Android malware detection serves as the front line against malicious apps.
With the rapid advancement of machine learning (ML), ML-based Android malware
detection has attracted increasing attention due to its capability of
automatically capturing malicious patterns from Android APKs. These
learning-driven methods have reported promising results in detecting malware.
However, the absence of an in-depth analysis of current research progress makes
it difficult to gain a holistic picture of the state of the art in this area.
This paper presents a comprehensive investigation to date into ML-based
Android malware detection with empirical and quantitative analysis. We first
survey the literature, categorizing contributions into a taxonomy based on the
Android feature engineering and ML modeling pipeline. Then, we design a
general-propose framework for ML-based Android malware detection, re-implement
12 representative approaches from different research communities, and evaluate
them from three primary dimensions, i.e., effectiveness, robustness, and
efficiency. The evaluation reveals that ML-based approaches still face open
challenges and provides insightful findings like more powerful ML models are
not the silver bullet for designing better malware detectors. We further
summarize our findings and put forth recommendations to guide future research.

机器学习方法对于安卓恶意软件检测起到了关键作用，而本文则通过实证分析全面调查了基于机器学习的安卓恶意软件检测的研究进展，并总结出一些建议以引导未来的研究。

揭示安卓恶意软件检测中的机器学习解决方案关键

Unraveling the Key of Machine Learning Solutions for Android Malware  Detection

In this article, we provide an overview of machine learning (ML) methods,
both classical and deep variants, that are used to implement self-healing for
cell outages in cellular networks. Self-healing is a promising approach to
network management, which aims to detect and compensate for cell outages in an
autonomous way. This technology aims to decrease the expenses associated with
the installation and maintenance of existing 4G and 5G, i.e. emerging 6G
networks by simplifying operational tasks through its ability to heal itself.
We provide an overview of the basic concepts and taxonomy for SON,
self-healing, and ML techniques, in network management. Moreover, we review the
state-of-the-art in literature for cell outages, with a particular emphasis on
ML-based approaches.

该研究概述了机器学习方法在自愈网络中实现蜂窝网络中的小区中断的概念和分类，介绍了自愈网络管理和基于机器学习的方法在细胞中断方面的研究现状。