Study of Machine Learning for Cloud Assisted IoT Security as a Service
Machine learning (ML) has been emerging as a viable solution for intrusion detection systems (IDS) to secure IoT devices against different types of attacks. ML based IDS (ML-IDS) normally detect network traffic anomalies caused by known attacks as well as newly introduced attacks. Recent research fo...
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doaj-c69302250e0b455b8f59913d0b1c0c402021-02-04T00:04:49ZengMDPI AGSensors1424-82202021-02-01211034103410.3390/s21041034Study of Machine Learning for Cloud Assisted IoT Security as a ServiceMaram Alsharif0Danda B. Rawat1Data Science and Cybersecurity Center, Howard University, Washington, DC 20059, USAData Science and Cybersecurity Center, Howard University, Washington, DC 20059, USAMachine learning (ML) has been emerging as a viable solution for intrusion detection systems (IDS) to secure IoT devices against different types of attacks. ML based IDS (ML-IDS) normally detect network traffic anomalies caused by known attacks as well as newly introduced attacks. Recent research focuses on the functionality metrics of ML techniques, depicting their prediction effectiveness, but overlooked their operational requirements. ML techniques are resource-demanding that require careful adaptation to fit the limited computing resources of a large sector of their operational platform, namely, embedded systems. In this paper, we propose cloud-based service architecture for managing ML models that best fit different IoT device operational configurations for security. An IoT device may benefit from such a service by offloading to the cloud heavy-weight activities such as feature selection, model building, training, and validation, thus reducing its IDS maintenance workload at the IoT device and get the security model back from the cloud as a service.https://www.mdpi.com/1424-8220/21/4/1034machine learningcloud assisted IoT security as a service |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Maram Alsharif Danda B. Rawat |
spellingShingle |
Maram Alsharif Danda B. Rawat Study of Machine Learning for Cloud Assisted IoT Security as a Service Sensors machine learning cloud assisted IoT security as a service |
author_facet |
Maram Alsharif Danda B. Rawat |
author_sort |
Maram Alsharif |
title |
Study of Machine Learning for Cloud Assisted IoT Security as a Service |
title_short |
Study of Machine Learning for Cloud Assisted IoT Security as a Service |
title_full |
Study of Machine Learning for Cloud Assisted IoT Security as a Service |
title_fullStr |
Study of Machine Learning for Cloud Assisted IoT Security as a Service |
title_full_unstemmed |
Study of Machine Learning for Cloud Assisted IoT Security as a Service |
title_sort |
study of machine learning for cloud assisted iot security as a service |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-02-01 |
description |
Machine learning (ML) has been emerging as a viable solution for intrusion detection systems (IDS) to secure IoT devices against different types of attacks. ML based IDS (ML-IDS) normally detect network traffic anomalies caused by known attacks as well as newly introduced attacks. Recent research focuses on the functionality metrics of ML techniques, depicting their prediction effectiveness, but overlooked their operational requirements. ML techniques are resource-demanding that require careful adaptation to fit the limited computing resources of a large sector of their operational platform, namely, embedded systems. In this paper, we propose cloud-based service architecture for managing ML models that best fit different IoT device operational configurations for security. An IoT device may benefit from such a service by offloading to the cloud heavy-weight activities such as feature selection, model building, training, and validation, thus reducing its IDS maintenance workload at the IoT device and get the security model back from the cloud as a service. |
topic |
machine learning cloud assisted IoT security as a service |
url |
https://www.mdpi.com/1424-8220/21/4/1034 |
work_keys_str_mv |
AT maramalsharif studyofmachinelearningforcloudassistediotsecurityasaservice AT dandabrawat studyofmachinelearningforcloudassistediotsecurityasaservice |
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