Kamer Ali Yüksel

Static Analysis of Executables for Collaborative Malware Detection on Android

Smartphones are getting increasingly popular and several malwares appeared targeting these devices. General countermeasures to smartphone malwares are currently limited to signature-based antivirus scanners which efficiently detect known malwares, but they have serious shortcomings with new and unknown malwares creating a window of opportunity for attackers. As smartphones become host for sensitive data and applications, extended malware detection mechanisms are necessary complying with the corresponding resource constraints. The contribution of this paper is twofold. First, we perform static analysis on the executables to extract their function calls in Android environment using the command readelf. Function call lists are compared with malware executables for classifying them with PART, Prism and Nearest Neighbor Algorithms. Second, we present a collaborative malware detection approach to extend these results. Corresponding simulation results are presented.

A Decision Forest Based Feature Selection Framework for Action Recognition from RGB-Depth Cameras

In this paper, we present an action recognition framework leveraging data mining capabilities of random decision forests trained on kinematic features. We describe human motion via a rich collection of kinematic feature time-series computed from the skeletal representation of the body in motion. We discriminatively optimize a random decision forest model over this collection to identify the most effective subset of features, localized both in time and space. Later, we train a support vector machine classifier on the selected features. This approach improves upon the baseline performance obtained using the whole feature set with a significantly less number of features (one tenth of the original). On MSRC-12 dataset (12 classes), our method achieves 94% accuracy. On the WorkoutSU-10 dataset, collected by our group (10 physical exercise classes), the accuracy is 98%. The approach can also be used to provide insights on the spatiotemporal dynamics of human actions.

MagiWrite: towards touchless digit entry using 3D space around mobile devices

In this work, we present a new approach for text (mainly digit) entry based on digit shaped gestures created in 3D space around a mobile device. Some new mobile devices such as Apple iPhone 3GS and Google Android are equipped with magnetic (compass) sensor. The main idea is to influence the magnetic sensor using a magnet taken in hand. The user draws (writes) digits in the 3D space around the device using the magnet taken in hand. Movement of the magnet changes temporal pattern of magnetic field around the device which is sensed and registered by the magnetic (compass) sensor. The registered pattern is then compared against already recorded templates for different digits. Such a text (digit) entry approach can be especially useful for small mobile devices in which it is hard to operate small buttons or touch screen. Using our technique, the text entry space extends beyond physical boundaries of the device. A demonstrator for this approach is implemented on Apple iPhone 3GS platform. It demonstrates registering a few templates for different digits, and recognizing digits written in the space around the device

MagiMusic: using embedded compass (magnetic) sensor for touch-less gesture based interaction with digital music instruments in mobile devices

Playing musical instruments such as chordophones, percussions and keyboard types accompany with harmonic interaction of players hand with the instruments. In this work, we present a novel approach that enables the user to imitate the music playing gestures around mobile devices. In our approach, touch-less gestures, which change magnetic field around the device, are employed for interaction. The activity of playing an instrument can be transparently pursued by moving a tiny magnet in hand around new generation of mobile phones equipped with embedded digital compass (magnetic sensor). The phonation intentions of the user can be simulated on the mobile device by capturing the gestural pattern using magnetic sensor. The proposed method allows digital imitation of a broad number of instruments while still being able to sense musical hits and relative plectrum gestures. It provides a framework for extending interaction space with music applications beyond physical boundaries of small mobile devices, and to 3D space around the device. This can allow for a more natural, comfortable and flexible interaction. We present several mobile music applications developed based on the proposed method for Apple iPhone 3GS.

A decision forest based feature selection framework for action recognition from RGB-depth cameras

In this paper, we present an action recognition framework leveraging data mining capabilities of random decision forests trained on kinematic features. We describe human motion via a rich collection of kinematic feature time-series computed from the skeletal representation of the body in motion. We discriminatively optimize a random decision forest model over this collection to identify the most effective subset of features, localized both in time and space. Later, we train a support vector machine classifier on the selected features. This approach improves upon the baseline performance obtained using the whole feature set with a significantly less number of features (one tenth of the original). On MSRC-12 dataset (12 classes), our method achieves 94% accuracy. On the WorkoutSU-10 dataset, collected by our group, the accuracy is 98%. The approach can also be used to provide insights on the spatiotemporal dynamics of human actions.

Towards digital music performance for mobile devices based on magnetic interaction

Digital music performance require high degree of interaction using natural, intuitive input controllers that provide fast feedback on users action. One of the primary considerations of professional artists is a powerful and creative tool that minimizes the number of steps required for the speed-demanding processes. Most of the musical performance applications, which are designed for mobile devices, use touch-screen or accelerometer as interaction modalities. In this work, we present a novel interface for musical performance that is based on the magnetic field sensor embedded in recent mobile devices. The proposed method, at this point, promises a new independent ground for inputting momentary data during music composition and manipulation process. Giving the opportunity to freely, fully and quickly utilize the surrounding 3D space, it possesses the potential to bring a wide-spectrum of unique options for production and performance process of music.

Touchless magnetic interaction with mobile devices

In this paper, we introduce a revolutionary interaction framework that is based on the idea of around device interaction. The proposed method constitutes a touch-less data entry system that is based on the interaction between the magnetic fields around a device and a magnet. The magnetic field that surrounds the device is generated by a magnetic sensor (compass) that is embedded in the new generation of mobile phones. The movements of a permanent magnet in front of the device deforms the sensors original magnetic field pattern whereby we can constitute a new means of communication between the user and the device. Thus, the magnetic field encompassing the device plays the role of a communication channel and encodes the hand-movement patterns of the user into temporal changes of the sensors magnetic field. In the back-end of the communication, an engine samples the momentary status of the field during a trial and recognizes the users pattern by matching it against some pre-recorded templates. The proposed method has been tested in a variety of applications (such as micro-interaction, handwriting recognition, user authentication, etc) and concluded in very promising results.

Designing mobile phones using silent speech input and auditory feedback

In this work, we have propose a novel design for a basic mobile phone, which is focused on the essence of mobile communication and connectivity, based on a silent speech interface and auditory feedback. This assistive interface takes the advantages of voice control systems while discarding its disadvantages such as the background noise, privacy and social acceptance. The proposed device utilizes low-cost and commercially available hardware components. Thus, it would be affordable and accessible by majority of users including disabled, elderly and illiterate people.

Parallel evolutionary optimization of digital sound synthesis parameters

In this research, we propose a novel parallelizable architecture for the optimization of various sound synthesis parameters. The architecture employs genetic algorithms to match the parameters of different sound synthesizer topologies to target sounds. The fitness function is evaluated in parallel to decrease its convergence time. Based on the proposed architecture, we have implemented a framework using the SuperCollider audio synthesis and programming environment and conducted several experiments. The results of the experiments have shown that the framework can be utilized for accurate estimation of the sound synthesis parameters at promising speeds.

Prototyping input controller for touch-less interaction with ubiquitous environments

In ubiquitous computing environments, the information processing is integrated into everyday objects that are ideally small, inexpensive and wirelessly networked devices. Contemporary human-computer interaction models are not adequate to control miniaturized devices, which are distributed throughout everyday life and activities. This post-desktop model requires natural gesture-based interaction with distributed devices in an egocentric manner as opposed to the current device-centric interaction. In this work, we have utilized the recently proposed touch-less gesture-based interaction method based on magnetic field to provide a hardware basis for a wearable input controller. Furthermore, we have discussed how the proposed device can allow natural interaction with other devices within a ubiquitous computing environment such as personal area network.