A Hierarchical Approach to Encrypted Data Packet Classification in Smart Home Gateways

TL;DR

This paper introduces a hierarchical encrypted packet classification scheme for smart home gateways that enhances end-to-end QoS measurement and supports privacy-preserving network management using deep learning.

Contribution

It proposes a novel two-level hierarchical classification approach combining real-time deep learning and high-accuracy non-real-time classifiers for encrypted data in smart homes.

Findings

Real-time classifier achieves high accuracy with fast processing.

Hierarchical scheme improves end-to-end QoS measurement.

Supports privacy-preserving network management.

Abstract

With the pervasive network based services in smart homes, traditional network management cannot guarantee end-user quality-of-experience (QoE) for all applications. End-user QoE must be supported by efficient network quality-of-service (QoS) measurement and efficient network resource allocation. With the software-defined network technology, the core network may be controlled more efficiently by a network service provider. However, end-to-end network QoS can hardly be improved the managing the core network only. In this paper, we propose an encrypted packet classification scheme for smart home gateways to improve end-to-end QoS measurement from the network operator side. Furthermore, other services such as statistical data collecting, billing to service providers, etc., can be provided without compromising end-user privacy nor security of a network. The proposed encrypted packet classification scheme has a two-level hierarchical structure. One is the service level, which is based on applications that have the same network QoS requirements. A faster classification scheme based on deep learning is proposed to achieve real-time classification with high accuracy. The other one is the application level, which is based on fine-grained applications. A non-real-time classifier can be applied to provide high accuracy. Evaluation is conducted on both level classifiers to demonstrate the efficiency and accuracy of the two types of classifiers.