Mitigando Ataques com a Orquestração de VNFs Baseadas em Contêineres Usando Aprendizado Supervisionado
Resumo
A virtualização de funções de rede (Network Function Virtualization NFV) desacopla as funções de rede dos dispositivos físicos, simplificando a implantação de novos serviços. A resiliência é o que possibilita às VNFs lidarem com possíveis problemas, adaptando-as a mudanças através de respostas sensíveis e imediatas a determinadas alterações. Neste artigo é proposto um mecanismo, chamado Intel-OCNF, que através do uso de aprendizado supervisionado permite identificar quais funções de rede devem ser instanciadas com base em dados de monitoramento, de forma a assegurar a mitigação de ataques em rede. O protótipo desenvolvido foi integrado ao orquestrador NFVO, e opera de forma automatizada e sem dependência de ações do operador de rede.
Referências
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Pattaranantakul, M. et al. Secmano: Towards network functions virtualization (nfv) based security management and orchestration. In: 2016 IEEE Trustcom/BigDataSE/ISPA. [S.l.: s.n.], 2016. p. 598–605. PAUNOVÍC, M. et al. Two-stage fuzzy logic model for cloud service supplier selection and evaluation. Mathematical Problems in Engineering, Hindawi, v. 2018, 2018.
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Salman, T. et al. Machine learning for anomaly detection and categorization in multi-cloud environments. In: 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud). [S.l.: s.n.], 2017. p. 97–103.
Saraiva de Sousa, N. F. et al. Network service orchestration: A survey. Computer Communications, v. 142-143, p. 69–94, 2019. ISSN 0140-3664. Disponível em: (cid:104) [link] (cid:105) .
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Schardong, F.; Nunes, I.; Schaeffer-Filho, A. Providing cognitive components with a bidding heuristic for emergent nfv orchestration. In: NOMS 2018 2018 IEEE/IFIP Network Operations and Management Symposium. [S.l.: s.n.], 2018. p. 1–9. ISSN 2374-9709.
Schardong, F.; Nunes, I.; Schaeffer-Filho, A. NFV resource allocation: a systematic review and taxonomy of VNF forwarding graph embedding. Computer Networks, v. 185, p. 107726, 2021. ISSN 1389-1286.
SEGAL, M. R. Machine learning benchmarks and random forest regression. 2004.
SHARAFALDIN, I.; LASHKARI, A. H.; GHORBANI, A. A. Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP. [S.l.: s.n.], 2018. p. 108–116.
Zhang, X. et al. Proactive vnf provisioning with multi-timescale cloud resources: Fusing online learning and online optimization. In: IEEE INFOCOM 2017 IEEE Conference on Computer Communications. [S.l.: s.n.], 2017. p. 1–9.
Zhou, L.; Guo, H. Applying nfv/sdn in mitigating ddos attacks. In: TENCON 2017 2017 IEEE Region 10 Conference. [S.l.: s.n.], 2017. p. 2061–2066.
Bhuyan, M. H.; Bhattacharyya, D. K.; Kalita, J. K. Network anomaly detection: Methods, systems and tools. IEEE Communications Surveys Tutorials, v. 16, n. 1, p. 303–336, 2014.
Bondan, L. et al. Fende: Marketplace-based distribution, execution, and life cycle management of vnfs. IEEE Communications Magazine, v. 57, n. 1, p. 13–19, 2019.
Chatras, B. On the standardization of nfv management and orchestration apis. IEEE Communications Standards Magazine, v. 2, n. 4, p. 66–71, 2018.
Dominicini, C. K. et al. Virtphy: Fully programmable nfv orchestration architecture for edge data centers. IEEE Transactions on Network and Service Management, v. 14, n. 4, p. 817–830, 2017.
ETSI, G. Network functions virtualisation (nfv): Architectural framework. ETsI Gs NFV, v. 2, n. 2, p. V1, 2013.
KOSTAS, K. Anomaly detection in networks using machine learning. Research Proposal, v. 23, 2018.
KRAWCZYK, B. et al. Ensemble learning for data stream analysis: A survey. Information Fusion, v. 37, p. 132–156, 2017. ISSN 1566-2535. Disponível em: (cid:104) [link] (cid:105).
LI, Y.; LI, T.; LIU, H. Recent advances in feature selection and its applications. Knowledge and Information Systems, Springer, v. 53, n. 3, p. 551–577, 2017.
Mijumbi, R. et al. Network function virtualization: State-of-the-art and research challenges. IEEE Communications Surveys Tutorials, v. 18, n. 1, p. 236–262, Firstquarter 2016.
MODI, C. et al. A survey of intrusion detection techniques in cloud. Journal of Network and Computer Applications, v. 36, n. 1, p. 42 – 57, 2013. ISSN 1084-8045.
Pattaranantakul, M. et al. Secmano: Towards network functions virtualization (nfv) based security management and orchestration. In: 2016 IEEE Trustcom/BigDataSE/ISPA. [S.l.: s.n.], 2016. p. 598–605. PAUNOVÍC, M. et al. Two-stage fuzzy logic model for cloud service supplier selection and evaluation. Mathematical Problems in Engineering, Hindawi, v. 2018, 2018.
Peuster, M.; Karl, H.; van Rossem, S. Medicine: Rapid prototyping of production-ready network services in multi-pop environments. In: 2016 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). [S.l.: s.n.], 2016. p. 148–153.
Salman, T. et al. Machine learning for anomaly detection and categorization in multi-cloud environments. In: 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud). [S.l.: s.n.], 2017. p. 97–103.
Saraiva de Sousa, N. F. et al. Network service orchestration: A survey. Computer Communications, v. 142-143, p. 69–94, 2019. ISSN 0140-3664. Disponível em: (cid:104) [link] (cid:105) .
Saravanan, R.; Sujatha, P. A state of art techniques on machine learning algorithms: A perspective of supervised learning approaches in data classification. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). [S.l.: s.n.], 2018. p. 945–949.
Schardong, F.; Nunes, I.; Schaeffer-Filho, A. Providing cognitive components with a bidding heuristic for emergent nfv orchestration. In: NOMS 2018 2018 IEEE/IFIP Network Operations and Management Symposium. [S.l.: s.n.], 2018. p. 1–9. ISSN 2374-9709.
Schardong, F.; Nunes, I.; Schaeffer-Filho, A. NFV resource allocation: a systematic review and taxonomy of VNF forwarding graph embedding. Computer Networks, v. 185, p. 107726, 2021. ISSN 1389-1286.
SEGAL, M. R. Machine learning benchmarks and random forest regression. 2004.
SHARAFALDIN, I.; LASHKARI, A. H.; GHORBANI, A. A. Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP. [S.l.: s.n.], 2018. p. 108–116.
Zhang, X. et al. Proactive vnf provisioning with multi-timescale cloud resources: Fusing online learning and online optimization. In: IEEE INFOCOM 2017 IEEE Conference on Computer Communications. [S.l.: s.n.], 2017. p. 1–9.
Zhou, L.; Guo, H. Applying nfv/sdn in mitigating ddos attacks. In: TENCON 2017 2017 IEEE Region 10 Conference. [S.l.: s.n.], 2017. p. 2061–2066.
Publicado
16/08/2021
Como Citar
SILVA, Fernando; SCHAEFFER-FILHO, Alberto E..
Mitigando Ataques com a Orquestração de VNFs Baseadas em Contêineres Usando Aprendizado Supervisionado. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 39. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 350-363.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2021.16732.
