博碩士論文 107522043 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:19 、訪客IP:3.142.245.243
姓名 涂軒豪(Hsuan-Hao Tu)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 基於P4交換機與入侵檢測系統 之泛洪攻擊偵防機制
(Flooding Attack Detection and Defense Mechanism Based on P4 Switches and Intrusion Detection System)
相關論文
★ 無線行動隨意網路上穩定品質服務路由機制之研究★ 應用多重移動式代理人之網路管理系統
★ 應用移動式代理人之網路協同防衛系統★ 鏈路狀態資訊不確定下QoS路由之研究
★ 以訊務觀察法改善光突發交換技術之路徑建立效能★ 感測網路與競局理論應用於舒適性空調之研究
★ 以搜尋樹為基礎之無線感測網路繞徑演算法★ 基於無線感測網路之行動裝置輕型定位系統
★ 多媒體導覽玩具車★ 以Smart Floor為基礎之導覽玩具車
★ 行動社群網路服務管理系統-應用於發展遲緩兒家庭★ 具位置感知之穿戴式行動廣告系統
★ 調適性車載廣播★ 車載網路上具預警能力之車輛碰撞避免機制
★ 應用於無線車載網路上之合作式交通資訊傳播機制以改善車輛擁塞★ 智慧都市中應用車載網路以改善壅塞之調適性虛擬交通號誌
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 近年來,新型網路架構蓬勃發展、對於網路攻擊的防禦思維也日新月異,其中軟體定義網路(Software Define Network, SDN)的技術被提出,將控制層從交換機硬體中抽離,使控制層透過軟體定義其行為並集中管理。隨著SDN技術日益成熟,Programming Protocol-independent Packet Processors(P4)被提出,不同於原始SDN技術使控制層可程式化,P4技術使資料層也可程式化,使得SDN的網路管理者不再只能單純基於交換機晶片廠提供的封包欄位進行程式編寫,在P4的網路環境中,是由網路管理者自行決定封包的處理轉發方式,以此達到真正的軟體定義網路。另一方面,入侵檢測系統(Intrusion Detection System, IDS)技術也被提出,IDS透過網路攻擊的特徵定義捕捉封包的規則,每一個封包都必須接受IDS中的規則比對,而IDS會針對符合規則描述特徵的封包發出Alert,並記錄成具可讀性的log以供網路管理者做日後分析。
本論文所提出的系統是針對分散式阻斷服務攻擊(Distributed Denial of Service, DDoS)及分散式反射阻斷服務攻擊(Distributed Reflection Denial of Service, DRDoS)泛洪攻擊的偵測與防禦機制,並提出基於入侵數據的複合型閥值演算法(Intrusion Statistics-based Hybrid Threshold AlgoRithm, ISHTAR),透過IDS針對每一個封包進行規則比對,將符合特徵的封包資訊構成入侵數據,ISHTAR將透過入侵數據計算當前時間段是否正遭受惡意攻擊,若正遭受攻擊,則會利用P4的protocol-independency的特性,對P4交換機佈建基於custom protocol的惡意攻擊防禦機制,使惡意封包被丟棄,並使合法封包能正常通訊,進而達成惡意攻擊的偵防機制。
摘要(英) In recent years, new network architectures are booming and defense thinking against cyber attacks is also evolving. Among them, Software Define Network (SDN) technology has been proposed to separate the control layer from the switch hardware, centrally manage the control layer and define what it should do by software. As SDN technology becomes more mature, Programming Protocol-independent Packet Processors (P4) are proposed. Unlike the original SDN technology that the control layer can be programmed. P4 technology enables the data layer to be programmed, so that SDN network managers no longer be restricted by switch manufacture. In the P4 network environment, the network administrator decides the packet processing and forwarding method to achieve a true software-defined network. Also, Intrusion Detection System (IDS) technology has also been proposed. IDS defines the rules for capturing packets through the characteristics of network attacks. Each packet must go through the rule comparison in IDS, and IDS will claim the alert to those packets which match the rules, and record it into a readable log for network administrators to do later analysis.
The system proposed in this paper is aimed at the detection and defense mechanism of Distributed Denial of Service (DDoS) and Distributed Reflection Denial of Service (DRDoS) flood attacks, and Intrusion Statistics-based Hybrid Threshold AlgoRithm (ISHTAR) is proposed. The IDS is used to compare the rules of each packet to match the characteristics of the packet information into the intrusion data. ISHTAR will use the intrusion data to calculate whether the current time period is under malicious attack. If it is under attack, it will use the protocol-independency feature of P4 to build a malicious attack defense mechanism based on custom protocol for the P4 switch. So that malicious packets are discarded, and legal packets can keep normal communication, and then achieve a malicious attack detection and prevention mechanism.
關鍵字(中) ★ 軟體定義網路
★ 入侵檢測系統
★ Programming Protocol-independent Packet Processors
★ 分散式阻斷服務攻擊
★ 分散式反射阻斷服務攻擊
關鍵字(英) ★ Software defined network
★ intrusion detection system
★ Programming Protocol-independent Packet Processors
★ Distributed Denial of Service
★ Distributed Reflected Denial of Service
論文目次 目錄
摘要 i
Abstract ii
誌謝 iv
目錄 v
圖目錄 viii
表目錄 xi
第一章 緒論 1
1.1. 概要 1
1.2. 研究動機 2
1.3. 研究目的 3
1.4. 章節架構 4
第二章 背景知識與相關研究 5
2.1. 軟體定義網路 5
2.2. P4: Programming Protocol-Independent Packet Processor 7
2.3. 入侵檢測系統 8
2.4. 分散式阻斷服務及分散式反射阻斷服務 10
2.5. 相關研究之比較 13
第三章 系統架構設計及機制運作 16
3.1. 系統架構與設計 16
3.1.1. Traffic Monitor Module 18
3.1.2. IDS Rule Implementer Module 19
3.1.3. Alert-Log Generator Module 20
3.1.4. Alert-Log Analyzer Module 21
3.1.5. ISHTAR Module 21
3.1.6. Malice Announcement Module 22
3.1.7. DDoS Notification Module 22
3.1.8. P4Runtime Rule Generator Module 22
3.1.9. In-Crisis Traffic Management Module 23
3.2. 系統運作及機制 24
3.2.1. 資料符號表 24
3.2.2. 系統運作流程與機制 29
3.3. 系統實作與假設 40
第四章 實驗與討論 44
4.1. P4網路環境及偵防機制之驗證 44
4.1.1. 基於IPv4/IPv6雙軌機制之P4交換機路由及轉送驗證 44
4.1.2. ISHTAR演算法之運作及驗證 50
4.1.3. 危機時封包管理模組對連線能力之驗證 54
4.2. DDoS及DRDoS之偵測與防禦機制驗證 57
4.2.1. TCP SYN Flooding攻擊及其偵測防禦機制驗證 57
4.2.2. IPv6 RA Flooding攻擊及其偵測防禦機制驗證 65
4.2.3. Memcached Flooding攻擊及其偵測防禦機制驗證 69
4.2.4. 複合式惡意攻擊及其偵測防禦機制驗證 73
4.3. P4網路環境評估及分析 76
4.3.1. Insider行為及其攻擊之偵測及防禦驗證 77
4.3.2. 自定義標頭長度對封包連線能力之影響 81
第五章 結論與未來研究方向 84
5.1. 結論 84
5.2. 研究限制 85
5.3. 未來研究方向 86
參考文獻 89
參考文獻 [1] McKeown, Nick, et al. "OpenFlow: enabling innovation in campus networks." ACM SIGCOMM Computer Communication Review 38.2 (2008): 69-74.
[2] Bosshart, Pat, et al. "P4: Programming protocol-independent packet processors." ACM SIGCOMM Computer Communication Review 44.3 (2014): 87-95.
[3] Wikipedia, Entropy.
Available: https://en.wikipedia.org/wiki/Entropy_(information_theory)
[4] P4Compiler Available: https://github.com/p4lang/p4c
[5] Cello, Marco, Mario Marchese, and Maurizio Mongelli. "On the qos estimation in an openflow network: The packet loss case." IEEE Communications Letters 20.3 (2016): 554-557.
[6] Kaur, Karamjeet, Sukhveer Kaur, and Vipin Gupta. "Performance analysis of python based openflow controllers." (2016).
[7] Yi, Tao, and Hanyu Li. "Flow-split: An approach to reduce flow establish time and invoking of controller in OpenFlow networks." 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference. IEEE, 2016.
[8] Osiński, Tomasz, et al. "DPPx: A P4-based Data Plane Programmability and Exposure framework to enhance NFV services." 2019 IEEE Conference on Network Softwarization (NetSoft). IEEE, 2019.
[9] Kundel, Ralf, et al. "P4-CoDel: Active queue management in programmable data planes." 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2018.
[10] Suricata, open-source IDS/IPS/NSM engine
Available: https://suricata-ids.org/
[11] Snort, Network Intrusion Detection & Prevention System
Available: https://www.snort.org/
[12] CUDA toolkit
Available: https://developer.nvidia.com/cuda-toolkit
[13] Nam, Kiho, and Keecheon Kim. "A study on sdn security enhancement using open source ids/ips suricata." 2018 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2018.
[14] Jakimoski, Kire, and Nidhi V. Singhai. "Improvement of Hardware Firewall’s Data Rates by Optimizing Suricata Performances." 2019 27th Telecommunications Forum (TELFOR). IEEE, 2019.
[15] Jiao, Jiahui, et al. "Detecting TCP-based DDoS attacks in Baidu cloud computing data centers." 2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS). IEEE, 2017.
[16] Hong, Kiwon, et al. "SDN-assisted slow HTTP DDoS attack defense method." IEEE Communications Letters 22.4 (2017): 688-691.
[17] Thomas, Roshni Mary, and Divya James. "DDOS detection and denial using third party application in SDN." 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017.
[18] Memcached - a distributed memory object caching system
Available: https://memcached.org/
[19] Priya, P. Mohana, et al. "The protocol independent detection and classification (PIDC) system for DRDoS attack." 2014 International Conference on Recent Trends in Information Technology. IEEE, 2014.
[20] Huang, Haiou, et al. "An authentication scheme to defend against UDP DrDoS attacks in 5G networks." IEEE Access 7 (2019): 175970-175979.
[21] Gao, Yuxuan, et al. "A machine learning based approach for detecting DRDoS attacks and its performance evaluation." 2016 11th Asia Joint Conference on Information Security (AsiaJCIS). IEEE, 2016.
[22] Zhauniarovich, Yury, and Priyanka Dodia. "Sorting the Garbage: Filtering Out DRDoS Amplification Traffic in ISP Networks." 2019 IEEE Conference on Network Softwarization (NetSoft). IEEE, 2019.
[23] Lukaseder, Thomas, et al. "An sdn-based approach for defending against reflective ddos attacks." 2018 IEEE 43rd Conference on Local Computer Networks (LCN). IEEE, 2018.
[24] Grigoryan, Garegin, and Yaoqing Liu. "LAMP: Prompt layer 7 attack mitigation with programmable data planes." 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA). IEEE, 2018.
[25] Sheng Hung Haung. "P4 Switch-Based Solution for Moving Target Defense Networks" Master, Department of Computer Science and Information Engineering, National Central University, 2018.
[26] Behavior Model version 2. Available: https://github.com/p4lang/behavioral-model
[27] Lua. Available: https://www.lua.org/
[28] P4Runtime. Available: https://github.com/p4lang/p4runtime
[29] gRPC, Google Remote Procedure call. Available: https://grpc.io/
[30] protobuf, protocol buffer. Available: https://github.com/protocolbuffers/protobuf
[31] OSI model. Available: https://en.wikipedia.org/wiki/OSI_model
[32] IEEE public EtherType list
Available: http://standards-oui.ieee.org/ethertype/eth.txt
[33] hping3. Available: http://www.hping.org/
[34] thc-ipv6. Available: https://github.com/vanhauser-thc/thc-ipv6
[35] Scapy. Available: https://scapy.net/
[36] v1model.
Available: https://github.com/p4lang/p4c/blob/master/p4include/v1model.p4
[37] Mininet. Available: http://mininet.org/
[38] iperf. Available: https://iperf.fr/
[39] IANA preserved IPv6 prefix.
Available: https://www.iana.org/assignments/ipv6-address-space/ipv6-address-space.xhtml
[40] RFC4291. Available: https://tools.ietf.org/html/rfc4291
[41] Yang, Guosong, et al. "Modeling and mitigating the coremelt attack." 2018 Annual American Control Conference (ACC). IEEE, 2018.
[42] Kim, Kyoungmin, et al. "DDoS mitigation: Decentralized CDN using private blockchain." 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN). IEEE, 2018.
[43] Hua, Yakang, Yuanzheng Du, and Dongzhi He. "Classifying Packed Malware Represented as Control Flow Graphs using Deep Graph Convolutional Neural Network." 2020 International Conference on Computer Engineering and Application (ICCEA). IEEE, 2020.
[44] Rajashree, S., K. S. Soman, and Pritam Gajkumar Shah. "Security with IP address assignment and spoofing for smart IOT devices." 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2018.
指導教授 周立德(Li-Der Chou) 審核日期 2020-7-28
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明