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Jueun Jeon
Department of Multimedia Engineering, Dongguk University, Seoul 100-715, Korea

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Journal article
Published: 20 May 2020 in IEEE Access
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Internet of Things (IoT) technology provides the basic infrastructure for a hyper connected society where all things are connected and exchange information through the Internet. IoT technology is fused with 5G and artificial intelligence (AI) technologies for use various fields such as the smart city and smart factory. As the demand for IoT technology increases, security threats against IoT infrastructure, applications, and devices have also increased. A variety of studies have been conducted on the detection of IoT malware to avoid the threats posed by malicious code. While existing models may accurately detect malicious IoT code identified through static analysis, detecting the new and variant IoT malware quickly being generated may become challenging. This paper proposes a dynamic analysis for IoT malware detection (DAIMD) to reduce damage to IoT devices by detecting both well-known IoT malware and new and variant IoT malware evolved intelligently. The DAIMD scheme learns IoT malware using the convolution neural network (CNN) model and analyzes IoT malware dynamically in nested cloud environment. DAIMD performs dynamic analysis on IoT malware in a nested cloud environment to extract behaviors related to memory, network, virtual file system, process, and system call. By converting the extracted and analyzed behavior data into images, the behavior images of IoT malware are classified and trained in the Convolution Neural Network (CNN). DAIMD can minimize the infection damage of IoT devices from malware by visualizing and learning the vast amount of behavior data generated through dynamic analysis.

ACS Style

Jueun Jeon; Jong Hyuk Park; Young-Sik Jeong. Dynamic Analysis for IoT Malware Detection With Convolution Neural Network Model. IEEE Access 2020, 8, 96899 -96911.

AMA Style

Jueun Jeon, Jong Hyuk Park, Young-Sik Jeong. Dynamic Analysis for IoT Malware Detection With Convolution Neural Network Model. IEEE Access. 2020; 8 (99):96899-96911.

Chicago/Turabian Style

Jueun Jeon; Jong Hyuk Park; Young-Sik Jeong. 2020. "Dynamic Analysis for IoT Malware Detection With Convolution Neural Network Model." IEEE Access 8, no. 99: 96899-96911.

Journal article
Published: 15 October 2019 in Applied Sciences
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Cloud computing services that provide computing resources to users through the Internet also provide computing resources in a virtual machine form based on virtualization techniques. In general, supercomputing and grid computing have mainly been used to process large-scale jobs occurring in scientific, technical, and engineering application domains. However, services that process large-scale jobs in parallel using idle virtual machines are not provided in cloud computing at present. Generally, users do not use virtual machines anymore, or they do not use them for a long period of time, because existing cloud computing assigns all of the use rights of virtual machines to users, resulting in the low use of computing resources. This study proposes a scheme to process large-scale jobs in parallel, using idle virtual machines and increasing the resource utilization of idle virtual machines. Idle virtual machines are basically identified through specific determination criteria out of virtual machines created using OpenStack, and then they are used in computing services. This is called the idle virtual machine–resource utilization (IVM–ReU), which is proposed in this study.

ACS Style

Jueun Jeon; Jong Hyuk Park; Young-Sik Jeong. Resource Utilization Scheme of Idle Virtual Machines for Multiple Large-Scale Jobs Based on OpenStack. Applied Sciences 2019, 9, 4327 .

AMA Style

Jueun Jeon, Jong Hyuk Park, Young-Sik Jeong. Resource Utilization Scheme of Idle Virtual Machines for Multiple Large-Scale Jobs Based on OpenStack. Applied Sciences. 2019; 9 (20):4327.

Chicago/Turabian Style

Jueun Jeon; Jong Hyuk Park; Young-Sik Jeong. 2019. "Resource Utilization Scheme of Idle Virtual Machines for Multiple Large-Scale Jobs Based on OpenStack." Applied Sciences 9, no. 20: 4327.