• 时间:: 2023
• 作者:: Madumitha Venkatasubramanian, Arash Habibi Lashkari, Saqib Hakak

• URL:: IoT Malware Analysis Using Federated Learning: A Comprehensive Survey | IEEE Journals & Magazine | IEEE Xplore
• DOI:: 10.1109/ACCESS.2023.3235389

• pdf:: IoT Malware Analysis Using Federated Learning_2023_Venkatasubramanian et al
• zotero链接:: IoT Malware Analysis Using Federated Learning_2023_Venkatasubramanian et al.pdf
• 标签:: #物联网/恶意代码检测 #机器学习/联邦学习 #机器学习/深度学习 #综述

这也是一篇综述，原本找这篇来看的时候没发现这是综述，只是觉得这个联邦学习挺牛逼的
Comprehensive：全面的

没看没看没看没看没看没看没看没看没看没看没看没看我不想看我不想看不想看

ABSTRACT：The Internet of Things (IoT) has paved the way to a highly connected society where all things are interconnected and exchanging information has become more accessible through the internet. With the use of IoT devices, the threat of malware has increased rapidly. The increased number of existing and new malware variants has made protecting IoT devices and networks challenging. The malware can hide in the systems and disables its activity when there are attempts to discover and detect them. With technological advances, there are various emerging techniques to address this problem. However, they still encounter issues concerning the privacy and security of the user’s data and suffer from a single point of failure. To address this issue, there are recent research developments conducted to use Federated Learning (FL). FL is a decentralized technique that trains the user’s data on-device and exchanges the parameters without sharing the user’s data. FL is implemented to secure the user’s data, provide safe and accurate models, and prevent the single point of failure in the centralized models. This paper provides an overview of different approaches that integrate FL with IoT. Finally, we discuss the applications of FL, the research challenges, and future research directions.
摘要（有道翻译）：物联网(IoT)为高度互联的社会铺平了道路，在这个社会中，所有事物都是相互联系的，通过互联网交换信息变得更加容易。随着物联网设备的使用，恶意软件的威胁迅速增加。现有和新的恶意软件变体数量的增加使得保护物联网设备和网络变得具有挑战性。恶意软件可以隐藏在系统中，并在有人试图发现和检测它们时禁用其活动。随着技术的进步，有各种新兴的技术来解决这个问题。然而，他们仍然会遇到用户数据的隐私和安全问题，并遭受单点故障的困扰。为了解决这个问题，最近进行了使用联邦学习(FL)的研究进展。FL是一种分散的技术，它在设备上训练用户的数据，并在不共享用户数据的情况下交换参数。为了保护用户的数据，提供安全准确的模型，防止集中式模型出现单点故障，实现了FL。本文概述了将FL与物联网集成的不同方法。最后，我们讨论了FL的应用、研究面临的挑战和未来的研究方向。

看了下摘要，没看了，现在要找论文来看