Skip to main content

FEVER: Intelligent Behavioral Fingerprinting for Anomaly Detection in P4-Based Programmable Networks

  • Conference paper
  • First Online:
Advanced Information Networking and Applications (AINA 2024)


The evolving computer network landscape has enabled programmability in various network aspects, including Software-defined Networking (SDN) for control plane programmability and the introduction of the Programming Protocol-independent Packet Processors (P4). P4, a vendor-independent protocol, allows programmability on the data plane, offering flexibility for new services and applications. However, this flexibility introduces the need for automated solutions to monitor and manage the security of evolving networks and services. In this work, we propose FEVER, a framework utilizing P4-based telemetry and network device (switch) resource consumption to create fingerprints of network and P4 application behaviors. FEVER provides a comprehensive approach to identifying network anomalies through various metrics. The framework was evaluated in a virtualized scenario using unsupervised Machine Learning (ML) algorithms to detect diverse P4 program behaviors and traffic overload, demonstrating its potential for early detection of malicious activities in programmable networks. The results indicate high accuracy in identifying misbehavior and detecting sudden changes in P4 programs affecting the network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


  1. Badotra, S., Panda, S.N.: Software-defined networking: a novel approach to networks. In: Gupta, B., Perez, G., Agrawal, D., Gupta, D. (eds.) Handbook of Computer Networks and Cyber Security: Principles and Paradigms, pp. 313–339. Springer, Cham (2020).

    Chapter  Google Scholar 

  2. Bai, S., Kim, H., Rexford, J.: Passive OS fingerprinting on commodity switches. In: IEEE 8th International Conference on Network Softwarization (NetSoft), pp. 264–268 (2022)

    Google Scholar 

  3. Bondan, L., et al.: FENDE: marketplace-based distribution, execution, and life cycle management of VNFs. IEEE Commun. Mag. 57(1), 13–19 (2019)

    Article  Google Scholar 

  4. Bosshart, P., et al.: P4: programming protocol-independent packet processors. SIGCOMM Comput. Commun. Rev. 44(3), 87–95 (2014)

    Article  Google Scholar 

  5. Ding, D., Savi, M., Siracusa, D.: Tracking normalized network traffic entropy to detect DDoS attacks in P4. Trans. Dependable Secure Comput. 19(6), 4019–4031 (2021)

    Article  Google Scholar 

  6. Dumitrescu, D., Stoenescu, R., Negreanu, L., Raiciu, C.: BF4: towards bug-free P4 programs. In: SIGCOMM 2020, Virtually, USA, pp. 571–585 (2020)

    Google Scholar 

  7. Goswami, B., Kulkarni, M., Paulose, J.: A survey on P4 challenges in software defined networks: P4 programming. IEEE Access 11, 54373–54387 (2023)

    Article  Google Scholar 

  8. Hauser, F., et al.: A survey on data plane programming with P4: fundamentals, advances, and applied research. J. Netw. Comput. Appl. 212, 103561 (2023)

    Article  Google Scholar 

  9. Li, G., et al.: NETHCF: enabling line-rate and adaptive spoofed IP traffic filtering. In: IEEE 27th International Conference on Network Protocols (ICNP 2019), Chicago, USA, pp. 1–12 (2019)

    Google Scholar 

  10. Saueressig, M., Franco, M.F.: FEVER-P4 repository (2024).

  11. Saueressig, M., Franco, M.F., Scheid, E.J., Granville, L.Z.: An approach for behavioral fingerprinting of P4 programmable switches. In: XX Escola Regional de Redes de Computadores (ERRC 2023), Porto Alegre, Brazil, pp. 22–60 (2023)

    Google Scholar 

  12. Musumeci, F., Ionata, V., Paolucci, F., Cugini, F., Tornatore, M.: Machine-learning-assisted DDoS attack detection with P4 language. In: IEEE International Conference on Communications (ICC 2020), Virtually, pp. 1–6 (2020)

    Google Scholar 

  13. Nunes, B.A.A., Mendonca, M., Nguyen, X.N., Obraczka, K., Turletti, T.: A survey of software-defined networking: past, present, and future of programmable networks. IEEE Commun. Surv. Tutor. 16(3), 1617–1634 (2014)

    Article  Google Scholar 

  14. Open Networking Foundation: P4Language (2023).

  15. Sánchez, P.M.S., Valero, J.M.J., Celdrán, A.H., Bovet, G., Pérez, M.G., Pérez, G.M.: A survey on device behavior fingerprinting: data sources, techniques, application scenarios, and datasets. IEEE Commun. Surv. Tutor. 23(2), 1048–1077 (2021)

    Article  Google Scholar 

  16. Tan, L., et al.: In-band network telemetry: a survey. Comput. Netw. 186, 107763 (2021)

    Article  Google Scholar 

  17. Teng, L., Hung, C.H., Wen, C.H.P.: P4SF: a high-performance stateful firewall on commodity P4-programmable switch. In: IEEE/IFIP Network Operations and Management Symposium (NOMS 2022), Budapest, Hungary, pp. 1–5 (2022)

    Google Scholar 

  18. Usama, M., et al.: Unsupervised machine learning for networking: techniques, applications and research challenges. IEEE Access 7, 65579–65615 (2019)

    Article  Google Scholar 

  19. Wang, Q., Pan, M., Wang, S., Doenges, R., Beringer, L., Appel, A.W.: Foundational verification of stateful P4 packet processing. In: 14th International Conference on Interactive Theorem Proving (ITP 2023). Schloss-Dagstuhl-Leibniz Zentrum für Informatik, pp. 1–32 (2023)

    Google Scholar 

Download references


This work was supported by The São Paulo Research Foundation (FAPESP) under the grant number 2020/05152-7, the PROFISSA project.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Muriel Figueredo Franco .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saueressig, M. et al. (2024). FEVER: Intelligent Behavioral Fingerprinting for Anomaly Detection in P4-Based Programmable Networks. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 201. Springer, Cham.

Download citation

Publish with us

Policies and ethics