| Title | Privacy-Preserving Multilayer In-Band Network Telemetry and Data Analytics |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Pan, Xiaoqin, Tang, Shaofei, Zhu, Zuqing |
| Conference Name | 2020 IEEE/CIC International Conference on Communications in China (ICCC) |
| Keywords | Collaboration, composability, Data analysis, Data models, deep learning (dl), Encryption, In-band network telemetry (INT), Integer vector homomorphic encryption (IVHE), ip privacy, Monitoring, Nonhomogeneous media, policy-based governance, Privacy-preserving network monitoring, Proposals, pubcrawl, resilience, Resiliency, soft failures, telemetry |
| Abstract | As a new paradigm for the monitoring and troubleshooting of backbone networks, the multilayer in-band network telemetry (ML-INT) with deep learning (DL) based data analytics (DA) has recently been proven to be effective on realtime visualization and fine-grained monitoring. However, the existing studies on ML-INT&DA systems have overlooked the privacy and security issues, i.e., a malicious party can apply tapping in the data reporting channels between the data and control planes to illegally obtain plaintext ML-INT data in them. In this paper, we discuss a privacy-preserving DL-based ML-INT&DA system for realizing AI-assisted network automation in backbone networks in the form of IP-over-Optical. We first show a lightweight encryption scheme based on integer vector homomorphic encryption (IVHE), which is used to encrypt plaintext ML-INT data. Then, we architect a DL model for anomaly detection, which can directly analyze the ciphertext ML-INT data. Finally, we present the implementation and experimental demonstrations of the proposed system. The privacy-preserving DL-based ML-INT&DA system is realized in a real IP over elastic optical network (IP-over-EON) testbed, and the experimental results verify the feasibility and effectiveness of our proposal. |
| DOI | 10.1109/ICCC49849.2020.9238883 |
| Citation Key | pan_privacy-preserving_2020 |
Privacy-Preserving Multilayer In-Band Network Telemetry and Data Analytics