Mobile network anomaly detection and mitigation: The NEMESYS approach

Mobile malware and mobile network attacks are becoming a significant threat that accompanies the increasing popularity of smart phones and tablets. Thus in this paper we present our research vision that aims to develop a network-based security solution combining analytical modelling, simulation and learning, together with billing and control-plane data, to detect anomalies and attacks, and eliminate or mitigate their effects, as part of the EU FP7 NEMESYS project. These ideas are supplemented with a careful review of the state-of-the-art regarding anomaly detection techniques that mobile network operators may use to protect their infrastructure and secure users against malware

A novel prototype generation technique for handwriting digit recognition

The aim of this paper is to introduce a novel prototype generation technique for handwriting digit recognition. Prototype generation is approached as a two-stage process. The first stage uses an Adaptive Resonance Theory 1 (ART1) based algorithm to select an effective initial solution, while the second one executes a fine tuning designed to generate the best prototypes. To this end, the second stage deals with an optimization problem, in which the objective function to be minimized is the cost function associated to the classification. A naive evolution strategy is used to generate the prototype set able to reduce classification time, without greatly affecting the accuracy. Moreover, as the ART1 based algorithm has incremental learning capability, the first stage is also useful for selecting the prototype set according to variations in handwriting style. The classification task is performed by the k-nearest neighbor classifier. Experimental tests on the MNIST dataset demonstrated that our technique represents a good trade-off among accuracy, classification speed and robustness to handwriting style changes

Similarity-based regularization for semi-supervised learning for handwritten digit recognition

This paper presents an experimental analysis on the use of semi-supervised learning in the handwritten digit recognition field. More specifically, two new feedback-based techniques for retraining individual classifiers in a multi-expert scenario are discussed. These new methods analyze the final decision provided by the multi-expert system so that sample classified with a confidence greater than a specific threshold is used to update the system itself. Experimental results carried out on the CEDAR (handwritten digits) database are presented. In particular, error rate, similarity index and a new correlation score among them are considered in order to evaluate the best retraining rule. For the experimental evaluation, an SVM classifier and five different combination techniques at abstract and measurement level have been used. Finally, the results show that iterating the feedback process, on different multi-expert systems built with the five combination techniques, one retraining rule is winning over the other respect to the best correlation score

Writing Generation Model for Health Care neuromuscular System

In this paper the use of handwriting artifacts for health investigation is addressed. For the purpose, the paper first presents the Delta-Log and Sigma-Lognormal models to investigate on the handwriting generation processes carried out by the neuromuscular system. Successively, a computational system for handwriting analysis is presented and some considerations are exploited about the use of the model to investigate insurgence and monitoring of some neuromuscular diseases. The experimental results show the validity of the proposed approach and highlight some directions for further research

PREAMI: Perindopril and remodelling in elderly with acute myocardial infarction: Study rationale and design

Angiotensin-converting enzyme (ACE) inhibitors reduce mortality, the development of remodeling, left ventricular (LV) dysfunction, and ischemic events, both when administered alone as long-term treatment in patients with impaired LV function and/or heart failure (HF) and as short-term treatment, early after acute myocardial infarction (AMI) and/or HF. The few data available on the use of ACE inhibitors in the elderly after AMI are conflicting. Nothing is known about the effects of ACE inhibitors in elderly postinfarction patients with preserved LV function: these patients have a remarkable medium- to long-term mortality and HF incidence after infarction. The aim of this study is to evaluate, in patients with AMI aged ≥65 years, the effects of Perindopril on the combined outcome of death, hospitalization for HF, and heart remodeling, considered to be a ≥8% increase in LV end-diastolic volume (LVEDV). Secondary objectives include the same factors listed in the primary end points hut considered separately. In addition, safety of the drug, ventricular remodeling, and adaptation are being evaluated. A total of 1100 patients with AMI (first episode or reinfarction), aged ≥65 years, and preserved or only moderately depressed LV (LV ejection fraction ≥40%), are to he enrolled and randomly assigned to treatment (8 mg for 12 months of Perindopril or placebo, in double-blind conditions). Clinical assessment is performed at fixed times, and periodic evaluations of (1) ventricular shape, dimensions, and function by quantitative 2-D echocardiography, and (2) heart rate variability and arrhythmias by ambulatory electrocardiographic monitoring are anticipated. The results and conclusions will be available by 2002 year