Introduction

Internet traffic monitoring and analysis entails monitoring the Internet network links and understanding their behavior. This course will cover the techniques and tools being developed for Internet traffic monitoring and analysis. Active and passive monitoring techniques will be studied. In this course, students will get to develop algorithms and a prototype system for capturing packets and analyzing them for various purposes. In this course, the students are allowed to monitor and analyze peer-to-peer networks including blockchain.

Instructor:

Prof. James Won-Ki Hong (PIRL337) : 279-2244, jwkhong@postech.ac.kr

Lectures:

Tue. & Thu. 11:00-12:15 (Science Building II 104)

TA:

Heegon Kim (PIRL 422) : 279-5641, sinjint@postech.ac.kr

Pre-requisites:

A course on computer or telecommunication networks is required. A course on network management is recommended.

Required Textbook:

There will not be a textbook for this course. Lecture materials and research papers will be used for the course.

Recommended Books:

• Alberto Leon-Garcia, Communication Networks: Fundamental Concepts and Key Architectures, McGraw-Hill, ISBN: 0070228396, 2003.
• Richard Stevens, TCP/IP Illustrated, Volume 1: The Protocols, Addison-Wesley, ISBN: 0-201-63346-9, 1994.
• Douglas E. Comer, Computer Networks and Internets, Prentice Hall, ISBN 0-13-599010-6, 1997.
• D. Comer, Internetworking with TCP/IP, Vol I: Principles, Protocols, and Architecture, Second edition, Prentice-Hall, Englewood Cliffs, NJ, ISBN 0-13-468505-9 1991.
• D. Comer and D. Stevens, Internetworking with TCP/IP, Vol II: Design, Implementation, and Internals, Prentice-Hall, Englewood Cliffs, NJ, ISBN 0-13-472242-6 1991.
• William Stallings, Data and Computer Communications, Fifth Edition, Prentice Hall, ISBN 0-02-415425-3, 1997.
• William Stallings, SNMP, SNMPv2, SNMPv3 and RMON 1 and 2, Third Edition, Addison-Wesley, 1999.

Suggested Reference Journals and Conferences:

• John Wiley & Sons, International Journal of Network Management, ISSN 1055-7148.
• IEEE, IEEE Transactions on Network and Service Management
• Springer, Journal of Network and Systems Management, ISSN 1064-7570.
• IEEE/ACM, IEEE/ACM Transactions on Networking
• IEEE Communications Society, IEEE Network, ISSN 0890-8044.
• IEEE Communications Society, IEEE Communications Magazine, ISSN 0163-6084.
• Passive and Active Measurement Workshop (PAM): 2004-2022, 2023
• Internet Measurement Conference (IMC)
• Network Traffic Monitoring & Analysis Conference (TMA)
• IEEE Network Softwarization (NetSoft)
• IEEE/IFIP Network Operations and Management Symposium (NOMS): 1988, 2000, 2004, 2010, 2018, 2023, 2024

Evaluation:

• Evaluation on each student will be done based on the following:
  1. Assignments - 50%
  2. Term Project - 40%
  3. Class Participation - 10%
• Note: the above evaluation scheme may change slightly during the course.

Term Project:

There will be a major term project (worth 40% of the final mark) on developing a traffic monitoring and analysis system. The topics will be discussed in class. Students will be asked to prepare, submit and present materials (Word & Powerpoint) related to the project throughout the course.
1. Project Proposals (in class, March 28)
2. Requirements Document (in class, April 13)
   • Use this guideline to prepare a requirements document for your group project
3. Detailed Design Document (5pm, May 4), Presentation (in class, May 9)
4. Project Presentations and Demos (in PIAI 453, 4:30 to 6:30 pm, Mon., June 5)
5. Term Project Paper for a conference (6 to 8 pages IEEE 2-column style, Due: 12 midnight, Jun 11) - Please follow the instructions given in https://noms2023.ieee-noms.org/authors for writing term project papers.

Assignments:

• There will be a few assignments (worth 40% of the final mark). You should submit your assignment materials to the PLMS.
  • Assignment 1 (1%) (Out: Feb. 20, Due: Mar. 7 in class)
  • Assignment 2 (4%) (Out: Feb. 28, Due: Mar. 14 in class) (your choice) Vmeeting (https://vmeeting.postech.ac.kr/postech/CS702) for presentations
  • Assignment 3 (3%) (Out: Mar. 14, Due: Mar. 21 in class) (your choice)
  • Assignment 4 (6%) (Out: Mar. 21, Due: Mar. 28 in class) (your choice)
  • Assignment 5 (8%) (Out: Mar. 28, Due: Apr. 6 in class)
  • Assignment 6 (7%) (Out: Apr. 6, Due: Apr. 18 in class) Paper choices
  • Assignment 7 (7%) (Out: Apr. 18, Due: Apr. 27 in class) Paper choices
  • Assignment 8 (7%) (Out: Apr. 27, Due: May 9 in class) Paper choices
  • Assignment 9 (7%) (Out: May 9, Due: May 18 in class) Paper choices

• Note: the above assignment schedule may change slightly during the course.

• Late assignments may be handed in, but there will be a penalty of 20% of the mark for assignments turned in less than one day late, and an additional penalty of 10% for each day thereafter.

• Cheating Policy -- Cheating will not be tolerated in this course. Students are encouraged discuss things related to courses and assignments but the materials handed in must be his/her own. The maximum penalty for the first offense is for the assignment in question. For subsequent offenses may result in an automatic failure of the course and possibly other academic punishments.

Class Participation:

Students are strongly encouraged to attend all lectures and to participate in discussions during lectures. 10% of the final mark is assigned for good and active class participation.

Research Papers:

• Surveys
  1. "Deep learning for network traffic monitoring and analysis (NTMA): A survey," Abbasi, Mahmoud, Amin Shahraki, and Amir Taherkordi, Computer Communications 170 (2021): 19-41
  2. "A comprehensive survey on 6G networks: Applications, core services, enabling technologies, and future challenges.," SHAHRAKI, Amin, et al. arXiv preprint arXiv:2101.12475, 2021.
  3. "Survey on zero-trust network security. ," YAN, Xiangshuai; WANG, Huijuan. In: Artificial Intelligence and Security: 6th International Conference, July 17–20, 2020.
  4. "A survey of deep learning-based network anomaly detection," Kwon, Donghwoon, et al, Cluster Computing 22 (2019): 949-961.
  5. "A survey on big data for network traffic monitoring and analysis.," D’ALCONZO, Alessandro, et al. IEEE Transactions on Network and Service Management, 2019, 16.3: 800-813.
  6. "Survey on SDN based network intrusion detection system using machine learning approaches.," SULTANA, Nasrin, et al. Peer-to-Peer Networking and Applications, 2019, 12: 493-501.
  7. "Swarm intelligence-based performance optimization for mobile wireless sensor networks: survey, challenges, and future directions.," CAO, Li; CAI, Yong; YUE, Yinggao. IEEE Access, 2019, 7: 161524-161553.
  8. "Towards the deployment of machine learning solutions in network traffic classification: A systematic survey," Pacheco, Fannia, et al, IEEE Communications Surveys & Tutorials 21.2 (2018): 1988-2014.

• Traffic Measurement and Analysis
  1. "X-Ray Goggles for the ISP: Improving in-Network Web and App QoE Monitoring with Deep Learning," Casas, Pedro, et al. 6th IFIP Network Traffic Measurement and Analysis Conference (TMA). 2022.
  2. "Analyzing the Influence of Resource Prioritization on HTTP/3 HOL Blocking and Performance.," Sander, Constantin, Ike Kunze, and Klaus Wehrle. 2022.
  3. "Swift and Accurate End-to-End Throughput Measurements for High-Speed Networks.," Arifuzzaman, Md, and Engin Arslan., The Network Traffic Measurement and Analysis Conference. 2022.
  4. "Analyzing real-time video delivery over cellular networks for remote piloting aerial vehicles," Baltaci, Aygün, et al. , Proceedings of the 22nd ACM Internet Measurement Conference. 2022.
  5. "Measurement and analysis of implied identity in ad delivery optimization," Kaplan, Levi, et al. , Proceedings of the 22nd ACM Internet Measurement Conference. 2022.
  6. "Enabling passive measurement of zoom performance in production networks," Michel, Oliver, et al. , Proceedings of the 22nd ACM Internet Measurement Conference. 2022.
  7. "A microscopic view of bursts, buffer contention, and loss in data centers," Ghabashneh, Ehab, et al. , Proceedings of the 22nd ACM Internet Measurement Conference. 2022.

• Network Performance
  1. "Understanding 5G performance for real-world services: a content provider's perspective.", Xinjie Yuan, et al. SIGCOMM '22: Proceedings of the ACM SIGCOMM 2022 Conference, August 2022
  2. "L25GC: a low latency 5G core network based on high-performance NFV platforms", Vivek Jain, et al. SIGCOMM '22: Proceedings of the ACM SIGCOMM 2022 Conference, August 2022
  3. "Continuous in-network round-trip time monitoring", Satadal Sengupta, et al. SIGCOMM '22: Proceedings of the ACM SIGCOMM 2022 Conference, August 2022
  4. "PrintQueue: performance diagnosis via queue measurement in the data plane", Yiran Lei, et al. SIGCOMM '22: Proceedings of the ACM SIGCOMM 2022 Conference, August 2022
  5. "Wireless sensor networks for smart cities: Network design, implementation and performance evaluation.," Khalifeh, Ala, et al. Electronics 10.2 (2021): 218.
  6. "Toward 6G: Understanding network requirements and key performance indicators," Slalmi, Ahmed, et al. Transactions on Emerging Telecommunications Technologies 32.3 (2021): e4201.
  7. "Cloud service performance evaluation: status, challenges, and opportunities–a survey from the system modeling perspective.," Duan, Qiang. Digital Communications and Networks 3.2 (2017): 101-111.

• Application Traffic Monitoring and Identification
  1. "Efficient Identification of Cloud Gaming Traffic at the Edge," Philippe Graf, et al. IEEE/IFIP Network Operations and Management Symposium, 8-12 May 2023
  2. "Wavelet-Based Hybrid Machine Learning Model for Out-of-distribution Internet Traffic Prediction," Sajal Saha, et al. IEEE/IFIP Network Operations and Management Symposium, 8-12 May 2023
  3. "A Novel Multimodal Deep Learning Framework for Encrypted Traffic Classification," Peng Lin, et al. IEEE/ACM Transactions on Networking, 2023
  4. "ProGraph: Robust Network Traffic Identification With Graph Propagation," Wenhao Li, et al. IEEE/ACM Transactions on Networking, 2023
  5. "Et-bert: A contextualized datagram representation with pre-training transformers for encrypted traffic classification," X. Lin, G. Xiong, G. Gou, Z. Li, J. Shi, and J. Yu, Feb. 19, 2022. Accessed: Apr. 02, 2022. [Online]. Available: http://arxiv.org/abs/2202.06335
  6. "Self-attentive deep learning method for online traffic classification and its interpretability," G. Xie, Q. Li, and Y. Jiang, Computer Networks, vol. 196, p. 108267, Sep. 2021, doi: 10.1016/j.comnet.2021.108267.
  7. "Multitask learning for network traffic classification," S. Rezaei and X. Liu, in 2020 29th International Conference on Computer Communications and Networks (ICCCN), Aug. 2020, pp. 1–9. doi: 10.1109/ICCCN49398.2020.9209652.

• Social Network & Mobile Traffic
  1. "Mobile access bandwidth in practice: measurement, analysis, and implications," Xinlei Yang, et al. SIGCOMM '22: Proceedings of the ACM SIGCOMM 2022 Conference, August 2022
  2. "A Nationwide Study on Cellular Reliability:Measurement, Analysis, and Enhancements," Yang Li, et al. SIGCOMM '21: Proceedings of the ACM SIGCOMM 2021 Conference, August 2022
  3. "Spatial-temporal aggregation graph convolution network for efficient mobile cellular traffic prediction.," Zhao, Nan, et al. IEEE Communications Letters 26.3 (2021): 587-591.
  4. "Spectragan: Spectrum based generation of city scale spatiotemporal mobile network traffic data," Xu, Kai, et al, Proceedings of the 17th International Conference on emerging Networking EXperiments and Technologies. 2021.
  5. "XAI meets mobile traffic classification: Understanding and improving multimodal deep learning architectures," Nascita, Alfredo, et al. IEEE Transactions on Network and Service Management 18.4 (2021): 4225-4246.
  6. "Multi-task learning at the mobile edge: An effective way to combine traffic classification and prediction.,"Rago, Arcangela, et al. IEEE Transactions on Vehicular Technology 69.9 (2020): 10362-10374.
  7. "Graph attention spatial-temporal network with collaborative global-local learning for citywide mobile traffic prediction," He, Kaiwen, et al. IEEE Transactions on mobile computing 21.4 (2020): 1244-1256.

Topics Covered:

The following is a tentative list of lecture topics for the course.

1. Overview of Computer Networking
2. Overview of Network Softwarization - SDN/NFV/Open Networking
3. Introduction to Traffic Monitoring and Analysis
4. Traffic Monitoring Metrics and Approaches
5. Active Monitoring Techniques
6. Passive Monitoring Techniques
7. Standards Activities
8. PCAP
9. Wireshark Tutorial
10. Monitoring and Analysis Tools
11. Network Neutrality
12. DPNM Research Activities in Traffic Monitoring and Analysis (WebTrafMon & NG-Mon)
13. Project Presentations