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El internet de las cosas, en ingles Internet of Things -IoT es un concepto utilizado en los últimos años para hacer referencia a la conexión de objetos, personas, que puedan interactuar entre sí; a través de la red de internet. Sin embargo, la construcción de viviendas y edificaciones residenciales es un sector que ha evolucionado a través del tiempo debido a los avances tecnológicos; permitiendo la masificación de dispositivos electrónicos utilizados para la automatización de viviendas y edificaciones residenciales. Por consiguiente, en Colombia, se ha generado un segmento de mercado en la comercialización de dispositivos inteligentes para el hogar, que al momento de la instalación; no se contempla una guía para una correcta instalación. Lo anterior, se refleja en la ejecución de reformas de obra civil, la modificación de los diferentes sistemas que hacen parte de una vivienda, ocasionando sobre costos en el proceso de construcción. Por tal razón el siguiente trabajo propone una guía, bajo el concepto del internet de las cosas, que evidencie los procesos de implementación de dispositivos electrónicos. Para la validación de la guía, se desarrolló un prototipo que evidencia los procesos de implementación de un sistema de IoT.Para la ejecución del presente trabajo, se diseñó una metodología la cual está distribuida en 5 fases, que permitió el desarrollo del presente trabajo.

Nowadays, because of its increased popularity, Android is target to a growing number of attacks and malicious applications, with the purpose of stealing private information and consuming credit by subscribing to premium services. Most of the current commercial antivirus solutions use static signatures for malware detection, which may fail to detect different variants of the same malware and zero‐day attacks. In this paper, we present a behavior‐based, dynamic analysis security solution, called Android Malware Detection System, for detecting both well‐known and zero‐day malware. The proposed solution uses a machine learning classifier in order to differentiate between the behaviors of legitimate and malicious applications. In addition, it uses the application statistics for determining its reputation. The final decision is based on a combination of the classifier's result and the application reputation. The solution includes a unique and extensive set of data collectors, which gather application‐specific data that describe the behavior of the monitored application. We evaluated our solution on a set of legitimate and malicious applications and obtained a high accuracy of 0.985. Our system is able to detect zero‐day malware samples that are not detected by current commercial solutions. Our solution outperforms other similar solutions running on mobile devices. Copyright © 2015 John Wiley & Sons, Ltd.

Port knocking is a form of host-to-host communication which relies on deliberately failed TCP connection attempts. The information is encoded into a port sequence. The client attempts to initiate several three-wayhandshakes and receives no reply. These connection attempts are monitored by a daemon which interprets their destination port numbers as data. This mechanism has vulnerabilities that can be exploited by hackers with the help of data sniffed off the network. Through synchronization, these vulnerabilities can be minimized. A knock sequence is less vulnerable to replay and brute force attacks if its lifespan is shorter. All the entities involved in the knock sequence need to be aware all the time of the knock sequence that can be used. For this, it is required that clients and server share the same time. Both possess the means of the determining the sequence, which consists of a hash based on a preshared key, time value, client IP address and destination port. We present the means to achieve the client-server synchronization and describe an application that implements this.