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題名：運用最佳化技術決定多階段網路攻防情境中之防禦資源分配策略以優化系統之存活度
作者：陳霈語
作者(外文)：Pei-Yu Chen
校院名稱：國立臺灣大學
系所名稱：資訊管理學研究所
指導教授：林永松
學位類別：博士
出版日期：2016
主題關鍵詞：平均網路分隔度；競爭函數；網路存活度；最佳化；資源分配；多階段網路攻防；Average Degree of Disconnectivity；Average DOD；Contest Success Function；Network Survivability；Optimization；Resource Allocation；Resource Reallocation；Network Recovery；Multi-Stage Network Attack and Defense
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隨著科技與網路的發展，駭客的惡意攻擊 (Malicious Attacks)手法持續演進著，近年來廣為討論的雲端運算，亦被攻擊者所應用。對國家或企業組織而言，惡意攻擊除了造成名譽受損之外，隱私資料外洩更會重創國家或企業組織，因此，資訊安全 (Information Security)的議題備受重視。網路存活性 (Survivability)便是其中一個重要的研究領域，本研究規劃以存活度指標衡量方式，提出並優化該指標的準確性。
除了最基本的網路連結度外，本研究亦延伸此指標之概念，提出一更優化之存活度指標為平均網路分隔度 (Average Degree of Disconnectivity, Average DOD)。傳統的網路分隔度指標假設攻擊者進行攻擊時，其結果非成功即失敗，屬二元的離散概念，較不符合現實的情況。故本研究引入機率的概念，透過競爭成功函數 (Contest Success Function)計算攻擊者成功攻克各個節點的機率，考量所有可能發生之情境機率，所形成之新型網路存活度指標即為Average DOD。
對於防禦者而言，為了保護其所管控之網路，投資一筆有限的預算 (例如：金錢、時間、人力)，並妥善地加以配置。另一方面，攻擊者所擁有的資源亦是有限的，故無法攻擊所需成本超過自身能負擔的網路。為了達到攻擊目的，攻擊者也會因應網路營運者所採用的資源配置策略 (Resource Allocation Strategies)，調整其攻擊策略 (Attack Strategies)，充分應用有限的資源達成目標。由於考量的問題，需運用數學規劃技巧轉為數學模型，加上問題具高度複雜性與困難度，為求最佳化決策之時效與品質，特採最佳化技巧為基礎方略；考量在攻防雙方各自具有不同的目標或利益，在多階段的攻防下，雙方必須考慮對手的各種可能的行動方案，並力圖選取對自己最為有利或最為合理的方案，因此本研究預計運用數學分析求解，已求解問題中是否存在著最合理的行為解決方案，以及如何找到這個合理的行為方案的高效率之研究方法。透過本研究結合數學規劃法中最佳化的技術，預測攻防情境模擬，可有效提供國家或企業組織之防禦者角色在防禦資源配置上的準則與參考依據。
Internet has become worldwide, publicly accessible network of interconnected computer networks. Specifically, it becomes the tools that terrorists can use to attack the nations and their economy or enterprises. Thus, it becomes an important and critical issue about how to efficiently evaluate network survivability for any network operator in a nation or an enterprise. We intent to proposed an effective and accurate network survivability metric to provide some engineering guidelines to improve network security.
In this research, an innovative metric called Average Degree of Disconnectivity (Average DOD) is proposed. It combining the concept of the probability calculated by contest success function with the DOD metric would be used to evaluate the damage degree of network. The larger value of the Average DOD, the more damage degree of the network would be.
A multi-stage network attack-defense scenario as a mathematical model is used to support network operators to predict that all the likelihood strategies both cyber attacker and network defender would take in this research. In addition, the Average DOD would be used to evaluate damage degree of network. In each stage, the attacker could use the attack resources to launch attack on the nodes of the target network. On the other hand, the network defender could reallocate existed resources of defender to recover compromised nodes and allocate defense resources to protect survival nodes of network. In the process of problem solving, the optimization techniques is adopted to find the optimal resource allocation strategies for both cyber attacker and mobile network defender.
References
[1]R.J. Ellison, D.A. Fisher, R.C. Linger, H.F. Lipson, T. Longstaff, and N.R. Mead, “Survivable Network Systems: An Emerging Discipline,” Technical Report CMU/SEI-97-TR-013, November 1997.
[2]A.T Murray. T.C. Matisziw, T. H. Grubesic, “Critical network infrastructure analysis: interdiction and system flow,” Journal of Geographical Systems. Vol. 9, No. 2, pp. 103-117, June, 2007.
[3]J.C. Smith, C. Lim, F. Sudargho, “Survivable network design under optimal and heuristic interdiction scenarios,” Journal of Global Optimization. Vol. 38, No. 2, pp. 181-199, June, 2007.
[4]E.Jonsson and T. Olovsson, “A Quantitative Model of the Security Intrusion Process Based on Attacker Behavior,” IEEE Transactions of Software Engineering, Vol. 23, No. 4, pp. 235-245, April 1997.
[5]R. Ortalo, Y. Deswarte, and M. Kaaniche, “Experimenting with Quantitative Evaluation Tools for Monitoring Operational Security,” IEEE Transactions on Software Engineering, Vol. 25, Vol. 5, pp. 633-650, September 1999.
[6]J. McDermott, “Attack-Potential-Based Survivability Modeling for High-Consequence Systems,” Proceedings of the 3rd IEEE International Workshop on Information Assurance, pp. 119-130, March 2005.
[7]D.A. Burke, “Towards a Game Theory Model of Information Warfare,” Master''s thesis, Graduate School of Engineering and Management, Airforce Institute of Technology, Air University, November 1999.
[8]J.C. Harsanyi, “Games with Incomplete Information Played by “Bayesian” Players,” I–III: Part I. The Basic Model&. Management Science, Vol. 50, pp. 1804-1817, 2004.
[9]K. Hausken, and G. Levitin, “Protection vs. False Targets in Series Systems,” Reliability Engineering & System Safety, Vol. 94, NO.5, pp. 973-981, 2009.
[10]G. Levitin, and K. Hausken, “False Targets Efficiency in Defense Strategy,” European Journal of Operational Research,Vol. 194, No. 1, pp 155-162, 2009.
[11]R. Peng, , G. Levitin, M. Xie, S.H. Ng, “Intelligence and Impact Contests in Defending a Single Object with Imperfect False Targets. in Industrial Engineering and Engineering Management,” IEEM. IEEE International Conference, 2009.
[12]P. Rui, , W. Wenbin, and Z. Fei, “Object Defense Strategy with Imperfect False Targets and disinformation. in Quality, Reliability,” Risk, Maintenance, and Safety Engineering (ICQR2MSE), 2012.
[13]T. Bas¸ar and G. J. Olsder, “Dynamic Noncooperative Game Theory,” 2nd edition, Classics in Applied Mathematics, SIAM, Philadelphia, 1999.
[14]M. Benaım, “Dynamics of Stochastic Approximations. Le Seminaire de Probabilites,” Lectures Notes in Mathematics, Vol. 1709, pp. 1-68, 1999.
[15]T. Borgers and R. Sarin, “Learning Through Reinforcement and Replicator Dynamics,” Appeared in Journal of Economic Theory, Vol. 77, No. 1, pp. 1-14, November 1997.
[16]H. P. Young, Strategic Learning and Its Limits, Oxford University Press, 2004.
[17]V.M. Bier, S. Oliveros and L. Samuelson, “Choosing What to Protect: Strategic Defensive Allocation against an Unknown Attacker,” Journal of Public Economic Theory, Vol. 9, No. 4, pp. 563-587, August 2007.
[18]J. Zhuang and V.M. Bier, “Balancing Terrorism and Natural Disasters - Defensive Strategy with Endogenous Attacker Effort,” Operations Research, Vol. 55, No. 5, pp. 976-991, September 2007.
[19]T. Sandler and D.G. Arce, “Terrorism and Game Theory,” Simulation & Gaming, Vol. 34, No. 3, pp.319-337, September 2003.
[20]K. Hausken and V.M. Bier, “Defending against Multiple Different Attackers,” European Journal of Operational Research, Vol. 211, No. 2, pp. 370-384, June 2011.
[21]J. Zhuang, V.M. Bier and O. Alagoz, “Modeling Secrecy and Deception in a Multiple-period Attacker–defender Signaling Game,” European Journal of Operational Research, Vol. 203, No. 2, pp. 409-418, June 2010.
[22]J. Zhuang and V.M. Bier, “Secrecy and Deception at Equilibrium, with Applications to Anti-terrorism Resource Allocation,” Defence and Peace Economics, Vol. 22, No. 1, pp. 43-61, February 2011.
[23]N.S. Dighe, J. Zhuang and V.M. Bier, “Secrecy in Defensive Allocations as a Strategy for Achieving More Cost-effective,” International Journal of Performability Engineering, Vol. 5, No. 1, pp. 31-43, January 2009.
[24]R. Powell, “Allocating Defensive Resources with Private Information about Vulnerability,” American Political Science Review, Vol. 101, No. 4, pp. 799-809, November 2007.
[25]N.C. Rowe, “Deception in Defense of Computer Systems from Cyber Attack,” Cyber Warfare and Cyber Terrorism, pp. 97-104, 2008.
[26]N.C. Rowe and H.S. Rothstein, “Two Taxonomies of Deception for Attacks on Information Systems,” Journal of Information Warfare, Vol. 3, No. 2, pp. 27-39, July 2004.
[27]M Borrett, “Cyber Strategies Revealed,” IBM Institute for Advanced Security, 11 December, 2011.
[28]K. Hausken, “Strategic Defense and Attack for Reliability Systems,” Reliability Engineering & System Safety, Vol. 93, No. 11, pp. 1740-1750, November 2008.
[29]M.N. Azaiez and V.M. Bier, “Optimal Resource Allocation for Security in Reliability Systems,” European Journal of Operational Research, Vol. 181, No. 2, pp. 773-786, September 2007.
[30]F.Y.S. Lin, P.H. Tsang, P.Y. Chen and H.T. Chen, “Maximization of Network Robustness Considering the Effect of Escalation and Accumulated Experience of Intelligent Attackers,” Proc. World Multiconference on Systemics, Cybernetics and Informatics, 2009.
[31]G. Levitin and K. Hausken, “Resource Distribution in Multiple Attacks against a Single Target,” Risk Analysis, Vol. 30, No. 8, pp. 1231-1239, August 2010.
[32]C. Pinzón, J. F. D Paz, C. Zato, and J. Pérez, “Protecting Web Services against DoS Attacks: A Case-Based Reasoning Approach,” Lecture Notes in Computer Science, Vol. 6076, pp. 229-236, 2010.
[33]A.T. Murray, T.C. Matisziw, and T. H. Grubesic, “Critical Network Infrastructure Analysis: Interdiction and System Flow,” Journal of Geographical Systems, Vol. 9, No. 2, pp. 103-117, June, 2007.
[34]J.C. Smith, C. Lim, and F. Sudargho, “Survivable Network Design under Optimal and Heuristic Interdiction Scenarios,” Journal of Global Optimization, Vol. 38, No. 2, pp. 181-199, June 2007.
[35]M.S. Deutsch and R.R. Willis, “Software Quality Engineering: A Total Technical and Management,” Approach, Englewood Cliffs, NJ: Prentice-Hall, 1988.
[36]“Terms and Definitions Related to Quality of Service, Availability, and Reliability,” CCITT Fascicle III. 1 Rec. G. 106, 1984.
[37]V.R. Westmark, “A Definition for Information System Survivability,” Proceedings of the 37th IEEE Hawaii International Conference on System Sciences, January 2004.
[38]M.S. Deutsch and R.R. Willis, “Software Quality Engineering: A Total Technical and Management Approach,” Englewood Cliffs, NJ: Prentice-Hall, 1988.
[39]L. Wu and P.K. Varshney, “On Survivability Measures for Military Networks,” IEEE Military Communications Conference, Vol. 3, pp. 1120-1124, October 1990.
[40]T.-Z. Jiang, “A New Definition on Survivability of Communication a Networks,” Conference Record of IEEE Military Communications Conference 1991 (MILCOM''91), Vol. 3, pp. 901-904, November 1991.
[41]S.C. Liew and K.W. Lu, “A Framework for Network Survivability Characterization,” IEEE International Conference on Communications 1992, Vol. 1, pp. 405-410, June 1992.
[42]J. Shi and J.P. Fonseka, “Traffic-based Survivability Analysis of Telecommunications Networks,” IEEE Global Telecommunications Conference, Vol. 2, pp. 936-940, November 1995.
[43]K.J. Sullivan, S. Geist, and P. Shaw, “Mediators in Infrastructure Survivability Enhancement,” ACM Proc. of the 3rdIinternational Workshop on Software Architecture, pp. 141-144, November 1998.
[44]A.P. Snow, U. Varshney, and A.D. Malloy, “Reliability and Survivability of Wireless and Mobile Networks,” Computer, Vol. 33, No. 7, pp. 49-55, July 2000.
[45]D. Awduche, A. Chiu, A. Elwalid, I. Widjaja, and X. Xiao, “Overview and Principles of Internet Traffic Engineering,” RFC3272, May 2002.
[46]ATIS Telecom Glossary 2007, Alliance for Telecommunications Industry Solutions, http://www.atis.org/glossary/definition.aspx?id=1039.
[47]S. Balasubramaniam, D. Botvich, W. Donnelly, and N. Agoulmine, “A Multi-layered Approach Towards Achieving Survivability in Autonomic Network,” IEEE International Conference on Telecommunications and Malaysia International Conference on Communications, pp. 360-365, May 2007.
[48]M. Garg and J. C. Smith, “Models and Algorithms for the Design of Survivable Multicommodity Flow Networks with General Failure Scenarios,” Omega, Vol. 36, No. 6, pp. 1057-1071, December 2008.
[49]Z. Ma and A.W. Krings, “Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management (PHM),” IEEE Aerospace Conference 2008, pp. 1-20, March 2008.
[50]B. Bassiri and S.S. Heydari,“Network Survivability in Large-Scale Regional Failure Scenarios,” Proceedings of the Second Canadian Conference on Computer Science and Software Engineering, Montreal, Quebec, Canada, pp. 83-87, 2009.
[51]P.E. Heegaard and K.S. Trivedi, “Network Survivability Modeling,” Computer Networks, Vol. 53, No. 8, pp. 1215-1234, 2009.
[52]F. Xing and W. Wang, “On the Survivability of Wireless Ad Hoc Networks with Node Misbehaviors and Failures,” IEEE Transactions on Dependable and Secure Computing, Vol. 7, No. 3, pp. 284-299, 2010.
[53]L. Xie, P.E. Heegaard, and Y. Jiang, “Network Survivability under Disaster Propagation: Modeling and Analysis,” IEEE Wireless Communications and Networking Conference, 2013.
[54]L. Ming, G. Zhao, D.X. Wang, M.H. Huang,X. Li, Q. Miao, F. Xu, “A Novel Method for Survivability Test Based on End Nodes in Large Scale Network,” KSII Transactions on Internet and Information Systems, Vol. 9 ,No. 2, pp. 620-636, 2015.
[55]S. Skaperdas, Contest Success Functions, Economic Theory, 1996.
[56]G. Levitin and K. Hausken, “Parallel Systems under two Sequential Attacks”, Reliability Engineering and System Safety, Vol. 94, No. 3, pp. 763-772, March 2009.
[57]G. Levitin and K. Hausken, “Resource Distribution in Multiple Attacks against a Single Target,” Risk Analysis, Vol. 30, 2010.
[58]J. E. Ramirez-Marquez, C.M. Rocco, G. Levitin, “Optimal Network Protection against Diverse Interdictor Strategies,” Reliability Engineering and System Safety, Vol. 96, pp.374-382, 2011.
[59]L. Wang, S. Ren, K. Yue, K. Kwiat, “Optimal Resource Allocation for Protecting System Availability against Random Cyber Attacks,” Proc. 3rd Int. Conf. ICCRD, Vol. 1, pp.477-482, 2011.
[60]F.Y.S. Lin, H.H. Yen, P.Y. Chen and Y.F. Wen, “Evaluation of Network Survivability Considering Degree of Disconnectivity,” Proc. HAIS’11, 2011.
[61]O.M. Al-Kofahi and A.E. Kamal, “Survivability Strategies in Multihop Wireless Networks,” IEEE Wireless Communications, Vol. 17, No. 5, pp. 71-80, October 2010.
[62]S. Neumayer and E. Modiano, “Network Reliability with Geographically Correlated Failures,” Proc. IEEE INFOCOM, 2010.
[63]T. Alpcan and T. Baser, “A Game Theoretic Analysis of Intrusion Detection in Access Control Systems,” Proc. 43rd IEEE Conference on Decision and Control, 2004.
[64]X. Liang and Y. Xiao, “Game Theory for Network Security,” IEEE Communications Surveys & Tutorials, 2013.
[65]T. Alpcan and T. Baser, “An Intrusion Detection Game with Limited Observations,” Proc. 12th Int. Symp. on Dynamic Games and Applications, 2012.
[66]R. Machado and S. Tekinay, “A Survey of Game-theoretic Approaches in Wireless Sensor Networks Computer Networks,” Computer Networks, Vol. 52, No. 16, pp. 3047-3061, 2008.
[67]H. Hassoun, Fundamentals of Artificial Neural Networks, MIT Press, 1995.
[68]G.Levitin and K. Hausken, “Resource Distribution in Multiple Attacks against a Single Target,” Risk Analysis, Vol. 30, No. 8, pp. 1231-1230, 2010.
[69]T. Schelling, “The Reciprocal Fear of Surprise Attack,” RAND Corporation, Santa Monica, 1958.

 
 
 
 
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