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• Kaynakça
• [1] Daemen, J., & Rijmen, V. (2013). The design of Rijndael: AES-the advanced encryption standard. Springer Science & Business Media.
• [2] Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). ACM.
• [3] Mangal, A., & Kumar, N. (2016, December). Using big data to enhance the bosch production line performance: A Kaggle challenge. In 2016 IEEE International Conference on Big Data (Big Data) (pp. 2029-2035). IEEE.
• [4] Zhou, Z. H., & Feng, J. (2017). Deep Forest. arXiv preprint arXiv:1702.08835.
• [5] Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
• [6] Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63(1), 3-42.
• [7] Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.
• [8] Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1), 119-139.
• [9] Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, 28(2), 337-407.
• [10] Natekin, A., & Knoll, A. (2013). Gradient boosting machines, a tutorial. Frontiers in neurorobotics, 7, 21.
• [11] Page, D. (2002). Theoretical use of cache memory as a cryptanalytic side-channel. IACR Cryptology ePrint Archive, 2002(169).
• [12] Patterson, D. A., & Hennessy, J. L. (2013). Computer organization and design MIPS edition: the hardware/software interface. Newnes.
• [13] Canteaut, A., Lauradoux, C., & Seznec, A. (2006). Understanding cache attacks.
• [14] Younis, Y., Kifayat, K., & Merabti, M. (2014, October). Cache side-channel attacks in cloud computing. In International Conference on Cloud Security Management (ICCSM) (p. 138).
• [15] Koc, C. K. (2009). About cryptographic engineering. In Cryptographic engineering (pp. 1-4). Springer, Boston, MA.
• [16] Acıiçmez, O., Schindler, W., & Koç, Ç. K. (2007, February). Cache based remote timing attack on the AES. In Cryptographers’ track at the RSA conference (pp. 271-286). Springer, Berlin, Heidelberg.
• [17] Acıiçmez, O., Brumley, B. B., & Grabher, P. (2010, August). New results on instruction cache attacks. In International Workshop on Cryptographic Hardware and Embedded Systems (pp. 110-124). Springer, Berlin, Heidelberg.
• [18] Aciçmez, O., & Koc, C. K. (2006). Trace-driven cache attacks on AES.
• [19] Neve, M., & Seifert, J. P. (2006, August). Advances on access-driven cache attacks on AES. In International Workshop on Selected Areas in Cryptography (pp. 147-162). Springer, Berlin, Heidelberg.
• [20] Tsunoo, Y., Saito, T., Suzaki, T., Shigeri, M., & Miyauchi, H. (2003, September). Cryptanalysis of DES implemented on computers with cache. In International Workshop on Cryptographic Hardware and Embedded Systems (pp. 62-76). Springer, Berlin, Heidelberg.
• [21] Tsunoo, Y., Tsujihara, E., Shigeri, M., Kubo, H., & Minematsu, K. (2006). Improving cache attacks by considering cipher structure. International Journal of Information Security, 5(3), 166-176.
• [22] Bernstein, D. J. (2005). Cache-timing attacks on AES.
• [23] Atici, A. C., Yilmaz, C., & Savas, E. (2016). Remote Cache-Timing Attack without Learning Phase. IACR Cryptology ePrint Archive, 2016, 2.
• [24] Neve, M. (2006). Cache-based Vulnerabilities and SPAM analysis. Doctor thesis, UCL.
• [25] Spreitzer, R., & Gérard, B. (2014, June). Towards more practical time-driven cache attacks. In IFIP International Workshop on Information Security Theory and Practice (pp. 24-39). Springer, Berlin, Heidelberg.
• [26] Palczewska, A., Palczewski, J., Robinson, R. M., & Neagu, D. (2014). Interpreting random forest classification models using a feature contribution method. In Integration of reusable systems (pp. 193-218). Springer, Cham.
• [27] Molnar, C. (2018). Interpretable machine learning: A guide for making black box models explainable. E-book at< https://christophm. github. io/interpretable-ml-book/>, version dated, 10.
• [28] Pal, M. (2005). Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26(1), 217-222.
• [29] Buduma, N., & Locascio, N. (2017). Fundamentals of deep learning: Designing next-generation machine intelligence algorithms. " O'Reilly Media, Inc.".