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2014 7th International Conference on Information and Automation for Sustainability (ICIAfS)
01 Jan 2014-
About: The article was published on 2014-01-01 and is currently open access. It has received 3 citations till now. The article focuses on the topics: Sustainability.
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27 Oct 2020
TL;DR: In this paper, the authors developed and evaluated a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in the wild, showing that millions of IoT devices are detectable and identifiable within hours, both at a major ISP and an IXP, using passive, sparsely sampled network flow headers.
Abstract: Consumer Internet of Things (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. These functionalities often come with significant privacy and security risks, with notable recent large-scale coordinated global attacks disrupting large service providers. Thus, an important first step to address these risks is to know what IoT devices are where in a network. While some limited solutions exist, a key question is whether device discovery can be done by Internet service providers that only see sampled flow statistics. In particular, it is challenging for an ISP to efficiently and effectively track and trace activity from IoT devices deployed by its millions of subscribers---all with sampled network data.In this paper, we develop and evaluate a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in-the-wild. Our findings indicate that millions of IoT devices are detectable and identifiable within hours, both at a major ISP as well as an IXP, using passive, sparsely sampled network flow headers. Our methodology is able to detect devices from more than 77% of the studied IoT manufacturers, including popular devices such as smart speakers. While our methodology is effective for providing network analytics, it also highlights significant privacy consequences.
26 citations
TL;DR: In this paper , the authors present a comprehensive systematic mapping review that focused on the application of the machine learning approach for the mitigation of security threats and attacks in the BYOD environment by highlighting the current trends in the existing studies.
Abstract: Bring your own device (BYOD) paradigm that permits employees to come with their own mobile devices to join the organizational network is rapidly changing the organizational operation method by enhancing flexibility, productivity, and efficiency. Despite these benefits, security issues remain a concern in organizational settings. A considerable number of studies have been conducted and published in this domain without a detailed review of the security solution mechanisms. Moreover, some reviews conducted focused more on conventional approaches such as mobile content management, and application content management. Hence, the implementation of security in BYOD using the conventional method is ineffective. Thus, machine learning approaches seem to be the promising approach, which provides a solution to the security problem in the BYOD environment. This study presents a comprehensive systematic mapping review that focused on the application of the machine learning approach for the mitigation of security threats and attacks in the BYOD environment by highlighting the current trends in the existing studies. Five academic databases were searched and a total of 753 of the primary studies published between 2012 and 2021 were initially retrieved. These studies were screened based on their title, abstract and full text to check their eligibility and relevance for the study. However, forty primary studies were included and analyzed in the systematic mapping review (SMR). Based on the analysis and bubble plot mapping, significant research trends were identified on security threats and attacks, machine learning approaches, datasets usage, and evaluation metrics. The SMR result demonstrates the rise in the number of investigations regarding malware and unauthorized access to existing security threats and attacks. The SMR study indicates that supervised learning approaches such as SVM, DT, and RF are the most employed learning model by the previous research. Thus, there is an open research issue in the application of unsupervised learning approaches such as clustering and deep learning approaches. Therefore, the SMR has set the pace for creating new ground research in the machine learning implementation in the BYOD environment, which will offer invaluable insight into the study field, and researchers can employ it to find a research gap in the research domain.
1 citations
01 Jan 2018
TL;DR: In this paper, the authors used DEA-SBM to obtain the target variables and the decision tree model to produce important indicators to promote Taiwan's development in all aspects by improving social, economic, and environmental efficiency, while ensuring the sustainable development of environmental protection.
Abstract: This study uses DEA-SBM to obtain the target variables and the decision tree model to produce important indicators. The government can promote Taiwan's development in all aspects by improving social, economic, and environmental efficiency, while ensuring the sustainable development of environmental protection. The 15 social, economic, and environmental variables are used in the decision tree to obtain 3 repeatable important indicators that can affect Taiwan's social change, economic development, operational capability, environmental pollution, and change. K-means divides the 22 counties and cities into 1 capital city, 5 municipalities, and 16 other counties and cities, in order to understand the influence of the economy on social and environmental development during the period of 2013-2015 in Taiwan.
1 citations
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TL;DR: This paper develops and evaluates a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in the wild, and indicates that millions of IoT devices are detectable and identifiable within hours, both at a major ISP as well as an IXP.
Abstract: Consumer Internet of Things (IoT) devices are extremely popular, providing users with rich and diverse functionalities, from voice assistants to home appliances. These functionalities often come with significant privacy and security risks, with notable recent large scale coordinated global attacks disrupting large service providers. Thus, an important first step to address these risks is to know what IoT devices are where in a network. While some limited solutions exist, a key question is whether device discovery can be done by Internet service providers that only see sampled flow statistics. In particular, it is challenging for an ISP to efficiently and effectively track and trace activity from IoT devices deployed by its millions of subscribers --all with sampled network data.
In this paper, we develop and evaluate a scalable methodology to accurately detect and monitor IoT devices at subscriber lines with limited, highly sampled data in-the-wild. Our findings indicate that millions of IoT devices are detectable and identifiable within hours, both at a major ISP as well as an IXP, using passive, sparsely sampled network flow headers. Our methodology is able to detect devices from more than 77% of the studied IoT manufacturers, including popular devices such as smart speakers. While our methodology is effective for providing network analytics, it also highlights significant privacy consequences.
22 citations
01 Jan 2018
TL;DR: In this paper, the authors used DEA-SBM to obtain the target variables and the decision tree model to produce important indicators to promote Taiwan's development in all aspects by improving social, economic, and environmental efficiency, while ensuring the sustainable development of environmental protection.
Abstract: This study uses DEA-SBM to obtain the target variables and the decision tree model to produce important indicators. The government can promote Taiwan's development in all aspects by improving social, economic, and environmental efficiency, while ensuring the sustainable development of environmental protection. The 15 social, economic, and environmental variables are used in the decision tree to obtain 3 repeatable important indicators that can affect Taiwan's social change, economic development, operational capability, environmental pollution, and change. K-means divides the 22 counties and cities into 1 capital city, 5 municipalities, and 16 other counties and cities, in order to understand the influence of the economy on social and environmental development during the period of 2013-2015 in Taiwan.
1 citations