Network Activity Monitoring Against Malware in Android Operating System

Google’s Android is the most used Operating System in mobile devices but as its popularity has increased hackers have taken advantage of the momentum to plague Google Play (Android’s Application Store) with multipurpose Malware that is capable of stealing private information and give the hacker remote control of smartphone’s features in the worst cases. This work presents an innovative methodology that helps in the process of malware detection for Android Operating System, which addresses aforementioned problem from a different perspective that even popular Anti-Malware software has left aside. It is based on the analysis of a common characteristic to all different kinds of malware: the need of network communications, so the victim device can interact with the attacker. It is important to highlight that in order to improve the security level in Android, our methodology should be considered in the process of malware detection. As main characteristic, it does not need to install additional kernel modules or to root the Android device. And finally as additional characteristic, it is as simple as can be considered for non-experienced users

Prediction of protein distance maps by assembling fragments according to physicochemical similarities

The prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein consists of determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Many approaches cited in the literature use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, implemented with parallel multithreading, a set of 30 physicochemical amino acid properties selected from the AAindex database were used. Several protein tertiary structure prediction methods produce a contact map. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. The results of experiments with several non-homologous protein sets demonstrate the generality of this method and its prediction quality using the amino acid properties considered

An Efficient Nearest Neighbor Method for Protein Contact Prediction

A variety of approaches for protein inter-residue contact pre diction have been developed in recent years. However, this problem is far from being solved yet. In this article, we present an efficient nearest neigh bor (NN) approach, called PKK-PCP, and an application for the protein inter-residue contact prediction. The great strength of using this approach is its adaptability to that problem. Furthermore, our method improves considerably the efficiency with regard to other NN approaches. Our NN-based method combines parallel execution with k-d tree as search algorithm. The input data used by our algorithm is based on structural features and physico-chemical properties of amino acids besides of evo lutionary information. Results obtained show better efficiency rates, in terms of time and memory consumption, than other similar approaches.Ministerio de Educación y Ciencia TIN2011-28956-C02-0

An Evolutionary Approach for Protein Contact Map Prediction

In this study, we present a residue-residue contact prediction approach based on evolutionary computation. Some amino acid properties are employed according to their importance in the folding process: hydrophobicity, polarity, charge and residue size. Our evolutionary algorithm provides a set of rules which determine different cases where two amino acids are in contact. A rule represents two windows of three amino acids. Each amino acid is characterized by these four properties. We also include a statistical study for the propensities of contacts between each pair of amino acids, according to their types, hydrophobicity and polarity. Different experiments were also performed to determine the best selection of properties for the structure prediction among the cited properties.Junta de Andalucía P07-TIC-02611Ministerio de Ciencia y Tecnología TIN2007-68084-C02-0

Alpha Helix Prediction Based on Evolutionary Computation

Multiple approaches have been developed in order to predict the protein secondary structure. In this paper, we propose an approach to such a problem based on evolutionary computation. The proposed ap proach considers various amino acids properties in order to predict the secondary structure of a protein. In particular, we will consider the hy drophobicity, the polarity and the charge of amino acids. In this study, we focus on predicting a particular kind of secondary structure: α-helices. The results of our proposal will be a set of rules that will identify the beginning or the end of such a structure.Junta de Andalucía P07-TIC-02611Ministerio de Ciencia y Tecnología TIN2007-68084-C02-0

Reconocimiento de voz basado en MFCC, SBC y Espectrogramas

One of the problems of the Automatic Speech Recognition systems is the voice’s changes. Typically, a person can have voluntary and involuntary voice’s changes and the system can get confused in these cases, also the changes could be natural and artificial. This paper proposes and recognition system with a parallel identification, using three different algorithms: MFCC, SBC and Spectrogram. Using a Support Vector Machine as a classifier, every algorithm gives a group of persons with the highest likelihood and, after an evaluation, the result is obtained. The aim of this paper is to take advantage of the three algorithms.Uno de los problemas en los sistemas de reconocimiento automático de hablante son los cambios en la voz. Comúnmente, una persona puede tener cambios voluntarios e involuntarios (también naturales y artificiales) que provocan confusiones en el sistema, los cambios en la voz también pueden ser naturales y artificiales. En el artículo presente se propone un sistema de reconocimiento a través de una identificación en paralelo, usando tres algoritmos: MFCC, SBC y el espectrograma. Empleando una máquina de soporte vectorial como clasificador, cada algoritmo arroja un grupo de personas con las probabilidades más altas y después de una evaluación, se toma una decisión. El objetivo de este artículo es tomar ventaja de los tres algoritmos

Evolutionary decision rules for predicting protein contact maps

Protein structure prediction is currently one of the main open challenges in Bioinformatics. The protein contact map is an useful, and commonly used, represen tation for protein 3D structure and represents binary proximities (contact or non-contact) between each pair of amino acids of a protein. In this work, we propose a multi objective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. The rules obtained by the algorithm impose a set of conditions based on amino acid properties to predict contacts. We present results obtained by our approach on four different protein data sets. A statistical study was also performed to extract valid conclusions from the set of prediction rules generated by our algorithm. Results obtained confirm the validity of our proposal

Short-Range Interactions and Decision Tree-Based Protein Contact Map Predictor

In this paper, we focus on protein contact map prediction, one of the most important intermediate steps of the protein folding prob lem. The objective of this research is to know how short-range interac tions can contribute to a system based on decision trees to learn about the correlation among the covalent structures of a protein residues. We propose a solution to predict protein contact maps that combines the use of decision trees with a new input codification for short-range in teractions. The method’s performance was very satisfactory, improving the accuracy instead using all information of the protein sequence. For a globulin data set the method can predict contacts with a maximal accu racy of 43%. The presented predictive model illustrates that short-range interactions play the predominant role in determining protein structur