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Our today’s world is becoming digital and mobile. Exploiting the advantages of wireless communication protocols is not only for telecommunication purposes, but also for payments, interaction with intelligent vehicles, etc. One of the most widespread wireless capabilities is the Bluetooth protocol. Just in 2010, 906 million mobile Bluetooth enabled phones had been sold, and in 2011, there were more than 40 million Bluetooth enabled health and medical devices on the market. Still in 2011, one third of all new vehicles produced worldwide included Bluetooth technology. Security and privacy protection is key in the digital world of today. There are security and privacy risks such as device tracking, communication eavesdropping, etc., which may come from improper Bluetooth implementation with very severe consequences for the users. The objective of this paper is to analyze the usage of Bluetooth in m-commerce and m-payment fields. The steps undertaken in this paper in order to come to a proposal for a secure architecture are the analysis of the state of the art of the relevant specifications, the existing risks and the known vulnerabilities the related known attacks. Therefore, we give first an overview of the general characteristics of Bluetooth technology today, going deeper in the analysis of Bluetooth stack’s layers and the security features offered by the specifications. After this analysis of the specifications, we study how known vulnerabilities have been exploited with a comprehensive list of known attacks, which poses serious threats for the users. With all these elements as background, we conclude the paper proposing a design for Secure Architecture for Bluetooth-Enhanced Mobile “Smart” Commerce Environments.
Mobile Security has been a rapidly growing field in the security area. With the increases of mobile devices and mobile applications, the need for mobile security has increased dramatically over the past several years. Many research and development projects on mobile security are ongoing in government, industry and academia. In this paper, we present an analysis of current mobile security problems and propose the possible solutions to malware threats. Our experiments show antimalware can protect mobile device from different types of mobile malware threats effectively.
The sale of products using the android Operation System (OS) phone
is increasing in rate: the fact is that its price is cheaper but its configured hardware is higher, users easily buy it and the approach to this product
increases the risk of the spread of mobile malware. The understanding of
majority of the users of this mobile malware is still limited. While they are
growing at a faster speed in the number and level of sophistication, especially
their variations have created confusion for
users; therefore worrying about the safety of its users is required. In this paper, the author discussed the identification and analysis of malware families on Android Mobiles.
The author selected the recognizable characteristics from ordinary users with
their families collected from 58 malware families and 1485 malware samples and
proposed solutions as recommendations to users before installing it with the
ultimate desire to mitigate the damage in the community that is on the android
phone, especially the ordinary users with limited understanding about potential
hazards. It would be helpful for the ordinary users to identify the mobile
malware in order to mitigate the information security risk.
In the last years, increasing smartphones’
capabilities have caused a paradigm shift in the way of users’ view and using mobile
devices. Although researchers have started to focus on behavioral models to
explain and predict human behavior, there is limited empirical research about
the influence of smartphone users’ individual differences on the usage of
security measures. The aim of this study is to examine the influence of
individual differences on cognitive determinants of behavioral intention to use
security measures. Individual differences are measured by the Five-Factor
Model; cognitive determinants of behavioral intention are adapted from the
validated behavioral models theory of planned behavior and technology
acceptance model. An explorative, quantitative survey of 435 smartphone users is served as data basis. The results suggest
that multiple facets of smartphone user’s personalities significantly affect
the cognitive determinants, which indicate the behavioral intention to use
security measures. From these findings, practical and theoretical implications
for companies, organizations, and researchers are derived and discussed.