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姓名 林承信(Cheng-Hsin Lin)  查詢紙本館藏   畢業系所 統計研究所
論文名稱 漸進最佳變點偵測在金融科技網路安全之分析
(Asymptotic Optimal Changepoint Detection with an Analysis on FinTech Cybersecurity)
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摘要(中) 近年來,全球在金融科技的投資快速增長。一些金融公司運用技術來改善他們的服務。例如,J.P. Morgan Chase & Co.(JPM)讓客戶透過他們的網站進行網路貸款。但也因為金融科技的發展,金融公司必須面對更大量的網路攻擊。其中較著名的攻擊是分散式拒絕服務(DDoS) 攻擊。它被用於攻擊金融公司的伺服器,使網路服務無法正常使用。
為了偵測DDoS 攻擊,我們提出了一種方法,藉由監控大量檢測器來偵測出其中一小部分檢測器中的數據流產生的分配變化。它可以看作是同時監控大量路由器,每個路由器都會接收到網路流量。當駭客發起攻擊時,其中一小部分路由器接收到異常流量,因此分配產生變化。而我們的目的就是及早發現這些變化,藉此減低駭客攻擊所造成的損失。在本文中,我們證明當檢測器的數目趨近於無窮大時,漸進最佳的檢測延遲取決於一小部分檢測器中數據流的分配變化。本文考慮了三種情況。一是我們所監控的檢測器中大部分的檢測器其數據流分配都產生變化,那我們會立即發現變化。二是在我們所監控的檢測器中,一小部分的檢測器其數據流分配產生變化,則檢測延遲隨著檢測器的數量呈對數增長。三則是在我們所監控的檢測器中,只有非常少的檢測器其數據流分配產生變化。
摘要(英) In the recent years, global investment in FinTech increase rapidly. Some financial companies use technology to improve their service. For example, the retail banking website
of J.P. Morgan Chase & Co. (JPM) allows customers to make online loans through their website. Because of the development, financial companies have to face more cyberattacks than before. One of the famous attack is Distributed Denial of Service (DDoS) attack. It is used to shot down the servers of financial companies, so that network services can not be used.
In order to detect DDoS attack, we propose a method to detect the distribution changes in a fraction of data streams under a large number of detectors. It can be seem as monitoring a large number of routers at the same time, and each router receives network traffic. When hacker launch an attack, a fraction of routers will receive abnormal traffic. Our task is to detect the change as quickly as possible, to reduce the loss. In this paper,
we show how the nearly optimal detection delay depends on the fraction of data streams undergoing distribution changes as the number of detectors goes to infinity. There are three detection domains. In the first domain for moderately large fractions, immediate detection is possible. In the second domain for smaller fractions, the detection delay grows logarithmically with the number of detectors. In the third domain for even smaller fractions.
關鍵字(中) ★ 變點分析 關鍵字(英) ★ changepoint detection
論文目次 Contents
摘要 i
Abstract ii
誌謝 iii
Contents iv
List of Tables v
1 Introduction 1
2 Review 3
2.1 Financial Technology 3
2.2 Distributed Denial of Service (DDoS) attacks 3
2.3 How to detect Distributed Denial of Service (DDoS) attacks 4
2.4 Changepoint detection 5
3 Detection delay lower bound 7
3.1 Theorem 1 7
3.2 Proof of Theorem 1 8
3.3 How to choose k? 16
3.4 Special case of Theorem 1 17
3.4.1 Corollary 1 18
4 Asymptotic optimal detection using detectability score 20
4.1 Theorem 2 20
4.2 Proof of Theorem 2 20
4.3 Asymptotic optimal detection of Corollary 1 26
4.3.1 Corollary 2 27
5 Simulation results 28
6 Conclusions 30
Reference 31
參考文獻 References
[1] Ricardo O De Schmidt, Ramin Sadre, and Aiko Pras. Gaussian traffic revisited. In IFIP Networking Conference, 2013, pages 1–9. IEEE, 2013.
[2] Ozge Cepheli, Saliha Buyukcorak, and Gune? Karabulut Kurt. Hybrid intrusion detection system for ddos attacks. Journal of Electrical and Computer Engineering, 2016, 2016.
[3] Wei Lu and Issa Traore. An unsupervised approach for detecting ddos attacks based on traffic-based metrics. In Communications, Computers and signal Processing, 2005.
PACRIM. 2005 IEEE Pacific Rim Conference on, pages 462–465. IEEE, 2005.
[4] Yu Chen, Kai Hwang, and Wei-Shinn Ku. Distributed change-point detection of ddos attacks over multiple network domains. In Int. Symp. on Collaborative Technologies and Systems, pages 543–550, 2006.
[5] Aleksey S Polunchenko, Alexander G Tartakovsky, and Nitis Mukhopadhyay. Nearly optimal change-point detection with an application to cybersecurity. Sequential Analysis,
31(3):409–435, 2012.
[6] Alexander Tartakovsky, Igor Nikiforov, and Michele Basseville. Sequential analysis:
Hypothesis testing and changepoint detection. Chapman and Hall/CRC, 2014.
[7] Ewan S Page. Continuous inspection schemes. Biometrika, 41(1/2):100–115, 1954.
[8] Albert N Shiryaev. On optimum methods in quickest detection problems. Theory of
Probability & Its Applications, 8(1):22–46, 1963.
[9] Gary Lorden. Procedures for reacting to a change in distribution. The Annals of
Mathematical Statistics, pages 1897–1908, 1971.
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[10] Moshe Pollak. Optimal detection of a change in distribution. The Annals of Statistics,
pages 206–227, 1985.
[11] Yao Xie and David Siegmund. Sequential multi-sensor change-point detection. In
Information Theory and Applications Workshop (ITA), 2013, pages 1–20. IEEE, 2013.
[12] Hock Peng Chan et al. Optimal sequential detection in multi-stream data. The
Annals of Statistics, 45(6):2736–2763, 2017.
[13] Tze Leung Lai. Sequential changepoint detection in quality control and dynamical
systems. Journal of the Royal Statistical Society. Series B (Methodological), pages
613–658, 1995.
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指導教授 傅承德(Cheng-Der Fuh) 審核日期 2018-7-19
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