Mobile devices have evolved and experienced an immense popularity over the last few years. This growth however has exposed mobile devices to an increasing number of security threats. Despite the variety of peripheral protection mechanisms described in the literature, authentication and access control cannot provide integral protection against intrusions. Thus, a need for more intelligent and sophisticated security controls such as intrusion detection systems (IDSs) is necessary. Whilst much work has been devoted to mobile device IDSs, research on anomaly-based or behaviour-based IDS for such devices has been limited leaving several problems unsolved. Motivated by this fact, in this paper, we focus on anomaly-based IDS for modern mobile devices. A dataset consisting of iPhone users data logs has been created, and various classification and validation methods have been evaluated to assess their effectiveness in detecting misuses. Specifically, the experimental procedure includes and cross-evaluates four machine learning algorithms (i.e. Bayesian networks, radial basis function, K-nearest neighbours and random Forest), which classify the behaviour of the end-user in terms of telephone calls, SMS and Web browsing history. In order to detect illegitimate use of service by a potential malware or a thief, the experimental procedure examines the aforementioned services independently as well as in combination in a multimodal fashion. The results are very promising showing the ability of at least one classifier to detect intrusions with a high true positive rate of 99.8%.

Keystroke dynamics is a well‐investigated behavioural biometric based on the way and rhythm in which someone interacts with a keyboard or keypad when typing characters. This paper explores the potential of this modality but for touchscreen‐equipped smartphones. The main research question posed is whether ‘touchstroking’ can be effective in building the biometric profile of a user, in terms of typing pattern, for future authentication. To reach this goal, we implemented a touchstroke system in the Android platform and executed different scenarios under disparate methodologies to estimate its effectiveness in authenticating the end‐user. Apart from typical classification features used in legacy keystroke systems, we introduce two novel ones, namely, speed and distance. From the experiments, it can be argued that touchstroke dynamics can be quite competitive, at least when compared to similar results obtained from keystroke evaluation studies. As far as we are aware of, this is the first time this newly arisen behavioural trait is put into focus. Copyright © 2014 John Wiley & Sons, Ltd.

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BackgroundRadial 2D MRI scans of the hip are routinely used for the diagnosis of the camtype of femoroacetabular impingement (FAI) and of avascular necrosis (AVN) of the femoral head, both considered causes of hip joint osteoarthritis in young and active patients. A method for automated and accurate segmentation of the proximal femur from radial MRI scans could be very useful in both clinical routine and biomechanical studies. However, to our knowledge, no such method has been published before. PurposeThe aims of this study are the development of a system for the segmentation of the proximal femur from radial MRI scans and the reconstruction of its 3D model that can be used for diagnosis and planning of hip-preserving surgery. MethodsThe proposed system relies on: (a) a random forest classifier and (b) the registration of a 3D template mesh of the femur to the radial slices based on a physicallybased deformable model. The input to the system are the radial slices and the manually-specified positions of three landmarks. Our dataset consists of the radial MRI scans of 25 patients symptomatic of FAI or AVN and accompanying manual segmentation of the femur, treated as the ground truth. ResultsThe achieved segmentation of the proximal femur has an average Dice similarity coefficient (DSC) of 96.37 ± 1.55 %, an average symmetric mean absolute distance (SMAD) of 0.94 ± 0.39 mm and an average Hausdorff distance of 2.37 ± 1.14 mm. In the femoral head sub-region, the average SMAD is 0.64 ± 0.18 mm and the average Hausdorff distance is 1.41 ± 0.56 mm. ConclusionsWe validated a semi-automated method for the segmentation of the proximal femur from radial MR scans. A 3D model of the proximal femur is also reconstructed, which can be used for the planning of hip-preserving surgery.

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The key issue for any mobile application or service is the way it is delivered and experienced by users, who eventually may decide to keep it on their software portfolio or not. Without doubt, security and privacy have both a crucial role to play towards this goal. Very recently, Gartner has identified the top ten of consumer mobile applications that are expected to dominate the market in the near future. Among them one can earmark location-based services in number 2 and mobile instant messaging in number 9. This paper presents a novel application namely MILC that blends both features. That is, MILC offers users the ability to chat, interchange geographic co-ordinates and make Splashes in real-time. At present, several implementations provide these services separately or jointly, but none of them offers real security and preserves the privacy of the end-users at the same time. On the contrary, MILC provides an acceptable level of security by utilizing both asymmetric and symmetric cryptography, and most importantly, put the user in control of her own personal information and her private sphere. The analysis and our contribution are threefold starting from the theoretical background, continuing to the technical part, and providing an evaluation of the MILC system. We present and discuss several issues, including the different services that MILC supports, system architecture, protocols, security, privacy etc. Using a prototype implemented in Google's Android OS, we demonstrate that the proposed system is fast performing, secure, privacypreserving and potentially extensible.

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