Mirang Park

An Authentication Method Independent of Tap Operation on the Touchscreen of a Mobile Device

At the present time, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, are detected over the skin surface. Muscle movement can be differentiated by analyzing the s-EMG. In this paper, a series of experiments was carried out to investigate the prospect of an authentication method using s-EMGs. Specifically, several gestures of the wrist were introduced, and the s-EMGs generated for each motion pattern were measured. We compared the s-EMG patterns generated by each subject with the patterns generated by other subjects. As a result, it was found that each subject has similar patterns that are different from those of other subjects. Thus, sEMGs can be used to confirm one’s identification for authenticating passwords on touchscreen devices.

Novel Unsupervised SPITters Detection Scheme by Automatically Solving Unbalanced Situation

Spam over Internet telephony (SPIT) is recognized as a new threat for voice communication services such as voice over Internet protocol (VoIP). Due to the privacy reason, it is desired to detect SPITters (SPIT callers) in a VoIP service without training data. Although a clustering-based unsupervised SPITters detection scheme has been proposed, it does not work well when the SPITters account for a small fraction of the entire caller. In this paper, we propose an unsupervised SPITters detection scheme by adding artificial SPITters data to solve the unbalanced situation. The key contribution is to propose a novel way to automatically decide how much artificial data should be added. We show that classification performance is improved by means of computer simulation with real and artificial call log data sets.

Evaluation of feature values of surface electromyograms for user authentication on mobile devices

At the present time, mobile devices, such as tablet-type PCs and smart phones, have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user authentication method for mobile devices that use surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, are detected over the skin surface. Muscle movement can be differentiated by analyzing the s-EMG. In this paper, a method that uses a list of gestures as a password is proposed. And also, results of experiments are presented that was carried out to investigate the performance of the method extracting feature values from s-EMG signals (using the Fourier transform) adopted in this research.

On applying support vector machines to a user authentication method using surface electromyogram signals

Myo^{TM}

Proposal of Rhythm Authentication Method Using Users Classification by Self-Organizing Map

MyoTM, which is the candidate of s-EMG measurement device used in a prototype system for future substantiative experiments, was used in the experiment together with the s-EMG measuring device used in the previous research to investigate its performance.

Evaluation of User Identification Methods for Realizing an Authentication System Using s-EMG

Recently, mobile terminals such as smartphones have come into widespread use. Most of such mobile terminals store several types of important data, such as personal information. Therefore, in order to prevent data theft, it is necessary to lock and unlock terminals using a personal authentication method such as personal identification numbers (PINs). However, most existing authentication methods have a common problem, referred to as shoulder-surfing in which authentication information is covertly obtained by peeking over the shoulder of a user as he/she completes the authentication sequence. In this paper, we propose a puzzle authentication method that is very simple and sufficiently secure, even when the authentication sequence is being watched. This method uses a grid-based authentication scheme in which a user selects four out of 16 panels, and four out of 16 positions. We also implemented the proposed method on a mobile terminal and evaluated it through experiments and questionnaire surveys.

A Proposal of Highly Responsive Distributed Denial-of-Service Attacks Detection Using Real-Time Burst Detection Method

At present, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are detected over the skin surface, are generated by the electrical activity of muscle fibers during contraction. Muscle movement can be differentiated by analyzing the s-EMG. Taking advantage of the characteristics, we proposed a method that uses a list of gestures as a password in the previous study. In this paper, we introduced support vector machines (SVM) for improvement of the method of identifying gestures. A series of experiments was carried out to evaluate the performance of the SVM based method as a gesture classifier and we also discussed its security.

Evaluation of Index Poisoning Method in Large Scale Winny Network

At the present time, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching.