Many sensitive applications require that data remain confidential and undisclosed, even for intrusion detection objectives. For this purpose, the detection of anomalies in encrypted data has become increasingly vital. Deep learning models are becoming good tools to detect anomalies in encrypted data without the need to pass through data decryption. This paper presents a systematic review focusing on the advancements made in deep learning models for intrusion detection over encrypted data with privacy preservation. This study aims to guide researchers on how to select the right tools to set up an intrusion detection system over encrypted data with privacy preservation. The study presented the context and challenges of intrusion detection on encrypted data and how machine learning-based solutions can circumvent these challenges. The paper looks at recently proposed solutions, examines metrics for assessing model performance, and evaluates frequently used reference datasets. Deep learning models are also evaluated with statistics on the most frequent models, datasets, and encryption tools. The performance metrics of the studied solutions are investigated as a function of the encryption tools, the deployed deep learning models, the privacy preservation tools, the deployed datasets, and the eventual additional tools and algorithms. Our recommendations help researchers evaluate their proposals for preserving privacy and detecting intrusions on encrypted data using deep learning techniques.

许多敏感应用程序要求数据保密且不公开，即使是出于入侵检测目的。为此，检测加密数据中的异常变得越来越重要。深度学习模型正在成为检测加密数据异常的良好工具，而无需通过数据解密。本文系统地回顾了深度学习模型在加密数据入侵检测和隐私保护方面取得的进展。本研究旨在指导研究人员如何选择正确的工具来建立具有隐私保护的加密数据入侵检测系统。该研究介绍了加密数据入侵检测的背景和挑战，以及基于机器学习的解决方案如何规避这些挑战。本文着眼于最近提出的解决方案，检查评估模型性能的指标，并评估常用的参考数据集。深度学习模型还通过最常用模型、数据集和加密工具的统计数据进行评估。研究的解决方案的性能指标作为加密工具、部署的深度学习模型、隐私保护工具、部署的数据集以及最终的附加工具和算法的函数进行了研究。我们的建议帮助研究人员评估他们使用深度学习技术保护隐私和检测加密数据入侵的建议。