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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林永松(Yeong-Sung Lin) | |
| dc.contributor.author | Wei-Hsin Wang | en |
| dc.contributor.author | 王偉鑫 | zh_TW |
| dc.date.accessioned | 2021-06-17T01:34:11Z | - |
| dc.date.available | 2020-08-20 | |
| dc.date.copyright | 2020-08-20 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-17 | |
| dc.identifier.citation | P. Mell, T. Grance et al., “The nist definition of cloud computing,” 2011. Y. Chen, V. Paxson, and R. H. Katz, “What’s new about cloud computing security,” University of California, Berkeley Report No. UCB/EECS-2010-5 January, vol. 20, no. 2010, pp. 2010–5, 2010. T. Peng, C. Leckie, and K. Ramamohanarao, “Survey of network-based defense mechanisms countering the dos and ddos problems,” ACM Comput. Surv., vol. 39, no. 1, p. 3–es, Apr. 2007. [Online]. Available: https://doi.org/10.1145/1216370.1216373 J. Ordonez-Lucena, P. Ameigeiras, D. Lopez, J. J. Ramos-Munoz, J. Lorca, and J. Folgueira, “Network slicing for 5g with sdn/nfv: Concepts, architectures, and challenges,” IEEE Communications Magazine, vol. 55, no. 5, pp.80–87, 2017. H. I. Kobo, A. M. Abu-Mahfouz, and G. P. Hancke, “A survey on software defined wireless sensor networks: Challenges and design requirements,” IEEE access, vol. 5, pp. 1872–1899, 2017. M. Series, “Imt vision–framework and overall objectives of the future development of imt for 2020 and beyond,” 2015. R. C. Streijl, S. Winkler, and D. S. Hands, “Mean opinion score (mos) revisited: methods and applications, limitations and alternatives,” Multimedia Systems, vol. 22, no. 2, pp. 213–227, Mar 2016. [Online]. Available: https://doi.org/10.1007/s00530-014-0446-1 P.-Y. Chen and F. Y.-S. Lin, “Recovery and resource allocation strategies to maximize mobile network survivability by using game theories and optimization techniques,” Journal of Applied Mathematics, vol. 2013, Special Issue, p. 9 pages, 2013. [Online]. Available: https://doi.org/10.1155/2013/207141 I. Benkacem, T. Taleb, M. Bagaa, and H. Flinck, “Optimal vnfs placement in cdn slicing over multi-cloud environment,” IEEE Journal on Selected Areas in Communications, vol. 36, no. 3, pp. 616–627, March 2018. S. Chaisiri, B.-S. Lee, and D. Niyato, “Optimization of resource provisioning cost in cloud computing,” IEEE transactions on services Computing, vol. 5, no. 2, pp. 164–177, 2011. T. Lin, Z. Zhou, M. Tornatore, and B. Mukherjee, “Demand-aware network function placement,” Journal of Lightwave Technology, vol. 34, no. 11, pp. 2590–2600, June 2016. S. Subashini and V. Kavitha, “A survey on security issues in service delivery models of cloud computing,” Journal of network and computer applications, vol. 34, no. 1, pp. 1–11, 2011. M. J. Osborne et al., An introduction to game theory. Oxford university press New York, 2004, vol. 3, no. 3. S. Skaperdas, “Contest success functions,” Economic theory, vol. 7, no. 2, pp. 283–290, 1996. D. P. Bertsekas, Constrained optimization and Lagrange multiplier methods. Academic press, 2014. M. L. Fisher, “The lagrangian relaxation method for solving integer pro gramming problems,” Management Science, vol. 27, no. 1, pp. 1–18, 1981. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/67483 | - |
| dc.description.abstract | 近年來,雲端運算已經對企業的基礎設施架構產生了巨大的影響,雲端廠商和企業的合作變得至關重要,隨著越來越多應用在雲端上的開發,網路安全的問題也跟著浮現,雲端廠商和企業都需要承擔責任來防禦惡意攻擊。因此,我們提供了一個數學模型,代表兩方對於利益的博弈關係。在網路攻防的情境下,一個資源配置的策略來抵禦惡意攻擊,兩方競爭者的利益相互衝突,防禦者想要最大化虛擬機的預期存活度,攻擊者想要最小化虛擬機的預期存活度,競爭成功函數是一個我們用來衡量競爭者成功的機率函數。 拉格朗日乘數法和拉格朗日放鬆法是兩個我們用來解決這個數學模型的方法,在實驗中,我們比較了不同方法的優缺點,像是暴力法,近似最佳法,和啟發式演算法,不同方法之間的功效,得到的結果是合理且符合我們的預期。 | zh_TW |
| dc.description.abstract | Cloud computing has made a huge impact on enterprise infrastructures. The cooperation of cloud providers and the firm is crucial. As more applications are being developed, network security issues emerge. Both of them should take the responsibilities and defense the malicious attacks. Thus, we develop a general mathematical model as a two-player max-min game. A resource allocation strategy is presented in a network defense and attack scenario. The interests of the two players contradict. One wants to maximize the expected value of VMs while the other wants to minimize it. Contest Success Function is a function we use to measure the compromise probability. Lagrange multiplier and Lagrangian relaxation are the methods we applied to solve the problem. In our experiment, we compare advantages and disadvantages for different approaches including brute force, near-optimal and heuristic methods. The effectiveness of different approaches is also presented. The result we gain is reasonable and corresponds to our expectation. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T01:34:11Z (GMT). No. of bitstreams: 1 U0001-1508202017370800.pdf: 8539519 bytes, checksum: c01337a03b2202eeec9ace4d3b5181ad (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | Abstract i List of Figures vii List of Tables xi 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Literature Review 7 2.1 Security Issues in Cloud . . . . . . . . . . . . . . . . . . . . . . 8 2.1.1 Distributed Denial of Service (DDoS) Attack . . . . . . . 9 2.2 Game Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Contest Success Function . . . . . . . . . . . . . . . . . . . . . . 12 3 Problem Formulation 17 3.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . 17 3.1.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2 Mathematical Formulation . . . . . . . . . . . . . . . . . . . . . 18 4 Solution Approaches 23 4.1 Lagrange Multiplier Method . . . . . . . . . . . . . . . . . . . . 23 4.2 Lagrangian Relaxation Method . . . . . . . . . . . . . . . . . . . 25 4.3 Small Number of VMs . . . . . . . . . . . . . . . . . . . . . . . 28 4.3.1 Inner Minimization Problem . . . . . . . . . . . . . . . . 30 4.3.2 Outer Maximization Problem . . . . . . . . . . . . . . . 31 4.3.3 Overall Algorithm . . . . . . . . . . . . . . . . . . . . . 32 4.4 Large Number of VMs . . . . . . . . . . . . . . . . . . . . . . . 36 4.4.1 Inner Minimization Problem . . . . . . . . . . . . . . . . 36 4.4.2 Outer Maximization Problem . . . . . . . . . . . . . . . 37 4.4.3 Overall Algorithm . . . . . . . . . . . . . . . . . . . . . 39 4.5 Simplified Form When m =1 . . . . . . . . . . . . . . . . . . . 43 5 Computational Experiments 55 5.1 Experiments for Small Number of VMs . . . . . . . . . . . . . . 55 5.2 Comparison between Solution Approaches . . . . . . . . . . . . . 70 5.3 Experiments for Large Number of VMs . . . . . . . . . . . . . . 79 6 Conclusion and Future Work 83 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 References 87 | |
| dc.language.iso | en | |
| dc.subject | 資源配置策略 | zh_TW |
| dc.subject | 博弈理論 | zh_TW |
| dc.subject | 拉格朗日放鬆法 | zh_TW |
| dc.subject | 拉格朗日乘數法 | zh_TW |
| dc.subject | 競爭成功函數 | zh_TW |
| dc.subject | 網路安全 | zh_TW |
| dc.subject | 雲端運算 | zh_TW |
| dc.subject | Contest Success Function | en |
| dc.subject | Lagrange Multiplier | en |
| dc.subject | Cloud Computing | en |
| dc.subject | Network Security | en |
| dc.subject | Resource Allocation | en |
| dc.subject | Game Theory | en |
| dc.subject | Lagrangian Relaxation | en |
| dc.title | 於雲端運算環境中對惡意攻擊之損害最小化資源分配策略 | zh_TW |
| dc.title | Resource Allocation Strategies to Minimize Network Damages under Malicious Attacks in a Cloud Environment | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 孔令傑(Ling-Chieh Kung),鍾順平(Shun-Ping Chung),莊東穎(Tong-Ying Juang),呂俊賢(Chun-Hsien Lu) | |
| dc.subject.keyword | 雲端運算,網路安全,競爭成功函數,拉格朗日乘數法,拉格朗日放鬆法,博弈理論,資源配置策略, | zh_TW |
| dc.subject.keyword | Cloud Computing,Network Security,Contest Success Function,Lagrange Multiplier,Lagrangian Relaxation,Game Theory,Resource Allocation, | en |
| dc.relation.page | 89 | |
| dc.identifier.doi | 10.6342/NTU202003531 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-17 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位: | 資訊管理學系 | |
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