2022 Jun 7.
- Barbier G, Liu H. Data mining in social media. InSocial network data analytics 2011 (pp. 327-352). Springer, Boston, MA.
- Tharani JS, Arachchilage NA. Understanding phishers' strategies of mimicking uniform resource locators to leverage phishing attacks: A machine learning approach. Security and Privacy. 2020 Sep;3(5):e120, DOI: 10.1002/spy2.120
- Le Page S, Jourdan GV, Bochmann GV, Flood J, Onut IV. Using url shorteners to compare phishing and malware attacks. In2018 APWG Symposium on Electronic Crime Research (eCrime) 2018 May 15 (pp. 1-13). IEEE, DOI: 10.1109/ECRIME.2018.8376215
- Kumar S, Carley KM. Understanding DDoS cyber-attacks using social media analytics. In2016 IEEE Conference on Intelligence and Security Informatics (ISI) 2016 Sep 28 (pp. 231-236). IEEE, DOI: 10.1109/ISI.2016.7745480
- Derhab A, Alawwad R, Dehwah K, Tariq N, Khan FA, AlMuhtadi J. Tweet-based bot detection using big data analytics. IEEE Access. 2021 Apr 22;9:65988-6005, DOI: 10.1109/ACCESS.2021.3074953
- Zhang C, Ma Y, editors. Ensemble machine learning: methods and applications. Springer Science & Business Media; 2012 Feb 17, https://link.springer.com/content/pdf/10.1007/978-1-4419-9326-7.pdf
- B. V. Dasarathy and B. V. Sheela, "Composite classifier system design: concepts and methodology," Proceedings of the IEEE, vol. 67, no. 5, pp. 708-713, 1979, DOI: 10.1109/PROC.1979.11321
- Y. Freund and R. E. Schapire, "Decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119-139, 1997, https://doi.org/10.1006/jcss.1997.1504
- L. Breiman, "Bagging predictors," Machine Learning, vol. 24, no. 2, pp. 123-140, 1996, https://doi.org/10.1007/BF00058655
- Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE. Adaptive mixtures of local experts. Neural computation. 1991 Mar;3(1):79-87, DOI: 10.1162/neco.1991.3.1.79
- Jordan MI, Jacobs RA. Hierarchical mixtures of experts and the EM algorithm. Neural computation. 1994 Mar;6(2):181-214, DOI: 10.1162/neco.1994.6.2.181
- Benediktsson JA, Swain PH. Consensus theoretic classification methods. IEEE transactions on Systems, Man, and Cybernetics. 1992 Jul;22(4):688-704, DOI: 10.1109/21.156582
- Xu L, Krzyzak A, Suen CY. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE transactions on systems, man, and cybernetics. 1992 May;22(3):418-35, DOI: 10.1109/21.155943
- Ho TK, Hull JJ, Srihari SN. Decision combination in multiple classifier systems. IEEE transactions on pattern analysis and machine intelligence. 1994 Jan;16(1):66-75, DOI: 10.1109/34.273716
- Rogova, G. (2008). Combining the Results of Several Neural Network Classifiers. In: Yager, R.R., Liu, L. (eds) Classic Works of the Dempster-Shafer Theory of Belief Functions. Studies in Fuzziness and Soft Computing, vol 219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44792-4_27
- Lam L, Suen CY. Optimal combinations of pattern classifiers. Pattern Recognition Letters. 1995 Sep 1;16(9):945-54, https://doi.org/10.1016/0167-8655(95)00050-Q
- Woods K, Kegelmeyer WP, Bowyer K. Combination of multiple classifiers using local accuracy estimates. IEEE transactions on pattern analysis and machine intelligence. 1997 Apr;19(4):405-10, DOI: 10.1109/34.588027
- Wolpert DH. Stacked generalization. Neural networks. 1992 Jan 1;5(2):241-59. https://doi.org/10.1016/S0893-6080(05)80023-1
- Ho TK. The random subspace method for constructing decision forests. IEEE transactions on pattern analysis and machine intelligence. 1998 Aug;20(8):832-44, DOI: 10.1109/34.709601
- Kuncheva LI. Combining pattern classifiers: methods and algorithms. John Wiley & Sons; 2014 Sep 9, https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.365.2334&rep=rep1&type=pdf
- Banfield RE, Hall LO, Bowyer KW, Kegelmeyer WP. Ensemble diversity measures and their application to thinning. Information Fusion. 2005 Mar 1;6(1):49-62, https://doi.org/10.1016/j.inffus.2004.04.005
- Kuncheva, L.I., Whitaker, C.J. Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning 51, 181-207 (2003). https://doi.org/10.1023/A:1022859003006
- Kuncheva, L.I. (2003). That Elusive Diversity in Classifier Ensembles. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_130
- Dietterich, T.G. (2000). Ensemble Methods in Machine Learning. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_1
- E. Filippi, M. Costa and E. Pasero, "Multi-layer perceptron ensembles for increased performance and fault-tolerance in pattern recognition tasks," Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), 1994, pp. 2901-2906 vol.5, doi: 10.1109/ICNN.1994.374692.
- Healey SP, Cohen WB, Yang Z, Brewer CK, Brooks EB, Gorelick N, Hernandez AJ, Huang C, Hughes MJ, Kennedy RE, Loveland TR. Mapping forest change using stacked generalization: An ensemble approach. Remote Sensing of Environment. 2018 Jan 1;204:717-28, https://doi.org/10.1016/j.rse.2017.09.029
- Zhang B, Luo L, Liu X, Li J, Chen Z, Zhang W, Wei X, Hao Y, Tsang M, Wang W, Liu Y. DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction. arXiv preprint arXiv:2203.11014. 2022 Mar 11, https://doi.org/10.48550/arXiv.2203.11014
- S. Haider et al., "A Deep CNN Ensemble Framework for Efficient DDoS Attack Detection in Software Defined Networks," in IEEE Access, vol. 8, pp. 53972-53983, 2020, doi: 10.1109/ACCESS.2020.2976908.
- Osanaiye, O., Cai, H., Choo, KK.R. et al. Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. J Wireless Com Network 2016, 130 (2016). https://doi.org/10.1186/s13638-016-0623-3
- S. Das, D. Venugopal, S. Shiva and F. T. Sheldon, "Empirical Evaluation of the Ensemble Framework for Feature Selection in DDoS Attack," 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), 2020, pp. 56-61, doi: 10.1109/CSCloud-EdgeCom49738.2020.00019.
- Jia B, Huang X, Liu R, Ma Y. A DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning. Journal of Electrical and Computer Engineering. 2017 Mar 15;2017, https://doi.org/10.1155/2017/4975343
- Hansrajh A, Adeliyi TT, Wing J. Detection of online fake news using blending ensemble learning. Scientific Programming. 2021 Jul 29;2021, https://doi.org/10.1155/2021/3434458
- Ahmad I, Yousaf M, Yousaf S, Ahmad MO. Fake news detection using machine learning ensemble methods. Complexity. 2020 Oct 17;2020, https://doi.org/10.1155/2020/8885861
- Khan, M.Z., Alhazmi, O.H. Study and analysis of unreliable news based on content acquired using ensemble learning (prevalence of fake news on social media). Int J Syst Assur Eng Manag 11, 145-153 (2020). https://doi.org/10.1007/s13198-020-01016-4
- Fayaz M, Khan A, Rahman JU, Alharbi A, Uddin MI, Alouffi B. Ensemble machine learning model for classification of spam product reviews. Complexity. 2020 Dec 18;2020, https://doi.org/10.1155/2020/8857570
- Li G, Shen M, Li M, Cheng J. Personal Credit Default Discrimination Model Based on Super Learner Ensemble. Mathematical Problems in Engineering. 2021 Mar 31;2021, https://doi.org/10.1155/2021/5586120
- Xiaojun C, Zicheng W, Yiguo P, Jinqiao S. A continuous reauthentication approach using ensemble learning. Procedia Computer Science. 2013 Jan 1;17:870-8, https://doi.org/10.1016/j.procs.2013.05.111
- Choi, J. A., & Lim, K. (2020). Identifying machine learning techniques for classification of target advertising. ICT Express, 6(3), 175-180, https://doi.org/10.1016/j.icte.2020.04.012
- Garcia-Mendez S, Leal F, Malheiro B, Burguillo-Rial JC, Veloso B, Chis AE, Gonzalez-Velez H. Simulation, modelling and classification of wiki contributors: Spotting the good, the bad, and the ugly. Simulation Modelling Practice and Theory. 2022 Nov 1;120:102616, https://doi.org/10.1016/j.simpat.2022.102616
- Zhan, X., You, Z., Yu, C., Li, L., & Pan, J. (2020). Ensemble learning prediction of drug-target interactions using GIST descriptor extracted from PSSM-based evolutionary information. BioMed Research International, 2020, https://doi.org/10.1155/2020/4516250
- Sanober, S., Alam, I., Pande, S., Arslan, F., Rane, K. P., Singh, B. K., ... & Shabaz, M. (2021). An enhanced secure deep learning algorithm for fraud detection in wireless communication. Wireless Communications and Mobile Computing, 2021, https://doi.org/10.1155/2021/6079582
- Mao, Z., Fang, Z., Li, M., & Fan, Y. (2022). EvadeRL: Evading PDF Malware Classifiers with Deep Reinforcement Learning. Security and Communication Networks, 2022, https://doi.org/10.1155/2022/7218800
- Anand, P. M., Kumar, T. G., & Charan, P. S. (2020). An ensemble approach for algorithmically generated domain name detection using statistical and lexical analysis. Procedia Computer Science, 171, 1129-1136, https://doi.org/10.1016/j.procs.2020.04.121
- Chen, Y., Chen, H., Zhang, Y., Han, M., Siddula, M., & Cai, Z. (2022). A survey on blockchain systems: Attacks, defenses, and privacy preservation. High-Confidence Computing, 2(2), 100048, https://doi.org/10.1016/j.hcc.2021.100048
- Saheed, Y. K., Abiodun, A. I., Misra, S., Holone, M. K., & Colomo-Palacios, R. (2022). A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Engineering Journal, 61(12), 9395-9409, https://doi.org/10.1016/j.aej.2022.02.063
- Bijalwan, A., Chand, N., Pilli, E. S., & Krishna, C. R. (2016). Botnet analysis using ensemble classifier. Perspectives in Science, 8, 502-504, https://doi.org/10.1016/j.pisc.2016.05.008
- Tebenkov, E., & Prokhorov, I. (2021). Machine learning algorithms for teaching AI chat bots. Procedia Computer Science, 190, 735-744, https://doi.org/10.1016/j.procs.2021.06.086
- Suchacka, G., Cabri, A., Rovetta, S., & Masulli, F. (2021). Efficient on-the-fly Web bot detection. Knowledge-Based Systems, 223, 107074, https://doi.org/10.1016/j.knosys.2021.107074
- Xie, Y., Li, A., Gao, L., & Liu, Z. (2021). A heterogeneous ensemble learning model based on data distribution for credit card fraud detection. Wireless Communications and Mobile Computing, 2021, https://doi.org/10.1155/2021/2531210
- Yan, J., Qi, Y., & Rao, Q. (2018). Detecting malware with an ensemble method based on deep neural network. Security and Communication Networks, 2018, https://doi.org/10.1155/2018/7247095
- Xu, H., Fan, G., & Song, Y. (2022). Application Analysis of the Machine Learning Fusion Model in Building a Financial Fraud Prediction Model. Security and Communication Networks, 2022, https://doi.org/10.1155/2022/8402329
- Shatnawi, A. S., Jaradat, A., Yaseen, T. B., Taqieddin, E., AlAyyoub, M., & Mustafa, D. (2022). An Android Malware Detection Leveraging Machine Learning. Wireless Communications and Mobile Computing, 2022, https://doi.org/10.1155/2022/1830201
- Martin, I., Hernandez, J. A., Munoz, A., & Guzman, A. (2018). Android malware characterization using metadata and machine learning techniques. Security and Communication Networks, 2018, https://doi.org/10.1155/2018/5749481
- Xiao, F., Lin, Z., Sun, Y., & Ma, Y. (2019). Malware detection based on deep learning of behavior graphs. Mathematical Problems in Engineering, 2019, https://doi.org/10.1155/2019/8195395
- Gera, T., Singh, J., Mehbodniya, A., Webber, J. L., Shabaz, M., & Thakur, D. (2021). Dominant feature selection and machine learning-based hybrid approach to analyze android ransomware. Security and Communication Networks, 2021, https://doi.org/10.1155/2021/7035233
- Park, S., & Choi, J. Y. (2020). Malware detection in selfdriving vehicles using machine learning algorithms. Journal of advanced transportation, 2020, https://doi.org/10.1155/2020/3035741
- Lu, T., Du, Y., Ouyang, L., Chen, Q., & Wang, X. (2020). Android malware detection based on a hybrid deep learning model. Security and Communication Networks, 2020, https://doi.org/10.1155/2020/8863617
- Subasi, A., Balfaqih, M., Balfagih, Z., & Alfawwaz, K. (2021). A Comparative Evaluation of Ensemble Classifiers for Malicious Webpage Detection. Procedia Computer Science, 194, 272-279, https://doi.org/10.1016/j.procs.2021.10.082
- Hota, H. S., Shrivas, A. K., & Hota, R. (2018). An ensemble model for detecting phishing attack with proposed removereplace feature selection technique. Procedia computer science, 132, 900-907, https://doi.org/10.1016/j.procs.2018.05.103
- Orunsolu AA, Sodiya AS, Akinwale AT. A predictive model for phishing detection. Journal of King Saud UniversityComputer and Information Sciences. 2019 Dec 24, https://doi.org/10.1016/j.jksuci.2019.12.005
- AbdulNabi, I., & Yaseen, Q. (2021). Spam email detection using deep learning techniques. Procedia Computer Science, 184, 853-858, https://doi.org/10.1016/j.procs.2021.03.107