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Analysis of Research Trend and Performance Comparison on Message Authentication Code
Cryptographic technologies providing confidentiality and integrity such as encryption algorithms and message authentication codes (MACs) are necessary for preventing security threats in the Internet of Things (IoT) where various kinds of devices are interconnected. As a number of encryption schemes that have passed security verification are not necessarily suitable for low-power and low-performance IoT devices, various lightweight cryptographic schemes have been proposed. However, a study of lightweight MACs is not sufficient in comparison to that of lightweight block ciphers. Therefore, in this paper, we reviewed various kinds of MACs for their classification and analysis and then, we presented a new way for future MAC development. We also implemented major MAC algorithms and performed experiments to investigate their performance degradation on low-end micro-controllers.
Malware Classification System to Support Decision Making of App Installation on Android OS
Hong Ryeol Ryu, Yun Jang, Taekyoung Kwon
Although Android systems provide a permission-based access control mechanism and demand a user to decide whether to install an app based on its permission list, many users tend to ignore this phase. Thus, an improved method is necessary for users to intuitively make informed decisions when installing a new app. In this paper, with regard to the permission-based access control system, we present a novel approach based on a machine-learning technique in order to support a user decision-making on the fly. We apply the K-NN (K-Nearest Neighbors) classification algorithm with necessary weighted modifications for malicious app classification, and use 152 Android permissions as features. Our experiment shows a superior classification result (93.5% accuracy) compared to other previous work. We expect that our method can help users make informed decisions at the installation step.
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