Posts with Tag ‘Machine Learning’

Risk Assessment of Atrial Fibrillation: a Failure Prediction Approach

Tuesday, June 17, 2014, 09:25
Risk Assessment of Atrial Fibrillation: a Failure Prediction Approach Authors:
Jelena Milosevic
Andreas Dittrich
Alberto Ferrante
Miroslaw Malek
Camilo Rojas Quiros
Rubén Braojos
Giovanni Ansaloni
David Atienza

41st Computing in Cardiology Conference, CinC 2014, Cambridge, MA, USA, September 7-10, 2014

Download: author version, final published version

We present a methodology for identifying patients who have experienced Paroxysmal Atrial Fibrillation (PAF) among a given subjects population. Our work is intended as an initial step towards the design of an unobtrusive system for concurrent detection and monitoring of chronic cardiac conditions.

Our methodology comprises two stages: off-line training and on-line analysis. During training the most significant features are selected using machine-learning methods, without relying on a manual selection based on previous knowledge. Analysis is based on two phases: feature extraction and detection of PAF patients. Light-weight algorithms are employed in the feature extraction phase, allowing the on-line implementation of this step on wearable and resource-constrained sensor nodes. The detection phase employs techniques borrowed from the field of failure prediction. While these algorithms have found extensive applications in diverse scenarios, their application to automated cardiac analysis has not been sufficiently investigated.

Obtained results, in terms of performance, are comparable to similar efforts in the field. Nonetheless, the proposed method employs computationally simpler and more efficient algorithms, which are compatible with the computational constraints of state-of-the-art body sensor nodes.

Categories: Publication, Research and Education
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A Resource-Optimized Approach to Efficient Early Detection of Mobile Malware

Friday, May 23, 2014, 14:36
A Resource-Optimized Approach to Efficient Early Detection of Mobile Malware Authors:
Jelena Milosevic
Andreas Dittrich
Miroslaw Malek
Alberto Ferrante

3rd International Workshop on Security of Mobile Applications, IWSMA 2014, Fribourg, Switzerland, September 8-12, 2014

Download: accepted version, final published version

With explosive growth in the number of mobile devices, the mobile malware is rapidly spreading. Existing solutions, which are mainly based on binary signatures, are no longer effective, making security one of the key issues. The main contribution of this paper is a novel methodology to design and implement secure mobile devices by offering a resource-optimized method that combines efficient, light-weight malware detection on the device with high precision detection methods on cloud servers. We focus on early detection of behavioral patterns of malware families rather than the detection of malware binary signatures. Together with the alarm about the device being attacked, damage that detected type of malware can cause is estimated. Furthermore, the database with behavioral patterns is continuously updated, thus keeping a device resistant to new malware families.

Categories: Publication, Research and Education, Security
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