Abstract

With the evolution of Internet infrastructure and network services, multilayer in-band network telemetry (ML-INT) and data analytics (DA) have been considered as key enabling techniques to realize real-time and fine-grained network monitoring, especially for backbone IP-over-Optical networks. However, the existing ML-INT&DA systems have privacy and security issues, because plaintext ML-INT data is reported from the data plane and gets analyzed in the control plane. In this work, we address these issues by designing a privacy-preserving ML-INT&DA system for IP-over-Optical networks. We first leverage vector homomorphic encryption (VHE) to design a lightweight encryption scheme, which overcomes the security breaches due to eavesdropping and preserves the delicate correlations buried in multi-dimensional ML-INT data. Then, we develop an effective data compression scheme to further encode the encrypted ML-INT data and make the results suitable for hash-based signature. The signature is for data certification and enables the DA in the control plane to verify the integrity of received ML-INT data. Hence, the threats from data tampering are removed. Next, we architect a deep learning (DL) model that can directly operate on encrypted ML-INT data for anomaly detection. Finally, we implement the proposed ML-INT&DA system, and experimentally demonstrate its effectiveness in a real IP over elastic optical network (IP-over-EON) testbed, whose key elements, i.e., optical line system (OLS), bandwidth-variable wavelength-selective switches (BV-WSS’) and programmable data plane (PDP) switches, are all commercial products.

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