Malware detection techniques for mobile devices

Mobile devices have become very popular nowadays, due to its portability and high performance, a mobile device became a must device for persons using information and communication technologies. In addition to hardware rapid evolution, mobile applications are also increasing in their complexity and performance to cover most needs of their users. Both software and hardware design focused on increasing performance and the working hours of a mobile device. Different mobile operating systems are being used today with different platforms and different market shares. Like all information systems, mobile systems are prone to malware attacks. Due to the personality feature of mobile devices, malware detection is very important and is a must tool in each device to protect private data and mitigate attacks. In this paper, analysis of different malware detection techniques used for mobile operating systems is provides. The focus of the analysis will be on the to two competing mobile operating systems - Android and iOS. Finally, an assessment of each technique and a summary of its advantages and disadvantages is provided. The aim of the work is to establish a basis for developing a mobile malware detection tool based on user profiling.Comment: 11 pages, 6 figure

Device-Centric Monitoring for Mobile Device Management

The ubiquity of computing devices has led to an increased need to ensure not only that the applications deployed on them are correct with respect to their specifications, but also that the devices are used in an appropriate manner, especially in situations where the device is provided by a party other than the actual user. Much work which has been done on runtime verification for mobile devices and operating systems is mostly application-centric, resulting in global, device-centric properties (e.g. the user may not send more than 100 messages per day across all applications) being difficult or impossible to verify. In this paper we present a device-centric approach to runtime verify the device behaviour against a device policy with the different applications acting as independent components contributing to the overall behaviour of the device. We also present an implementation for Android devices, and evaluate it on a number of device-centric policies, reporting the empirical results obtained.Comment: In Proceedings FESCA 2016, arXiv:1603.0837

Bluetooth familiarity: methods of calculation, applications and limitations

We present an approach for utilising a mobile device’s Bluetooth sensor to automatically identify social interactions and relationships between individuals in the real world. We show that a high degree of accuracy is achievable in the automatic identification of mobile devices of familiar individuals. This has implications for mobile device security, social networking and in context aware information access on a mobile device

Further Perspectives on Corporate Wrongdoing, In Pari Delicto, and Auditor Malpractice

In recent years, instant messaging (IM) has started to replace short message service (SMS) in communication. IM offers more functionality but there is a great downside. IM demands more power and drains the mobile device battery faster. This paper shows the energy consumption of  IM when the user is not using the application and how the consumption  can be reduced by enabling mobile sensors and sending fewer packets by the application. We began by investigating the various sensors that are supported by mobile devices. With the retrieved vendor information, we evaluated the different sensors and chose two sensors, light sensor and proximity sensor in order to study their use for reduction of energy in  an instant messaging scenario. These two sensors can together estimate if the mobile device is placed in the pocket of the user. The development of a simple IM application was completed and sensors were used to create an extension to the application. The extension would lengthen the interval between the updates of the automatic update function when the mobile was inactive, reducing the energy consumption. Two types of tests were performed. The first test evaluated if the extension would correctly deduce that the mobile device was placed inside a pocket. The mobile device with the pocket-aware application was used in different common situations and the tests showed that the extension made a correct computation in seven of nine situations. The faulty situations were when the mobile device is placed with the screen faced down to a surface. The second test compared the energy consumed by a pocket-aware application compared to a mobile device without our extension. Based on the results that we retrieved, we estimated that during a one minute period the pocket-aware application with an update interval of ten seconds could save on average 12% and could save on average 62% when the update interval was increased to fifteen seconds

A Forensically Sound Adversary Model for Mobile Devices

In this paper, we propose an adversary model to facilitate forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device technologies. An integral principle and significant constraint upon forensic practitioners is that of forensic soundness. Our adversary model specifically considers and integrates the constraints of forensic soundness on the adversary, in our case, a forensic practitioner. One construction of the adversary model is an evidence collection and analysis methodology for Android devices. Using the methodology with six popular cloud apps, we were successful in extracting various information of forensic interest in both the external and internal storage of the mobile device

Phishing Techniques in Mobile Devices

The rapid evolution in mobile devices and communication technology has increased the number of mobile device users dramatically. The mobile device has replaced many other devices and is used to perform many tasks ranging from establishing a phone call to performing critical and sensitive tasks like money payments. Since the mobile device is accompanying a person most of his time, it is highly probably that it includes personal and sensitive data for that person. The increased use of mobile devices in daily life made mobile systems an excellent target for attacks. One of the most important attacks is phishing attack in which an attacker tries to get the credential of the victim and impersonate him. In this paper, analysis of different types of phishing attacks on mobile devices is provided. Mitigation techniques - anti-phishing techniques - are also analyzed. Assessment of each technique and a summary of its advantages and disadvantages is provided. At the end, important steps to guard against phishing attacks are provided. The aim of the work is to put phishing attacks on mobile systems in light, and to make people aware of these attacks and how to avoid themComment: 9 pages, two figure

Reading with Mobile Phone & Large Display

In this paper we compare performance and usability between three different device combinations: a) mobile phone b) touch screen c) mobile phone & screen. We show that mobile phone & screen has a better perform-ance than phone only. We also discuss some interaction issues when using a mobile phone with a large screen