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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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8879591/ |
| _version_ | 1818667670276407296 |
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| author | Fan Liang William Grant Hatcher Weixian Liao Weichao Gao Wei Yu |
| author_facet | Fan Liang William Grant Hatcher Weixian Liao Weichao Gao Wei Yu |
| author_sort | Fan Liang |
| collection | DOAJ |
| 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 benefits (good use) that machine learning has brought, both in general, and specifically 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. |
| first_indexed | 2024-12-17T06:24:07Z |
| format | Article |
| id | doaj.art-919e8b4e5daa4ff1a095a5a47c99ab2f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T06:24:07Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-919e8b4e5daa4ff1a095a5a47c99ab2f2022-12-21T22:00:20ZengIEEEIEEE Access2169-35362019-01-01715812615814710.1109/ACCESS.2019.29489128879591Machine Learning for Security and the Internet of Things: The Good, the Bad, and the UglyFan Liang0William Grant Hatcher1Weixian Liao2Weichao Gao3Wei Yu4https://orcid.org/0000-0003-4522-7340Department of Computer and Information Sciences, Towson University, Towson, MD, USADepartment of Computer and Information Sciences, Towson University, Towson, MD, USADepartment of Computer and Information Sciences, Towson University, Towson, MD, USADepartment of Computer and Information Sciences, Towson University, Towson, MD, USADepartment of Computer and Information Sciences, Towson University, Towson, MD, USAThe 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 benefits (good use) that machine learning has brought, both in general, and specifically 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.https://ieeexplore.ieee.org/document/8879591/Securitymachine learningcyber physical systemsInternet of Thingsapplicationsdistributed environments |
| spellingShingle | Fan Liang William Grant Hatcher Weixian Liao Weichao Gao Wei Yu Machine Learning for Security and the Internet of Things: The Good, the Bad, and the Ugly IEEE Access Security machine learning cyber physical systems Internet of Things applications distributed environments |
| title | 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_short | 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 |
| topic | Security machine learning cyber physical systems Internet of Things applications distributed environments |
| url | https://ieeexplore.ieee.org/document/8879591/ |
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