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題名 自動化學習環資變異以減緩RFID移位冒傳風險
Automatically analyzing ambient conditions to mitigate RFID relay risks
作者 蔡承憲
Tsai, Chen-Hsien
貢獻者 杜雨儒
Tu, Yu-Ju
蔡承憲
Tsai, Chen-Hsien
關鍵詞 無線射頻標籤
移位冒傳攻擊
機器學習
環境資訊
資料保護規範
RFID
Relay attack
Machine learning
Ambient condition
GDPR
日期 2020
上傳時間 3-Aug-2020 17:36:49 (UTC+8)
摘要 RFID (Radio Frequency Identification) 連同 NFC (Near Field Communication)，已大量應用在日常生活中，例如: 電子票券、免接觸式車鑰、自動化物貨管理等等。另一方面，自2018年起生效的GDPR (General Data Protection Regulation) 更加嚴格規範了數據隱私與保護，因此對於RFID的種種應用，資訊安全層面的考量，益發顯得重要。
移位冒傳攻擊(Relay Attack)，對於RFID而言，非常難以防範，而目前相關文獻中也並無存在任何最佳解決方案。本研究因此提出一個自動化學習環資變異系統架構，以此藉由機器學習模型，整合RFID外在環境資訊，模擬在不同場景之下，受到移位冒傳攻擊的防禦對策，以期對於如此棘手的資安攻擊達到更好的偵防成效。
參考文獻 1. Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H. P. (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys & Tutorials, 16(4), 1996-2018.
2. Avoine, G., & Oechslin, P. (2005, February). RFID traceability: A multilayer problem. In International Conference on Financial Cryptography and Data Security (pp. 125-140). Springer, Berlin, Heidelberg.
3. Azmoodeh, A., Dehghantanha, A., Conti, M., & Choo, K. K. R. (2018). Detecting crypto-ransomware in IoT networks based on energy consumption footprint. Journal of Ambient Intelligence and Humanized Computing, 9(4), 1141-1152.
4. Baashirah, R., and Abuzneid, A. (2018). "Survey on Prominent RFID Authentication Protocols for Passive Tags", Sensors (18:10), p. 3584
5. Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: Logistic regression. Critical care, 9(1), 112.
6. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
7. Brun, O., Yin, Y., Gelenbe, E., Kadioglu, Y. M., Augusto-Gonzalez, J., & Ramos, M. (2018, February). Deep learning with dense random neural networks for detecting attacks against iot-connected home environments. In International ISCIS Security Workshop (pp. 79-89). Springer, Cham.
8. Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), 1153-1176.
9. Burmester, M., and Medeiros, B. (2007). "RFID security: attacks countermeasures and challenges", The 5th RFID Academic Convocation The RFID Journal Conference,.
10. C. Lim and T. Kwon. Strong and robust RFID authentication enabling perfect ownership transfer. In P. Ning, S. Qing, and N. Li, editors, Conference on Information and Communications Security — ICICS ’06, volume 4307 of Lecture Notes in Computer Science, pages 1–20, Raleigh, North Carolina, USA, December 2006. Springer-Verlag
11. Caprolu, M., Sciancalepore, S., & Di Pietro, R. (2020). Short-Range Audio Channels Security: Survey of Mechanisms, Applications, and Research Challenges. IEEE Communications Surveys & Tutorials.
12. Chen, X., Cao, T., Zhu, M., Liu, W., & Guo, Y. (2014). Traceability attack algorithm on EOHLCAP RFID authentication protocol. Journal of Computers, 9(4), 939.
13. Chen, X., Cao, T., Zhu, M., Liu, W., and Guo, Y. (2014). "Traceability Attack Algorithm on EOHLCAP RFID Authentication Protocol", Journal of Computers (9:4)
14. Choi, W., Seo, M., and Lee, D. (2018). "Sound-Proximity: 2-Factor Authentication against Relay Attack on Passive Keyless Entry and Start System", Journal of Advanced Transportation(2018), pp. 1-13.
15. Conti, M., & Lal, C. (2020). Context-based Co-presence detection techniques: A survey. Computers & Security, 88, 101652.
16. D. Tagra, M. Rahman and S. Sampalli, "Technique for preventing DoS attacks on RFID systems," SoftCOM 2010, 18th International Conference on Software, Telecommunications and Computer Networks, Split, Dubrovnik, 2010, pp. 6-10.
17. Deng, M., & Zhu, W. (2013). Desynchronization attacks on RFID security protocols. TELKOMNIKA Indonesian Journal of Electrical Engineering, 11(2), 681-688.
18. Deng, M., and Zhu, W. (2013). "Desynchronization Attacks on RFID Security Protocols", TELKOMNIKA Indonesian Journal of Electrical Engineering (11:2)
19. Dimitriou, T. (2005, September). A lightweight RFID protocol to protect against traceability and cloning attacks. In First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM`05) (pp. 59-66). IEEE.
20. Dreiseitl, S., & Ohno-Machado, L. (2002). “Logistic regression and artificial neural network classification models: a methodology review. Journal of biomedical informatics,” 35(5-6), 352-359.
21. Garcia, F. C. C., & Muga II, F. P. (2016). Random forest for malware classification. arXiv preprint arXiv:1609.07770.
22. Gupta, G. P., & Kulariya, M. (2016). A framework for fast and efficient cyber security network intrusion detection using apache spark. Procedia Computer Science, 93, 824-831.
23. Gurulian, I., Akram, R., Markantonakis, K., and Mayes, K. (2017). "Preventing relay attacks in mobile transactions using infrared light", Proceedings of the Symposium on Applied Computing - SAC `17 .
24. Gurulian, I., Shepherd, C., Markantonakis, K., Akram, R. N., & Mayes, K. (2016). When theory and reality collide: Demystifying the effectiveness of ambient sensing for NFC-based proximity detection by applying relay attack data. arXiv preprint arXiv:1605.00425.
25. Habibi, M. H., Gardeshi, M., & Alaghband, M. R. (2011). Practical attacks on a RFID authentication protocol conforming to EPC C-1 G-2 standard. arXiv preprint arXiv:1102.0763.
26. Habibi, M., Gardeshi, M., and Alaghband, M. (2011). "Practical Attacks on a RFID Authentication Protocol Conforming to EPC C-1 G-2 Standard", International Journal of UbiComp (2:1), pp. 1-13
27. Hancke, G., and Kuhn, M. (2005). "An RFID Distance Bounding Protocol", First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM`05)
28. Kamaludin, H., Mahdin, H., & Abawajy, J. H. (2018). Clone tag detection in distributed RFID systems. Plos One, 13(3). doi: 10.1371/journal.pone.0193951
29. Kang, S., Kim, J., and Hong, M. (2014). "Button-based method for the prevention of near field communication relay attacks", International Journal of Communication Systems (28:10), pp. 1628-1639
30. Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). “Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering,” 160, 3-24.
31. Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1), 3-24.
32. Kotsiantis, Sotiris. (2007). “Supervised Machine Learning: A Review of Classification Techniques.” Informatica (Ljubljana).
33. Lin, C., Lai, Y., Tygar, J., Yang, C., and Chiang, C. (2007). "Coexistence Proof Using Chain of Timestamps for Multiple RFID Tags", Lecture Notes in Computer Science , pp. 634-643
34. Liu, Y., Zhang, J., Zheng, W., & Hancke, G. P. (2019, July). Approaches for Best-Effort Relay-Resistant Channels on Standard Contactless Channels. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) (Vol. 1, pp. 1719-1724). IEEE.
35. Mitrokotsa, A., Rieback, M. R., & Tanenbaum, A. S. (2010). Classification of RFID attacks. Gen, 15693(14443), 14.
36. Narudin, F. A., Feizollah, A., Anuar, N. B., & Gani, A. (2016). Evaluation of machine learning classifiers for mobile malware detection. Soft Computing, 20(1), 343-357.
37. Ozay, M., Esnaola, I., Vural, F. T. Y., Kulkarni, S. R., & Poor, H. V. (2015). Machine learning methods for attack detection in the smart grid. IEEE transactions on neural networks and learning systems, 27(8), 1773-1786.
38. Parada, R., Melia-Segui, J., Morenza-Cinos, M., Carreras, A., & Pous, R. (2015). Using RFID to detect interactions in ambient assisted living environments. IEEE Intelligent Systems, 30(4), 16-22.
39. Parada, Raúl & Melià-Seguí, Joan & Morenza-Cinos, Marc & Carreras, Anna & Pous, Rafael. (2015). Using RFID to Detect Interactions in Ambient Assisted Living Environments. IEEE Intelligent Systems. 30. 16 - 22.
40. Paydar, S., Endut, I. R., & Lajevardi, A. (2013, September). Environmental determinants of RFID adoption in retail supply chain, a binary logistic regression analysis. In 2013 IEEE International Conference on RFID-Technologies and Applications (RFID-TA) (pp. 1-6). IEEE.
41. Paydar, S., Endut, I. R., & Lajevardi, A. (2013, September). Environmental determinants of RFID adoption in retail supply chain, a binary logistic regression analysis. In 2013 IEEE International Conference on RFID-Technologies and Applications (RFID-TA) (pp. 1-6). IEEE.
42. Rizvi, S., Imler, J., Ritchey, L., & Tokar, M. (2019, March). Securing PKES against Relay Attacks using Coordinate Tracing and Multi-Factor Authentication. In 2019 53rd Annual Conference on Information Sciences and Systems (CISS) (pp. 1-6). IEEE.
43. Saxena, N., Uddin, M. B., Voris, J., & Asokan, N. (2011, March). Vibrate-to-unlock: Mobile phone assisted user authentication to multiple personal RFID tags. In 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom) (pp. 181-188). IEEE.
44. Shen, W., Xu, H., Sun, R., and Wang, R. (2015). "Research on Defense Technology of Relay Attacks in RFID Systems", Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication
45. Shun, J., & Malki, H. A. (2008, October). Network intrusion detection system using neural networks. In 2008 Fourth International Conference on Natural Computation (Vol. 5, pp. 242-246). IEEE.
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47. Song, B., and Mitchell, C. (2008). "RFID authentication protocol for low-cost tags", Proceedings of the first ACM conference on Wireless network security - WiSec `08 .
48. Sportiello, L., & Ciardulli, A. (2013, July). Long distance relay attack. In International Workshop on Radio Frequency Identification: Security and Privacy Issues (pp. 69-85). Springer, Berlin, Heidelberg.
49. Sung, A. H., & Mukkamala, S. (2003, January). Identifying important features for intrusion detection using support vector machines and neural networks. In 2003 Symposium on Applications and the Internet, 2003. Proceedings. (pp. 209-216). IEEE.
50. T. Dimitriou, "A Lightweight RFID Protocol to protect against Traceability and Cloning attacks," First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM`05), Athens, 2005, pp. 59-66.
51. Tagra, D., Rahman, M., & Sampalli, S. (2010, September). Technique for preventing DoS attacks on RFID systems. In SoftCOM 2010, 18th International Conference on Software, Telecommunications and Computer Networks (pp. 6-10). IEEE.
52. Tsai, C. F., Hsu, Y. F., Lin, C. Y., & Lin, W. Y. (2009). “Intrusion detection by machine learning: A review. expert systems with applications,” 36(10), 11994-12000.
53. Tu, Y., and Piramuthu, S. (2017). "Lightweight non-distance-bounding means to address RFID relay attacks", Decision Support Systems (102), pp. 12-21
54. Urien, P., and Piramuthu, S. (2013). "Identity-Based Authentication to Address Relay Attacks in Temperature Sensor-enabled Smartcards", Systems and Technologies (SmartSysTech), Proceedings of 2013 European Conference on, June 2013, pp. 1–7.
55. Wang, J., Lounis, K., & Zulkernine, M. (2019, July). CSKES: A context-based secure keyless entry system. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 1, pp. 817-822). IEEE.
56. Wang, S,H., Liu, S., and Chen, D. (2012). "Efficient Passive Full-disclosure Attack on RFID Light-weight Authentication Protocols LMAP++ and SUAP", TELKOMNIKA Indonesian Journal of Electrical Engineering (10:6)
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60. Xu, H., Shen, W., Li, P., Mayes, K., Wang, R., Li, D., & Yang, S. (2019). Novel implementation of defence strategy of relay attack based on cloud in RFID systems. International Journal of Information and Computer Security, 11(2), 120-144.
 
描述 碩士
國立政治大學
資訊管理學系
107356033
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107356033
資料類型 thesis
dc.contributor.advisor 杜雨儒zh_TW
dc.contributor.advisor Tu, Yu-Juen_US
dc.contributor.author (Authors) 蔡承憲zh_TW
dc.contributor.author (Authors) Tsai, Chen-Hsienen_US
dc.creator (作者) 蔡承憲zh_TW
dc.creator (作者) Tsai, Chen-Hsienen_US
dc.date (日期) 2020en_US
dc.date.accessioned 3-Aug-2020 17:36:49 (UTC+8)-
dc.date.available 3-Aug-2020 17:36:49 (UTC+8)-
dc.date.issued (上傳時間) 3-Aug-2020 17:36:49 (UTC+8)-
dc.identifier (Other Identifiers) G0107356033en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/130983-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 107356033zh_TW
dc.description.abstract (摘要) RFID (Radio Frequency Identification) 連同 NFC (Near Field Communication)，已大量應用在日常生活中，例如: 電子票券、免接觸式車鑰、自動化物貨管理等等。另一方面，自2018年起生效的GDPR (General Data Protection Regulation) 更加嚴格規範了數據隱私與保護，因此對於RFID的種種應用，資訊安全層面的考量，益發顯得重要。
移位冒傳攻擊(Relay Attack)，對於RFID而言，非常難以防範，而目前相關文獻中也並無存在任何最佳解決方案。本研究因此提出一個自動化學習環資變異系統架構，以此藉由機器學習模型，整合RFID外在環境資訊，模擬在不同場景之下，受到移位冒傳攻擊的防禦對策，以期對於如此棘手的資安攻擊達到更好的偵防成效。		zh_TW
dc.description.tableofcontents 第一章 緒論 1
第二章 文獻探討 4
第一節 RFID資訊安全背景 4
一、 GDPR (General Data Protection Regulation)背景 5
二、 GDPR 原則 6
三、 GDPR與RFID相關條例 6
第二節 移位冒傳攻擊 8
一、 移位冒傳攻擊(Relay Attack)的背景 8
二、 綜觀RFID資訊攻擊，與移位冒傳攻擊之差別 8
三、 RFID移位冒傳攻擊的進行方式 15
四、 已知移位冒傳攻擊的解決方法 15
五、 移位冒傳攻擊的解決趨勢 18
第三節 自動化機器學習與數據解析 19
一、 機器學習的背景 19
二、 機器學習常見模型與使用領域 19
三、 數位資安風險常用模型 20
四、 隨機森林Random Forests 23
五、 羅吉斯回歸Logistic regression 23
六、 支援向量機Support Vector Machines 24
七、 類神經網路 Neural Network 24
八、 自動化機器學習總結 25
第三章 本研究所提出自動化學習環資變異方案 25
一、 自動化學習環資變異方案流程 25
二、 自動化學習環資變異方案結果評估 29
第四章 實驗設計與評估 30
一、 移位冒傳攻擊狀況設計 30
二、 攻擊狀況數據基本統計含意 36
三、 攻擊狀況數據模型結果 38
四、 環資失真異常模擬 43
第五章 未來展望 82
第六章 文獻參考 84
附錄一 環資實驗照片實景 1
附錄二 機器學習模型參數圖 9
附錄三 政府開放資料參考格式 11		zh_TW
dc.format.extent 5928264 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107356033en_US
dc.subject (關鍵詞) 無線射頻標籤zh_TW
dc.subject (關鍵詞) 移位冒傳攻擊zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 環境資訊zh_TW
dc.subject (關鍵詞) 資料保護規範zh_TW
dc.subject (關鍵詞) RFIDen_US
dc.subject (關鍵詞) Relay attacken_US
dc.subject (關鍵詞) Machine learningen_US
dc.subject (關鍵詞) Ambient conditionen_US
dc.subject (關鍵詞) GDPRen_US
dc.title (題名) 自動化學習環資變異以減緩RFID移位冒傳風險zh_TW
dc.title (題名) Automatically analyzing ambient conditions to mitigate RFID relay risksen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 1. Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H. P. (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys & Tutorials, 16(4), 1996-2018.
2. Avoine, G., & Oechslin, P. (2005, February). RFID traceability: A multilayer problem. In International Conference on Financial Cryptography and Data Security (pp. 125-140). Springer, Berlin, Heidelberg.
3. Azmoodeh, A., Dehghantanha, A., Conti, M., & Choo, K. K. R. (2018). Detecting crypto-ransomware in IoT networks based on energy consumption footprint. Journal of Ambient Intelligence and Humanized Computing, 9(4), 1141-1152.
4. Baashirah, R., and Abuzneid, A. (2018). "Survey on Prominent RFID Authentication Protocols for Passive Tags", Sensors (18:10), p. 3584
5. Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: Logistic regression. Critical care, 9(1), 112.
6. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
7. Brun, O., Yin, Y., Gelenbe, E., Kadioglu, Y. M., Augusto-Gonzalez, J., & Ramos, M. (2018, February). Deep learning with dense random neural networks for detecting attacks against iot-connected home environments. In International ISCIS Security Workshop (pp. 79-89). Springer, Cham.
8. Buczak, A. L., & Guven, E. (2015). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications surveys & tutorials, 18(2), 1153-1176.
9. Burmester, M., and Medeiros, B. (2007). "RFID security: attacks countermeasures and challenges", The 5th RFID Academic Convocation The RFID Journal Conference,.
10. C. Lim and T. Kwon. Strong and robust RFID authentication enabling perfect ownership transfer. In P. Ning, S. Qing, and N. Li, editors, Conference on Information and Communications Security — ICICS ’06, volume 4307 of Lecture Notes in Computer Science, pages 1–20, Raleigh, North Carolina, USA, December 2006. Springer-Verlag
11. Caprolu, M., Sciancalepore, S., & Di Pietro, R. (2020). Short-Range Audio Channels Security: Survey of Mechanisms, Applications, and Research Challenges. IEEE Communications Surveys & Tutorials.
12. Chen, X., Cao, T., Zhu, M., Liu, W., & Guo, Y. (2014). Traceability attack algorithm on EOHLCAP RFID authentication protocol. Journal of Computers, 9(4), 939.
13. Chen, X., Cao, T., Zhu, M., Liu, W., and Guo, Y. (2014). "Traceability Attack Algorithm on EOHLCAP RFID Authentication Protocol", Journal of Computers (9:4)
14. Choi, W., Seo, M., and Lee, D. (2018). "Sound-Proximity: 2-Factor Authentication against Relay Attack on Passive Keyless Entry and Start System", Journal of Advanced Transportation(2018), pp. 1-13.
15. Conti, M., & Lal, C. (2020). Context-based Co-presence detection techniques: A survey. Computers & Security, 88, 101652.
16. D. Tagra, M. Rahman and S. Sampalli, "Technique for preventing DoS attacks on RFID systems," SoftCOM 2010, 18th International Conference on Software, Telecommunications and Computer Networks, Split, Dubrovnik, 2010, pp. 6-10.
17. Deng, M., & Zhu, W. (2013). Desynchronization attacks on RFID security protocols. TELKOMNIKA Indonesian Journal of Electrical Engineering, 11(2), 681-688.
18. Deng, M., and Zhu, W. (2013). "Desynchronization Attacks on RFID Security Protocols", TELKOMNIKA Indonesian Journal of Electrical Engineering (11:2)
19. Dimitriou, T. (2005, September). A lightweight RFID protocol to protect against traceability and cloning attacks. In First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM`05) (pp. 59-66). IEEE.
20. Dreiseitl, S., & Ohno-Machado, L. (2002). “Logistic regression and artificial neural network classification models: a methodology review. Journal of biomedical informatics,” 35(5-6), 352-359.
21. Garcia, F. C. C., & Muga II, F. P. (2016). Random forest for malware classification. arXiv preprint arXiv:1609.07770.
22. Gupta, G. P., & Kulariya, M. (2016). A framework for fast and efficient cyber security network intrusion detection using apache spark. Procedia Computer Science, 93, 824-831.
23. Gurulian, I., Akram, R., Markantonakis, K., and Mayes, K. (2017). "Preventing relay attacks in mobile transactions using infrared light", Proceedings of the Symposium on Applied Computing - SAC `17 .
24. Gurulian, I., Shepherd, C., Markantonakis, K., Akram, R. N., & Mayes, K. (2016). When theory and reality collide: Demystifying the effectiveness of ambient sensing for NFC-based proximity detection by applying relay attack data. arXiv preprint arXiv:1605.00425.
25. Habibi, M. H., Gardeshi, M., & Alaghband, M. R. (2011). Practical attacks on a RFID authentication protocol conforming to EPC C-1 G-2 standard. arXiv preprint arXiv:1102.0763.
26. Habibi, M., Gardeshi, M., and Alaghband, M. (2011). "Practical Attacks on a RFID Authentication Protocol Conforming to EPC C-1 G-2 Standard", International Journal of UbiComp (2:1), pp. 1-13
27. Hancke, G., and Kuhn, M. (2005). "An RFID Distance Bounding Protocol", First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM`05)
28. Kamaludin, H., Mahdin, H., & Abawajy, J. H. (2018). Clone tag detection in distributed RFID systems. Plos One, 13(3). doi: 10.1371/journal.pone.0193951
29. Kang, S., Kim, J., and Hong, M. (2014). "Button-based method for the prevention of near field communication relay attacks", International Journal of Communication Systems (28:10), pp. 1628-1639
30. Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). “Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering,” 160, 3-24.
31. Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1), 3-24.
32. Kotsiantis, Sotiris. (2007). “Supervised Machine Learning: A Review of Classification Techniques.” Informatica (Ljubljana).
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dc.identifier.doi (DOI) 10.6814/NCCU202001142en_US