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研究生(外文):Kai-Zhong Zhou
論文名稱(外文):Detecting Hybrid DDoS Attacks with Flow Differentiation in Software-Defined Networking
指導教授(外文):Yuan-Cheng Lai
口試委員(外文):Nai-Wei LoYen-Hung Chen
外文關鍵詞:SDNhybrid DDoS attacksflow-based detecting
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DDoS (Distributed Denial-of-Service)攻擊已是當今網路的主要攻擊之一,目標除了提供服務的伺服器,亦可能為基礎設施的鏈路,針對目前單一型態(伺服器或鏈路)之DDoS攻擊,過去許多研究已提供了偵測這些單一型態的DDoS攻擊的方法,然而隨著攻擊手法推陳出新,混合型的DDoS攻擊能同時攻擊多種目標(伺服器或鏈路),由於攻擊多種目標時,攻擊伺服器及鏈路會相較單一型態之DDoS攻擊情況來得輕微,導致現行單一型態DDoS攻擊偵測方法不易查覺。
然而不論攻擊型態為何,DDoS攻擊最主要目的為讓伺服器無法提供正常服務,故本研究提出了一個能偵測混合型的DDoS攻擊的方法FDD (Flow Differentiation Detector),此方法以服務伺服器所在位置(目標區域)連接對外網路的重要鏈路(目標鏈路)為觀察點,觀察資料流流入及流出個數的差異量。在發生混合型的DDoS攻擊的情況下,流經目標鏈路且請求目標區域的服務伺服器之資料流仍然會增加,但從目標區域回應的資料流數目變化很小。因此利用請求與回應兩者之間差異量來表示混合攻擊行為對於服務造成的危害程度,藉此偵測混合型的DDoS攻擊。
本研究的貢獻為(1)證明混合型DDoS攻擊確實能對目標區域造成危害;(2)提出能偵測混合型的DDoS攻擊的方法FDD;(3)於實際SDN控制器上實作FDD,並量測其偵測效果。實驗結果顯示FDD一個有7個交換器的SDN網路下針對混合型DDoS攻擊能保持平均偵測率達96.7%,優於直接混合現行偵測伺服器之DDoS攻擊及偵測鏈路之DDoS攻擊之綜合方法Combiner (COM)的平均偵測率90%,並發現不論目標區域或是網路拓樸大小改變,FDD均能保持良好的偵測效果。
Distributed denial-of-service (DDoS) attacks have become one of the main attacks in the network nowadays. The target except providing servers for services, it may also be a link in infrastructure. For the current single type (server or link) DDoS attacks, many pieces of research have been conducted in the past, providing ways to detect these single type DDoS attacks. However, as the attacks techniques evolving, hybrid DDoS attacks can attack multiple targets (servers or links) simultaneously, attacking servers and chains as they attack multiple targets. The situation will be slightly smaller than the single type of DDoS attack, which makes hybrid DDoS attack difficult to detect.
However, regardless of the attack type, the main purpose of the DDoS attack is to deny the server from providing normal services. Therefore, this thesis proposes a novel approach (FDD) to strengthen the current detection to detect hybrid DDoS attacks. This approach monitors target area where the service servers are located on target link which connecte to the external network to calculate the differentiation input and output through target area. Under hybrid DDoS attack, the number of flows which destine to the servers in target area increases while the number of flows which depart from the servers in target area is almost fixed. Therefore, by the phenomenon that the differentiation between the request and the response is used to indicate the degree of harm caused by hybrid DDoS attacks behavior to the service, thereby detecting it.
The contribution of this thesis: (1) prove that hybrid DDoS attack does cause damage to service; (2) propose the approach, Flow Differentiation Detector (FDD), to detect hybrid DDoS attacks; (3) deployed FDD in SDN controller, OpenDayLight, to implement the hybrid DDoS attack detection system. Finally, the experimental results that FDD the average detection accuracy under different ratio of hybrid DDoS attacks in the topology which there are 7 switches is 96.7% better than Combiner (COM) whose the average detection accuracy is 90%; no matter what the number of servers in the target area or the topology change, the FDD has good effects.
List of tables
List of figures
Chapter 1. Introduction
Chapter 2. Related works
Chapter 3. Flow Differentiation Detector
Chapter 4. Evaluation
Chapter 5. Conclusion and Future Work
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