Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly

The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneousl...

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Tác giả chính:	Liang, F.
Đồng tác giả:	Hatcher, W. G.
Định dạng:	BB
Ngôn ngữ:	en_US
Thông tin xuất bản:	IEEE Xplore 2020
Chủ đề:	Machine learning cyber physical systems Internet of Things distributed environments
Truy cập trực tuyến:	http://tailieuso.tlu.edu.vn/handle/DHTL/9864
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spelling	oai:localhost:DHTL-98642020-12-08T03:02:53Z Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly Liang, F. Hatcher, W. G. Liao, W. Gao, W. Yu, W. Machine learning cyber physical systems Internet of Things distributed environments The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneously, dramatic improvements in machine learning and deep neural network architectures have enabled unprecedented analytical capabilities, which we see in increasingly common applications and production technologies, such as self-driving vehicles and intelligent mobile applications. Predictably, these technologies have seen rapid adoption, which has left many implementations vulnerable to threats unforeseen or undefended against. Moreover, such technologies can be used by malicious actors, and the potential for cyber threats, attacks, intrusions, and obfuscation that are only just being considered, applied, and countered. In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT. In detail, we consider the numerous bene ts (good use) that machine learning has brought, both in general, and speci cally for security and CPS/IoT, such as the improvement of intrusion detection mechanisms and decision accuracy in CPS/IoT. More pressing, we consider the vulnerabilities of machine learning (bad use) from the perspectives of security and CPS/IoT, including the ways in which machine learning systems can be compromised, misled, and subverted at all stages of the machine learning life-cycle (data collection, pre-processing, training, validation, implementation, etc.). Finally, the most concerning, a growing trend has been the utilization of machine learning in the execution of cyberattacks and intrusions (ugly use). Thus, we consider existing mechanisms with the potential to improve target acquisition and existing threat patterns, as well as those that can enable novel attacks yet to be seen. 2020-12-08T03:01:20Z 2020-12-08T03:01:20Z 2019 BB http://tailieuso.tlu.edu.vn/handle/DHTL/9864 en_US IEEE Access, (2019), Vol 7, pp 158126-158147 application/pdf IEEE Xplore
institution	Trường Đại học Thủy Lợi
collection	DSpace
language	en_US
topic	Machine learning cyber physical systems Internet of Things distributed environments
spellingShingle	Machine learning cyber physical systems Internet of Things distributed environments Liang, F. Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly
description	The advancement of the Internet of Things (IoT) has allowed for unprecedented data collection, automation, and remote sensing and actuation, transforming autonomous systems and bringing smart command and control into numerous cyber physical systems (CPS) that our daily lives depend on. Simultaneously, dramatic improvements in machine learning and deep neural network architectures have enabled unprecedented analytical capabilities, which we see in increasingly common applications and production technologies, such as self-driving vehicles and intelligent mobile applications. Predictably, these technologies have seen rapid adoption, which has left many implementations vulnerable to threats unforeseen or undefended against. Moreover, such technologies can be used by malicious actors, and the potential for cyber threats, attacks, intrusions, and obfuscation that are only just being considered, applied, and countered. In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT. In detail, we consider the numerous bene ts (good use) that machine learning has brought, both in general, and speci cally for security and CPS/IoT, such as the improvement of intrusion detection mechanisms and decision accuracy in CPS/IoT. More pressing, we consider the vulnerabilities of machine learning (bad use) from the perspectives of security and CPS/IoT, including the ways in which machine learning systems can be compromised, misled, and subverted at all stages of the machine learning life-cycle (data collection, pre-processing, training, validation, implementation, etc.). Finally, the most concerning, a growing trend has been the utilization of machine learning in the execution of cyberattacks and intrusions (ugly use). Thus, we consider existing mechanisms with the potential to improve target acquisition and existing threat patterns, as well as those that can enable novel attacks yet to be seen.
author2	Hatcher, W. G.
author_facet	Hatcher, W. G. Liang, F.
format	BB
author	Liang, F.
author_sort	Liang, F.
title	Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly
title_short	Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly
title_full	Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly
title_fullStr	Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly
title_full_unstemmed	Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly
title_sort	machine learning for security and the internet of things: the good, the bad, and the ugly
publisher	IEEE Xplore
publishDate	2020
url	http://tailieuso.tlu.edu.vn/handle/DHTL/9864
work_keys_str_mv	AT liangf machinelearningforsecurityandtheinternetofthingsthegoodthebadandtheugly
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Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly

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