CSPWN: A Solution to Protect HTTPS-based Services From Compromised Internet-of-Things

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：28

、訪客IP：18.220.242.160

姓名

歐智文(Chih-Wen Ou) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(CSPWN: A Solution to Protect HTTPS-based Services From Compromised Internet-of-Things)

相關論文

★ USB WORM KILLER: Cure USB Flash Worms Through a USB Flash Worm	★ Discoverer- Rootkit即時偵測系統
★ 一項Android手機上詐騙簡訊的偵測與防禦機制	★ SRA系統防禦ARP欺騙劫持路由器
★ A Solution for Detecting and Defending ARP Spoofing on Virtual Machines	★ 針對遠端緩衝區溢位攻擊之自動化即時反擊系統
★ 即時血清系統: 具攻性防壁之自動化蠕蟲治癒系統	★ DNSPD: Entrap Botnets Through DNS Cache Poisoning Detection
★ TransSQL: A Translation and Validation-based Solution for SQL-Injection Attacks	★ A Spam Mail-based Solution for Botnet Detection and Network Bandwidth Protection
★ Shark: Phishing Information Recycling from Spam Mails	★ FFRTD: Beat Fast-Flux by Response Time Differences
★ Antivirus Software Shield against Antivirus Terminators	★ MAC-YURI : My ACcount, YoUr ResponsIbility
★ KKBB: Kernel Keylogger Bye-Bye	★ CIDP Treatment: An Innovative Mobile Botnet Covert Channel based on Caller IDs with P8 Treatment

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]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

隨著這幾年雲端物聯網裝置產品快速普及，其與桌上終端，或是手機平板等等的行動終端不同的地方主要有兩點。第一點，雲端物聯網裝置的產品成本通常較桌上與行動終端低，因此多數的雲端物聯網產品的安全性無法滿足基本的資安要求。第二點，雲端物聯網裝置的使用者介面明顯不同於桌上行動終端。物聯網裝置的操作介面大多數只是簡單的燈號，除非透過專業工具，否則一般使用者難以於日常使用中快速發覺物聯網裝置的異常運作行為。調查發現，部分雲端物聯網裝置設有基於HTTPS的設定網頁提供給使用者去進行裝置設定，這些HTTPS網頁已成為雲端物聯網殭屍網路入侵的主要目標。對於網路服務提供者以及大多數雲端物聯網裝置產品而言，他們預期的客戶應請求來自於桌上或行動終端的瀏覽器，或是自家開發的客戶端App，來自於陌生的雲端物聯網裝置的服務請求基本上是在預期之外的。若網站服務端的管理者以及雲端物聯網裝置可偵測出遠端的客戶裝置屬於物聯網裝置而非預期的桌上或行動終端瀏覽器時，網站服務端以及雲端物聯網裝置便可根據預先定義的措施來處理這些來者不善的請求，從而避免後續針對該網站以及雲端物聯網的潛在惡意行為。本研究提出了可於雲端物聯網裝置運作之偵測連入裝置為雲端物聯網裝置之技術，取名為CSPWN偵測器。本研究基於物聯網裝置與桌上型電腦跟行動裝置先天性計算能力的差異，找出穩定性高且可識別偵測位於遠端網路雲端物聯網裝置的方法。簡單而言，此方法取得遠端裝置進行金鑰產製的計算時間，並利用此時間差異來做為判定裝置的依據。此法不僅穩定可信避免特徵資料庫慣有的時效問題之外，即使雲端物聯網裝置在遠端網路也能準確偵測。實驗結果顯示，CSPWN偵測器偵測雲端物聯網裝置擁有九成以上的正確率，其中五個裝置的正確率超過98%。偵測非雲端物聯網裝置也擁有八成的正確率，其中五個非雲端物聯網裝置也擁有九成以上的正確率。

摘要(英)

As Internet-of-Things (IoT) devices went popular in recent years, they have become ideal targets for malicious activists, especially for botnet activists. Due to the low cost nature of most IoT devices, the security protection among these cheap devices is often insufficient. Mi- rai botnet is a typical IoT botnet. It is composed of compromised IoT devices, and using these IoT devices to compromise other vulnerable IoT devices across Internet. The Web interface of these vulnerable IoT devices is a major target aimed by Mirai. Most of these targeted IoT devices run the Web interface over HTTPS. For most administrators of Web sites and owners of cloud IoT devices, they may expect that their clients use browsers or their proprietary client Apps to visit their Web interface. Hence, the visiting coming from unknown cloud IoT devices is basically unexpected or even undesired. Under the situation of so many unidentified cloud IoT devices on Internet, the IoT device detec- tion is ideal so that the Web interface can directly reject the connection from unexpected cloud IoT devices. In this dissertation, we propose an approach, named CSPWN Detector, protecting HTTPS-based Web ser- vices, including Web services running on the IoT devices, from accessed by other undesired cloud IoT devices on Internet. CSPWN Detector basically works on diversified key exchange calculation time during TLS negotiation between IoT and non-IoT devices. The result of accuracy evaluation shows that with a best threshold value, CSPWN Detector can detect six IoT devices with the accuracy of 91.6% at least. The accu- racy among five of these six IoT devices are at least 98.1%. Meanwhile, CSPWN Detector can detect six non-IoT devices with the accuracy of 82.5% at least. If the six year old tablet is excluded, the accuracy among rest five of these six non-IoT devices are at least 90.6%.

關鍵字(中)

★ 雲端
★ 物聯網
★ 識別
★ 傳輸層安全協定
★ 殭屍網路

關鍵字(英)

★ CPU
★ IoT
★ fingerprinting
★ TLS
★ negotiation

論文目次

中文摘要.............................. i
Abstract............................... iii
Acknowledgement ......................... v
Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . vii ListofFigures ........................... xi
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

1. Introduction ...................... 1
1.1 The case study of Mirai Botnet . . . . . . . . . . . . 2
1.2 Threats of Cloud IoT devices ............. 4
1.2.1 E-mail spamming ................... 5
1.2.2 Fraud.......................... 5
1.2.3 Vulnerability Mining.................. 6
1.3 Contribution ...................... 7
1.4 Organization ...................... 7

2. Background....................... 9
2.1 WBD: Web-basedBotnetDefender . . . . . . . . . . 9
2.1.1 Methodology of WBD ................. 13
2.1.2 Discussion of WBD .................. 18
2.1.3 Conclusion of WBD .................. 22
2.2 IoTD: E-mail Spamming IoT Devices Defender . . . 22
2.2.1 Background of TLS and SMTP............ 23
2.2.2 Threat Model of IoTD................. 25
2.2.3 IoTD System Design.................. 25
2.2.4 IoTD Threshold Selection............... 28
2.2.5 Discussion of IoTD................... 28
2.2.6 Conclusion of IoTD .................. 30
2.3 RethinkingWBDandIoTD.............. 31

3. RelatedWork...................... 35
3.1 IoTbotnet ....................... 35
3.2 Fingerprinting ..................... 38

4. Objective........................ 41
4.1 Motivation ....................... 41
4.2 ECDHE......................... 42
4.3 BetterApplicability .................. 44
4.4 Threat Model ..................... 44
4.5 Targeted IoT Devices ................. 44
4.6 Assumption and Objective .............. 45

5. ComputationandTLSNegotiation . . . . . . . . . . 47
5.1 TLSNegotiation.................... 47
5.1.1 Cipher Suites...................... 49
5.1.2 RSA for Key Exchange ................ 50
5.1.3 Diffie-Hellman Algorithm ............... 50
5.1.4 Elliptic Curve Diffie Hellman ............. 50
5.1.5 ForwardSecrecy: DHE and ECDHE . . . . . . . . . 51
5.1.6 Using RSA for Authentication . . . . . . . . . . . . 51
5.1.7 ECDSA......................... 52
5.2 Device Computation Capability and TLS negotiation 52
5.2.1 Targeted IoT Devices ................. 53
5.2.2 Diversified Time complexity.............. 55
5.2.3 Pre-Pwning Sample Devices.............. 57

6. CSPWN Detector ................... 63
6.1 System Implementation ................ 63
6.2 Threshold........................ 65

7. Evaluation ....................... 69
7.1 CSPWN Detector Prototype ............. 69
7.2 Devices for Evaluation................. 70
7.3 Accuracy of CSPWNDetector ............ 72

8. Discussion ....................... 77
8.1 Comparisons ...................... 77
8.1.1 Comparison to Nmap ................. 77
8.1.2 Comparison among Wi-Fi Device Fingerprinting Approaches ........................79
8.2 Limitation ....................... 80
8.2.1 IoT Devices behind Proxies .............. 80
8.2.2 Fake IoT Devices.................... 81
8.2.3 IoT Devices Mimic................... 81
8.3 PracticalDeployment ................. 82

9. Conclusion ....................... 83
9.1 FutureWork ...................... 83
9.2 Contribution ...................... 84
9.3 Conclusion ....................... 85

索引 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
參考文獻 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

參考文獻

[1] Gartner says 4.9 billion connected things will be in use in 2015. https://www.gartner.com/newsroom/id/2905717, 2014. [On- line; accessed 6-Sep-2018].

[2] S. Notra, M. Siddiqi, H. Habibi Gharakheili, V. Sivaraman, and R. Boreli. An experimental study of security and privacy risks with emerging household appliances. In 2014 IEEE Conference on Com- munications and Network Security, pages 79–84, Oct 2014.

[3] Sarthak Grover and Nick Feamster. The internet of unpatched
things. https://www.ftc.gov/system/files/documents/ public_comments/2015/10/00071-98118.pdf, 2016. [Online; accessed 8-Jul-2019].

[4] Manos Antonakakis, Tim April, Michael Bailey, Matt Bernhard, Elie Bursztein, Jaime Cochran, Zakir Durumeric, J. Alex Hal- derman, Luca Invernizzi, Michalis Kallitsis, Deepak Kumar, Chaz Lever, Zane Ma, Joshua Mason, Damian Menscher, Chad Seaman, Nick Sullivan, Kurt Thomas, and Yi Zhou. Understanding the mirai botnet. In 26th USENIX Security Symposium (USENIX Security 17), pages 1093–1110, Vancouver, BC, 2017. USENIX Association.

[5] E. Bertino and N. Islam. Botnets and internet of things security. Computer, 50(2):76–79, Feb 2017.

[6] M. Eslahi, R. Salleh, and N. B. Anuar. Mobots: A new generation of botnets on mobile devices and networks. In 2012 International Symposium on Computer Applications and Industrial Electronics (ISCAIE), pages 262–266, Dec 2012.

[7] C. Adams. Sms botnet detection on mobile devices, May 24 2016. US Patent 9,351,167.

[8]Your fridge is full of spam: Proof of an iot-driven at- tack. https://www.proofpoint.com/us/threat-insight/post/ Your-Fridge-is-Full-of-SPAM, 2012. [Online; accessed 2-Oct- 2018].

[9] Justin M. Rao and David H. Reiley. The economics of spam. http://www.davidreiley.com/papers/SpamEconomics. pdf, 2014. [Online; accessed 6-Sep-2018].

[10] M3aawg email metrics report. https://www.m3aawg.org/ for-the-industry/email-metrics-report, 2014. [Online; ac- cessed 2-Oct-2018].

[11] Spam and phishing in q1 2018. https://securelist.com/ spam-and-phishing-in-q1-2018/85650/, 2018. [Online; ac- cessed 2-Oct-2018].

[12] Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. The rise of social bots. Commun. ACM, 59(7):96–104, June 2016.

[13] T. S. Wang, C. S. Lin, and H. T. Lin. Dga botnet detection uti- lizing social network analysis. In 2016 International Symposium on Computer, Consumer and Control (IS3C), pages 333–336, July 2016.

[14] Natarajan Venkatachalam and R. Anitha. A multi-feature approach to detect stegobot: a covert multimedia social network botnet. Mul- timedia Tools and Applications, 76(4):6079–6096, 2017.

[15] Moheeb Abu Rajab, Jay Zarfoss, Fabian Monrose, and Andreas Terzis. A multifaceted approach to understanding the botnet phe- nomenon. In Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement, IMC ’06, pages 41–52, New York, NY, USA, 2006. ACM.

[16] D. Dagon, Guofei Gu, C.P. Lee, and Wenke Lee. A taxonomy of botnet structures. In Computer Security Applications Confer- ence, 2007. ACSAC 2007. Twenty-Third Annual, pages 325–339, Dec 2007.

[17] Guofei Gu, Junjie Zhang, and Wenke Lee. Botsniffer: Detecting botnet command and control channels in network traffic. In NDSS. The Internet Society, 2008.

[18] FBI. Botnets 101 what they are and how to avoid them. https: //www.fbi.gov/news/news_blog/botnets-101, 2013. [Online; accessed 6-Jan-2016].

[19] Aditya K. Sood, Richard J. Enbody, and Rohit Bansal. Dissect- ing spyeye - understanding the design of third generation botnets. Comput. Netw., 57(2):436–450, February 2013.

[20] H. Binsalleeh, T. Ormerod, A. Boukhtouta, P. Sinha, A. Youssef, M. Debbabi, and L. Wang. On the analysis of the zeus botnet crime- ware toolkit. In 2010 Eighth International Conference on Privacy, Security and Trust, pages 31–38, Aug 2010.

[21] Guofei Gu, V. Yegneswaran, P. Porras, J. Stoll, and Wenke Lee. Ac- tive botnet probing to identify obscure command and control chan- nels. In Computer Security Applications Conference, 2009. ACSAC ’09. Annual, pages 241–253, Dec 2009.

[22] C. Livadas, R. Walsh, David Lapsley, and W.T. Strayer. Usilng machine learning technliques to identify botnet traffic. In Local Computer Networks, Proceedings 2006 31st IEEE Conference on, pages 967–974, Nov 2006.

[23] Anestis Karasaridis, Brian Rexroad, and David Hoeflin. Wide-scale botnet detection and characterization. In Proceedings of the First Conference on First Workshop on Hot Topics in Understanding Botnets, HotBots’07, pages 7–7, Berkeley, CA, USA, 2007. USENIX Association.

[24] T. Cai and F. Zou. Detecting http botnet with clustering network traffic. In 2012 8th International Conference on Wireless Communications, Networking and Mobile Computing, pages 1–7, Sept 2012.

[25] G. Kirubavathi Venkatesh and R. Anitha Nadarajan. HTTP Botnet Detection Using Adaptive Learning Rate Multilayer Feed-Forward Neural Network, pages 38–48. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012.

[26] David Zhao, Issa Traore, Bassam Sayed, Wei Lu, Sherif Saad, Ali Ghorbani, and Dan Garant. Botnet detection based on traffic behav- ior analysis and flow intervals. Comput. Secur., 39:2–16, November 2013.

[27] F.H.Hsu,C.S.Wang,C.H.Hsu,C.K.Tso,L.H.Chen,andS.H. Lin. Detect fast-flux domains through response time differences. IEEE Journal on Selected Areas in Communications, 32(10):1947– 1956, Oct 2014.

[28] Postfix. http://www.postfix.org/. [Online; accessed 8-Oct- 2018].

[29] Jonathan B. Postel. Simple mail transfer protocol. STD 10, RFC Editor, August 1982. http://www.rfc-editor.org/rfc/rfc821. txt.

[30] J. Klensin. Simple mail transfer protocol. RFC 2821, RFC Editor, April 2001. http://www.rfc-editor.org/rfc/rfc2821.txt.

[31] J. Klensin. Simple mail transfer protocol. RFC 5321, RFC Editor, October 2008. http://www.rfc-editor.org/rfc/rfc5321.txt.

[32] P. Hoffman. Smtp service extension for secure smtp over transport layer security. RFC 3207, RFC Editor, February 2002. http:// www.rfc-editor.org/rfc/rfc3207.txt.

[33] Tim Dierks and Christopher Allen. The tls protocol version 1.0. RFC 2246, RFC Editor, January 1999. http://www.rfc-editor. org/rfc/rfc2246.txt.

[34] T. Dierks and E. Rescorla. The transport layer security (tls) pro- tocol version 1.1. RFC 4346, RFC Editor, April 2006. http: //www.rfc-editor.org/rfc/rfc4346.txt.

[35] T. Dierks and E. Rescorla. The transport layer security (tls) pro- tocol version 1.2. RFC 5246, RFC Editor, August 2008. http: //www.rfc-editor.org/rfc/rfc5246.txt.

[36] E. Rescorla. The transport layer security (tls) protocol version
1.3. RFC 8446, RFC Editor, August 2018.

[37] A. Freier, P. Karlton, and P. Kocher. The secure sockets layer (ssl) protocol version 3.0. RFC 6101, RFC Editor, August 2011. http://www.rfc-editor.org/rfc/rfc6101.txt.

[38] Cve-2014-3566. https://nvd.nist.gov/vuln/detail/ CVE-2014-3566. [Online; accessed 9-Oct-2018].

[39] Poodle: Sslv3 vulnerability (cve-2014-3566). https://access. redhat.com/articles/1232123. [Online; accessed 9-Oct-2018].

[40] This poodle bites: Exploiting the ssl 3.0 fallback. https://www. openssl.org/~bodo/ssl-poodle.pdf. [Online; accessed 8-Oct- 2018].

[41] Guidelines for the selection, configuration, and use of transport layer security (tls) implementations, 2014.

[42] An update on sslv3 in chrome. https://groups.google.com/ a/chromium.org/forum/#!topic/security-dev/Vnhy9aKM_l4. [Online; accessed 8-Oct-2018].

[43] Q. Xu, R. Zheng, W. Saad, and Z. Han. Device fingerprinting in wireless networks: Challenges and opportunities. IEEE Communi- cations Surveys Tutorials, 18(1):94–104, Firstquarter 2016.

[44] Loh Chin Choong Desmond, Cho Chia Yuan, Tan Chung Pheng, and Ri Seng Lee. Identifying unique devices through wireless finger- printing. In Proceedings of the First ACM Conference on Wireless Network Security, WiSec ’08, pages 46–55, New York, NY, USA, 2008. ACM.

[45] Sergey Bratus, Cory Cornelius, David Kotz, and Daniel Peebles. Active behavioral fingerprinting of wireless devices. In Proceedings of the First ACM Conference on Wireless Network Security, WiSec ’08, pages 56–61, New York, NY, USA, 2008. ACM.

[46] Suman Jana and Sneha Kumar Kasera. On fast and accurate de- tection of unauthorized wireless access points using clock skews. In Proceedings of the 14th ACM International Conference on Mo- bile Computing and Networking, MobiCom ’08, pages 104–115, New York, NY, USA, 2008. ACM.

[47] M. Miettinen, S. Marchal, I. Hafeez, N. Asokan, A. Sadeghi, and S. Tarkoma. Iot sentinel: Automated device-type identification for security enforcement in iot. In 2017 IEEE 37th International Con- ference on Distributed Computing Systems (ICDCS), pages 2177– 2184, June 2017.

[48] sklearn document. http://scikit-learn.org/stable/modules/ generated/sklearn.metrics.roc_curve.html. [Online; accessed 15-Oct-2018].

[49] Guofei Gu, Roberto Perdisci, Junjie Zhang, and Wenke Lee. Bot- miner: Clustering analysis of network traffic for protocol- and structure-independent botnet detection. In Proceedings of the 17th Conference on Security Symposium, SS’08, pages 139–154, Berkeley, CA, USA, 2008. USENIX Association.

[50] Guofei Gu, Phillip Porras, Vinod Yegneswaran, Martin Fong, and Wenke Lee. Bothunter: Detecting malware infection through ids- driven dialog correlation. In Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium, SS’07, pages 12:1– 12:16, Berkeley, CA, USA, 2007. USENIX Association.

[51] Fang Yu, Yinglian Xie, and Qifa Ke. Sbotminer: Large scale search bot detection. In ACM International Conference on Web Search and Data Mining (WSDM), February 2010.

[52] Ali Zand, Giovanni Vigna, Xifeng Yan, and Christopher Kruegel. Extracting probable command and control signatures for detecting botnets. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC ’14, pages 1657–1662, New York, NY, USA, 2014. ACM.

[53] Kuochen Wang, Chun-Ying Huang, Shang-Jyh Lin, and Ying-Dar Lin. A fuzzy pattern-based filtering algorithm for botnet detection. Comput. Netw., 55(15):3275–3286, October 2011.

[54] Junjie Zhang, R. Perdisci, Wenke Lee, Xiapu Luo, and U. Sarfraz. Building a scalable system for stealthy p2p-botnet detection. In In- formation Forensics and Security, IEEE Transactions on, volume 9, pages 27–38, Jan 2014.

[55] S. Khattak, N. R. Ramay, K. R. Khan, A. A. Syed, and S. A. Khayam. A taxonomy of botnet behavior, detection, and defense. IEEE Communications Surveys Tutorials, 16(2):898–924, Second 2014.

[56] Brett Stone-Gross, Marco Cova, Lorenzo Cavallaro, Bob Gilbert, Martin Szydlowski, Richard Kemmerer, Christopher Kruegel, and Giovanni Vigna. Your botnet is my botnet: Analysis of a botnet takeover. In Proceedings of the 16th ACM Conference on Computer and Communications Security, CCS ’09, pages 635–647, New York, NY, USA, 2009. ACM.

[57] Yacin Nadji, Manos Antonakakis, Roberto Perdisci, David Dagon, and Wenke Lee. Beheading hydras: Performing effective botnet takedowns. In Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications Security, CCS ’13, pages 121– 132, New York, NY, USA, 2013. ACM.

[58] Roberto Perdisci, Wenke Lee, and Nick Feamster. Behavioral clus- tering of http-based malware and signature generation using mali- cious network traces. In Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation, NSDI’10, pages 26–26, Berkeley, CA, USA, 2010. USENIX Association.

[59] Erhan J. Kartaltepe, Jose Andre Morales, Shouhuai Xu, and Ravi Sandhu. Social network-based botnet command-and-control: Emerging threats and countermeasures. In Proceedings of the 8th International Conference on Applied Cryptography and Network Se- curity, ACNS’10, pages 511–528, Berlin, Heidelberg, 2010. Springer- Verlag.

[60] Nmap network scanning. https://nmap.org/book/osdetect. html. [Online; accessed 15-Oct-2018].

[61] Jason Franklin, Damon McCoy, Parisa Tabriz, Vicentiu Neagoe, Jamie Van Randwyk, and Douglas Sicker. Passive data link layer 802.11 wireless device driver fingerprinting. In Proceedings of the 15th Conference on USENIX Security Symposium - Volume 15, USENIX-SS’06, Berkeley, CA, USA, 2006. USENIX Association.

[62] Corbett, Beyah, and Copeland. Using active scanning to iden- tify wireless nics. In 2006 IEEE Information Assurance Workshop, pages 239–246, June 2006.

[63] Cherita L. Corbett, Raheem A. Beyah, and John A. Copeland. Passive classification of wireless nics during rate switching. EURASIP Journal on Wireless Communications and Networking, 2008(1):495070, Dec 2007.

[64] C. Neumann, O. Heen, and S. Onno. An empirical study of passive 802.11 device fingerprinting. In 2012 32nd International Conference on Distributed Computing Systems Workshops, pages 593–602, June 2012.

[65] R. Tomšů, S. Marchal, and N. Asokan. Profiling users by modeling web transactions. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pages 2399–2404, June 2017.

[66] J. François, H. Abdelnur, R. State, and O. Festor. Ptf: Passive temporal fingerprinting. In 12th IFIP/IEEE International Sym- posium on Integrated Network Management (IM 2011) and Work- shops, pages 289–296, May 2011.

[67] Yair Meidan, Michael Bohadana, Asaf Shabtai, Martín Ochoa, Nils Ole Tippenhauer, Juan David Guarnizo, and Yuval Elovici. Detection of unauthorized iot devices using machine learning tech- niques. CoRR, abs/1709.04647, 2017.

[68] Bruhadeshwar Bezawada, Maalvika Bachani, Jordan Peterson, Hos- sein Shirazi, Indrakshi Ray, and Indrajit Ray. Iotsense: Behavioral fingerprinting of iot devices. CoRR, abs/1804.03852, 2018.

[69] R. L. Rivest, A. Shamir, and L. Adleman. A method for obtaining digital signatures and public-key cryptosystems. Commun. ACM, 21(2):120–126, February 1978.

[70] W. Diffie and M. Hellman. New directions in cryptography. IEEE Trans. Inf. Theor., 22(6):644–654, September 2006.

[71] Daniel J. Bernstein. Curve25519: New diffie-hellman speed records. In Proceedings of the 9th International Conference on Theory and Practice of Public-Key Cryptography, PKC’06, pages 207–228, Berlin, Heidelberg, 2006. Springer-Verlag.

[72] Openssl 1.1.0 series release notes. https://www.openssl.org/ news/openssl-1.1.0-notes.html. [Online; accessed 24-Aug- 2016].

[73] Wikidevi. https://wikidevi.com/wiki. [Online; accessed 24- May-2019].

指導教授

許富皓(Fu-Hau Hsu)

審核日期

2019-7-12

推文