skip to main content
10.1145/3626111.3628198acmconferencesArticle/Chapter ViewAbstractPublication PagescommConference Proceedingsconference-collections
research-article

Securing Public Clouds using Dynamic Communication Graphs

Published:28 November 2023Publication History

ABSTRACT

We leverage a novel telemetry source available in public clouds today: periodic summaries of every flow that enters or leaves any VM. A key aspect is that such telemetry can be collected transparently to customers and with minimal impact on their workloads. By consuming this telemetry, we show how one may realize complete and dynamic graphs of the communication inside cloud subscriptions. We describe novel analyses over these communication graphs with implications on network security and management.

References

  1. Advanced persistent threats. https://www.cisa.gov/topics/cyber-threats-and-advisories/advanced-persistent-threats.Google ScholarGoogle Scholar
  2. Amazon redshift. https://aws.amazon.com/redshift/.Google ScholarGoogle Scholar
  3. Apache spark. https://www.databricks.com/spark/about.Google ScholarGoogle Scholar
  4. AWS: Data Transfer Costs for Common Architectures. https://go.aws/3cg5J3O.Google ScholarGoogle Scholar
  5. Azure: Bandwidth Pricing. https://bit.ly/3Cou81Z.Google ScholarGoogle Scholar
  6. Azure synapse analytics. https://learn.microsoft.com/en-us/azure/synapse-analytics/.Google ScholarGoogle Scholar
  7. Cisco Adaptive Security Virtual Appliance (ASAv) Data Sheet. https://bit.ly/3UldTJq.Google ScholarGoogle Scholar
  8. Fast ica. https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.FastICA.html.Google ScholarGoogle Scholar
  9. Flow logging for network security groups. https://learn.microsoft.com/en-us/azure/network-watcher/network-watcher-nsg-flow-logging-overview.Google ScholarGoogle Scholar
  10. FortiGate-VM on Amazon Web Services. https://bit.ly/3Bp5qwb.Google ScholarGoogle Scholar
  11. Gcp: Vpc flow logs. https://cloud.google.com/vpc/docs/flow-logs.Google ScholarGoogle Scholar
  12. Google Cloud: Bandwidth Pricing. https://bit.ly/3Cw83i9.Google ScholarGoogle Scholar
  13. Google cloud platform microservices demo. https://github.com/GoogleCloudPlatform/microservices-demo.Google ScholarGoogle Scholar
  14. Horizontal pod autoscaling. https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/.Google ScholarGoogle Scholar
  15. Infection monkey - breach and attack simulation. https://www.akamai.com/infectionmonkey/breach-and-attack-simulation.Google ScholarGoogle Scholar
  16. Intel tofino. https://intel.ly/3wxWT8w.Google ScholarGoogle Scholar
  17. Intel tofino 2. https://intel.ly/3QTeD6F.Google ScholarGoogle Scholar
  18. Logging ip traffic using vpc flow logs. https://docs.aws.amazon.com/vpc/latest/userguide/flow-logs.html.Google ScholarGoogle Scholar
  19. A new walmart 'cloud factory' will accelerate digital innovation, boost business efficiency. https://shorturl.at/amwHI.Google ScholarGoogle Scholar
  20. Palo Alto Networks VM-Series Firewall. https://docs.paloaltonetworks.com/vm-series.Google ScholarGoogle Scholar
  21. tcpdump. http://ee.lbl.gov/tcpdump.tar.Z.Google ScholarGoogle Scholar
  22. Using netflow filtering or sampling to select the network traffic to track. https://rb.gy/83mcu.Google ScholarGoogle Scholar
  23. What is an advanced persistent threat (apt)? https://www.cisco.com/c/en/us/products/security/advanced- persistent- threat.html.Google ScholarGoogle Scholar
  24. Vfp: A virtual switch platform for host sdn in the public cloud. In NSDI, 2017.Google ScholarGoogle Scholar
  25. Azure accelerated networking: Smartnics in the public cloud. In NSDI, 2018.Google ScholarGoogle Scholar
  26. Microsegmentation - global strategic business report. https://www.researchandmarkets.com/report/microsegmentation, 2023.Google ScholarGoogle Scholar
  27. Akamai. Akamai Guardicore Segmentation. https://www.akamai.com/products/akamai-guardicore-segmentation.Google ScholarGoogle Scholar
  28. I. Antonellis, H. G. Molina, and C. C. Chang. Simrank++: Query rewriting through link analysis of the click graph. In VLDB Endowment, 2008.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. P. Bahl, R. Chandra, A. Greenberg, S. Kandula, D. Maltz, and M. Zhang. Towards Highly Reliable Enterprise Network Services via Inference of Multi-level Dependencies. In SIGCOMM, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. J. Bailey and B. Jensen. Walmart and azure. https://shorturl.at/vMTY0.Google ScholarGoogle Scholar
  31. H. Ballani, Y. Chawathe, S. Ratnasamy, T. Roscoe, and S. Shenker. Off by default! In HotNets, 2005.Google ScholarGoogle Scholar
  32. D. Bansal, G. DeGrace, R. Tewari, M. Zygmunt, J. Grantham, S. Gai, M. Baldi, K. Doddapaneni, A. Selvarajan, A. Arumugam, B. Raman, A. Gupta, S. Jain, D. Jagasia, E. Langlais, P. Srivastava, R. Hazarika, N. Motwani, S. Tiwari, S. Grant, R. Chandra, and S. Kandula. Disaggregating stateful network functions. In NSDI, 2023.Google ScholarGoogle Scholar
  33. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  34. P. Bodík, I. Menache, M. Chowdhury, P. Mani, D. A. Maltz, and I. Stoica. Surviving failures in bandwidth-constrained datacenters, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. A. Broder. On the resemblance and containment of documents. In Proceedings of the Compression and Complexity of Sequences 1997. IEEE Computer Society, 1997.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. P. Carbone, A. Katsifodimos, S. Ewen, V. Markl, S. Haridi, et al. Apache Flink: Stream and batch processing in a single engine. In ICDE, 2015.Google ScholarGoogle Scholar
  37. M. Casado, M. J. Freedman, J. Pettit, J. Luo, N. McKeown, and S. Shenker. Ethane: Taking Control of the Enterprise. ACM SIGCOMM Computer Communication Review, 37(4):1, Oct. 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. N. M. M. K. Chowdhury and R. Boutaba. Network Virtualization: State of the Art and Research Challenges. IEEE ComSoc, 2009.Google ScholarGoogle Scholar
  39. Cisco. Cisco Tetration. https://www.cisco.com/c/en_sg/products/data-center-analytics/tetration-analytics/index.html.Google ScholarGoogle Scholar
  40. C. Cranor, Y. Gao, T. Johnson, V. Shkapenyuk, and O. Spatscheck. Gigascope: High performance network monitoring with an sql interface. In SIGMOD, 2002.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. C. Cranor, T. Johnson, O. Spataschek, and V. Shkapenyuk. Gigascope: A stream database for network applications. In SIGMOD, 2003.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. C. Cranor, T. Johnson, O. Spatscheck, and V. Shkapenyuk. The gigascope stream database. IEEE Data Eng. Bull., 2003.Google ScholarGoogle Scholar
  43. M. Dalton et al. Andromeda: Performance, Isolation, and Velocity at Scale in Cloud Network Virtualization. In NSDI, 2018.Google ScholarGoogle Scholar
  44. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding, 2019.Google ScholarGoogle Scholar
  45. O. Ertl. Superminhash -- a new minwise hashing algorithm for jaccard similarity estimation. https://arxiv.org/pdf/1706.05698.pdf.Google ScholarGoogle Scholar
  46. C. Estan, S. Savage, and G. Varghese. Automatically Inferring Patterns of Resource Consumption in Network Traffic. In SIGCOMM, 2003.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. B. Evans. Walmart cio: We picked microsoft for huge cloud deal to accelerate digital transformation. https://shorturl.at/aABI3.Google ScholarGoogle Scholar
  48. A. Gember-Jacobson, R. Viswanathan, C. Prakash, R. Grandl, J. Khalid, S. Das, and A. Akella. Opennf: Enabling innovation in network function control. In SIGCOMM, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. C. Guo, L. Yuan, D. Xiang, Y. Dang, R. Huang, D. Maltz, Z. Liu, V. Wang, B. Pang, H. Chen, Z.-W. Lin, and V. Kurien. Pingmesh: A large-scale system for data center network latency measurement and analysis. In SIGCOMM, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition, 2015.Google ScholarGoogle Scholar
  51. K. Henderson, B. Gallagher, T. Eliassi-Rad, H. Tong, S. Basu, L. Akoglu, D. Koutra, C. Faloutsos, and L. Li. Rolx: Structural role extraction & mining in large graphs. In KDD, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. M. Iliofotou, P. Pappu, M. Faloutsos, M. Mitzenmacher, S. Singh, and G. Varghese. Network monitoring using traffic dispersion graphs (tdgs). In IMC, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Illumio. Zero Trust: the security paradigm for the modern organization. https://www.illumio.com/solutions/zero-trust.Google ScholarGoogle Scholar
  54. G. Jeh and J. Widom. Simrank: A measure of structural-context similarity. In KDD, 2002.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. S. Kandula, R. Chandra, and D. Katabi. What's Going On? Learning Communication Rules in Edge Networks. In SIGCOMM, 2008.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. S. Kandula, S. Sengupta, A. Greenberg, P. Patel, and R. Chaiken. The Nature of Datacenter Traffic: Measurements & Analysis. In IMC, 2009.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. T. Koponen, K. Amidon, P. Balland, M. Casado, A. Chanda, B. Fulton, I. Ganichev, J. Gross, P. Ingram, E. Jackson, A. Lambeth, R. Lenglet, S.-H. Li, A. Padmanabhan, J. Pettit, B. Pfaff, R. Ramanathan, S. Shenker, A. Shieh, J. Stribling, P. Thakkar, D. Wendlandt, A. Yip, and R. Zhang. Network virtualization in multi-tenant datacenters. In NSDI, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. A. Lakhina, M. Crovella, and C. Diot. Mining anomalies using traffic feature distributions. SIGCOMM CCR, 2005.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Y. Li, R. Miao, C. Kim, and M. Yu. Flowradar: A better netflow for data centers. In NSDI, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. D. Lizorkin, P. Velikhov, M. Grinev, and D. Turdakov. Accuracy estimate and optimization techniques for simrank computation. In VLDB Endowment, 2008.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Y. Mirsky, T. Doitshman, Y. Elovici, and A. Shabtai. Kitsune: An ensemble of autoencoders for online network intrusion detection. In NDSS, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  62. J. C. Mogul, D. Goricanec, M. Pool, A. Shaikh, D. Turk, B. Koley, and X. Zhao. Experiences with modeling network topologies at multiple levels of abstraction. In NSDI, 2020.Google ScholarGoogle Scholar
  63. B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social representations. In KDD, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. B. Pfaff, J. Pettit, T. Koponen, E. Jackson, A. Zhou, J. Rajahalme, J. Gross, A. Wang, J. Stringer, P. Shelar, K. Amidon, and M. Casado. The design and implementation of open vSwitch. In NSDI, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. A. Rawashdeh and A. Ralescu. Similarity measure for social networks -- a brief survey. volume 1353, 2015.Google ScholarGoogle Scholar
  66. A. Roy, H. Zeng, J. Bagga, G. Porter, and A. C. Snoeren. Inside the social network's (datacenter) network. In SIGCOMM, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. S. Singh, F. Baboescu, G. Varghese, and J. Wang. Packet Classification Using Multidimensional Cutting. In ACM SIGCOMM 2003.Google ScholarGoogle Scholar
  68. Verizon. 2023 data breach investigations report. https://www.verizon.com/business/resources/reports/dbir/.Google ScholarGoogle Scholar
  69. Verizon. Data breach investigations report: 2008-2022. https://www.verizon.com/business/resources/reports/2022/dbir/2022-data-breach-investigations-report-dbir.pdf.Google ScholarGoogle Scholar
  70. VMWare. VMware NSX. https://www.vmware.com/products/nsx.html.Google ScholarGoogle Scholar
  71. K. Zhao, P. Goyal, M. Alizadeh, and T. E. Anderson. Scalable tail latency estimation for data center networks. In NSDI, 2023.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Conferences
    HotNets '23: Proceedings of the 22nd ACM Workshop on Hot Topics in Networks
    November 2023
    306 pages
    ISBN:9798400704154
    DOI:10.1145/3626111

    Copyright © 2023 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 28 November 2023

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate110of460submissions,24%
  • Article Metrics

    • Downloads (Last 12 months)50
    • Downloads (Last 6 weeks)9

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader