• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.605-614
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• 2012

Nowadays u-healthcare which is very sensitive to the character of user's information among other ubiquitous computing field is popular in medical field. u-healthcare deals extremely personal information including personal health/medical information so it is exposed to various weaknees and threats in the part of security and privacy. In this paper, RFID based patient's information protecting protocol that prevents to damage the information using his or her mobile unit illegally by others is proposed. The protocol separates the authority of hospital(doctor, nurse, pharmacy) to access to patient's information by level of access authority of hospital which is registered to management server and makes the hospital do the minimum task. Specially, the management server which plays the role of gateway makes access permission key periodically not to be accessed by others about unauthorized information except authorized information and improves patient's certification and management.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.67-75
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• 2018

According to Symantec's Internet Security Threat Report(2018), Internet security threats such as Cryptojackings, Ransomwares, and Mobile malwares are rapidly increasing and diversifying. It means that detection of malwares requires not only the detection accuracy but also versatility. In the past, malware detection technology focused on qualitative performance due to the problems such as encryption and obfuscation. However, nowadays, considering the diversity of malware, versatility is required in detecting various malwares. Additionally the optimization is required in terms of computing power for detecting malware. In this paper, we present Stream Order(SO)-CNN and Incremental Coordinate(IC)-CNN, which are malware detection schemes using CNN(Convolutional Neural Network) that effectively detect intelligent and diversified malwares. The proposed methods visualize each malware binary file onto a fixed sized image. The visualized malware binaries are learned through GoogLeNet to form a deep learning model. Our model detects and classifies malwares. The proposed method reveals better performance than the conventional method.