With the rapid spread of smart devices, such as smartphones and tablet PCs, user authentication is becoming increasingly important because various kinds of data concerning user privacy are processed within them. At present, in the case of smart devices, password-based authentication is frequently used; however, biometric authentication has attracted more attention as a user authentication technology. A smart device is equipped with various sensors, such as cameras, microphones, and touch panels, many of which enable biometric information to be obtained. While the function of biometric authentication is available in many smart devices, there remain some problems to be addressed for more secure and convenient user authentication. In this paper, we summarize the current problems with user authentication on smart devices and propose a novel user authentication system based on the concept of context awareness to resolve these problems. We also present our evaluation of the performance of the system by using biometric information that was acquired from smart devices. The evaluation demonstrates the effectiveness of our system.

The war between cyber attackers and security analysts is gradually intensifying. Owing to the ease of obtaining and creating support tools, recent malware continues to diversify into variants and new species. This increases the burden on security analysts and hinders quick analysis. Identifying malware families is crucial for efficiently analyzing diversified malware; thus, numerous low-cost, general-purpose, deep-learning-based classification techniques have been proposed in recent years. Among these methods, malware images that represent binary features as images are often used. However, no models or architectures specific to malware classification have been proposed in previous studies. Herein, we conduct a detailed analysis of the behavior and structure of malware and focus on PE sections that capture the unique characteristics of malware. First, we validate the features of each PE section that can distinguish malware families. Then, we identify PE sections that contain adequate features to classify families. Further, we propose an ensemble learning-based classification method that combines features of highly discriminative PE sections to improve classification accuracy. The validation of two datasets confirms that the proposed method improves accuracy over the baseline, thereby emphasizing its importance.