Building malware classificators usable by State security agencies

Useche-Peláez, David Esteban | 2018

El sandboxing ha sido usado de manera regular para analizar muestras de software y determinar si estas contienen propiedades o comportamientos sospechosos. A pesar de que el sandboxing es una técnica poderosa para desarrollar análisis de malware, esta requiere que un analista de malware desarrolle un análisis riguroso de los resultados para determinar la naturaleza de la muestra: goodware o malware. Este artículo propone dos modelos de aprendizaje automáticos capaces de clasificar muestras con base a un análisis de firmas o permisos extraídos por medio de Cuckoo sandbox, Androguard y VirusTotal. En este artículo también se presenta una propuesta de arquitectura de centinela IoT que protege dispositivos IoT, usando uno de los modelos de aprendizaje automáticos desarrollados anteriormente. Finalmente, diferentes enfoques y perspectivas acerca del uso de sandboxing y aprendizaje automático por parte de agencias de seguridad del Estado también son aportados.

Ciudades inteligentes: Antecedentes, perspectivas, retos y oportunidades

Ferro Escobar, Roberto | 2019

COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things

Nespoli, Pantaleone | 2019

The Internet of Things (IoT) became established during the last decade as an emerging technology with considerable potentialities and applicability. Its paradigm of everything connected together penetrated the real world, with smart devices located in several daily appliances. Such intelligent objects are able to communicate autonomously through already existing network infrastructures, thus generating a more concrete integration between real world and computer-based systems. On the downside, the great benefit carried by the IoT paradigm in our life brings simultaneously severe security issues, since the information exchanged among the objects frequently remains unprotected from malicious attackers. The paper at hand proposes COSMOS (Collaborative, Seamless and Adaptive Sentinel for the Internet of Things), a novel sentinel to protect smart environments from cyber threats. Our sentinel shields the IoT devices using multiple defensive rings, resulting in a more accurate and robust protection. Additionally, we discuss the current deployment of the sentinel on a commodity device (i.e., Raspberry Pi). Exhaustive experiments are conducted on the sentinel, demonstrating that it performs meticulously even in heavily stressing conditions. Each defensive layer is tested, reaching a remarkable performance, thus proving the applicability of COSMOS in a distributed and dynamic scenario such as IoT. With the aim of easing the enjoyment of the proposed entinel, we further developed a friendly and ease-to-use COSMOS App, so that end-users can manage sentinel(s) directly using their own devices (e.g., smartphone).

Induced EEG activity during the IAPS tests and avEMT in intimate partner violence against women

López López, Juan M. | 2018

Intimate Partner Violence (IPV) against women is a major problem in Colombia. Nowadays the question about the effects of violence on women and the identification of latent risks that affect their health, is increasingly relevant. This article describes a pilot study that aimed to measure electrophysiological signals corresponding to the emotional neurophysiological response of women who had experienced IPV in contrast to those who did not. Six healthy female adults, ranged in age from 18 to 55 years old enrolled in this study. For the measurement we used the International Affective Picture System (IAPS) and an Auditory and Visual Emotional Memory Test (avEMT), and we recorded the EEG signal with a g.Nautilus 32 g.LadyBird. EEG signals acquired from baseline and during the tests were compared. As a result of IAPS test, we found for all the participants a higher power spectrum at low EEG frequencies and a decrease in power as the frequencies increase for baseline and emotional pictures. For the avMET, both groups show a higher power spectrum in the different phases of the task compared with the baseline, with an exception of one participant from the WIPV group who show the opposite tendency. Also, two machine learning models were trained and an accuracy of more than 85% were achieved to classify EEG signals from women who experienced IPV and women who had not. This research is an approach to the phenomenon of violence against women and broadens the understanding of the effects on emotional response and electrophysiological activation in women who have experienced this type of situation.

Machine Learning Prediction of Flexural Behavior of UHPFRC

Abellán García, Joaquín | 2020

To evaluate the possibility of predicting the flexural behaviour of UHPFRC, four analytical models were developed, based on artificial neural networks (ANN), to predict the first cracking tension or Limit of Proportionality (LOP), its corresponding deflection (δLOP), ultimate strength or Modulus of Rupture (MOR), and its corresponding deflection (δMOR) of UHPFRC under bending test. The models that were composed of an input level, one output level, and four hidden levels were developed through the R platform. The input level applied the most significative Principal Components (PC) of a large dimension of input dataset. To avoid overfitting K-fold validation and l2 regularization was used. After the models were created, an improvement based on assembling of models by incorporating the predicted values in the dataset of features. The results indicated that the developed assembling models have a good accuracy for the prediction of the behaviour of UHPFRC under three or four points bending test, even when containing supplementary cementitious materials and hybrid mixture of fibers.

Open source intelligence (OSINT) as support of cybersecurity operations. Use of OSINT in a colombian context and sentiment Analysis

Hernandez Mediná, Martin Jose | 2018

Open source intelligence (OSINT) is used to obtain and analyze information related to adversaries, so it can support risk assessments aimed to prevent damages against critical assets. This paper presents a research about different OSINT technologies and how these can be used to perform cyber intelligence tasks. One of the key components in the operation of OSINT tools are the “transforms”, which are used to establish relations between entities of information from queries to different open sources. A set of transforms addressed to the Colombian context are presented, which were implemented and contributed to the community allowing to the law enforcement agencies to develop information gathering process from Colombian open sources. Additionally, this paper shows the implementation of three machine learning models used to perform sentiment analysis over the information obtained from an adversary. Sentiment analysis can be extremely useful to understand the motivation that an adversary can have and, in this way, define proper cyber defense strategies. Finally, some challenges related to the application of OSINT techniques are identified and described.

TRIS: A Three-Rings IoT Sentinel to Protect Against Cyber-Threats

Useche Pelaez, David | 2018-10

The Internet of Things (IoT) is considered as an emerging technology with considerable potentialities. Its paradigm of everything connected together invaded the real world, with smart objects located in several daily appliances able to communicate autonomously through already existing network infrastructures. On the downside, the great advance carried by IoT in our life brings at the same time serious security issues, since the information flow among the objects remains mainly unprotected from malicious attackers. The paper at hand proposes TRIS, a novel sentinel to protect smart environments from cyber threats. Our sentinel shields the IoT devices using three defense rings, resulting in a more accurate protection. Additionally, we discuss the actual deployment of the sentinel using open-source tools. Exhaustive experiments are conducted on the sentinel, showing that the deployed sentinel performs meticulously even in heavily stressing conditions, thus demonstrating the applicability of TRIS in a distributed and dynamic scenario such as IoT.