Juice Jacking: Security Issues and Improvements in USB Technology

For a reliable and convenient system, it is essential to build a secure system that will be protected from outer attacks and also serve the purpose of keeping the inner data safe from intruders. A juice jacking is a popular and spreading cyber-attack that allows intruders to get inside the system through the web and theive potential data from the system. For peripheral communications, Universal Serial Bus (USB) is the most commonly used standard in 5G generation computer systems. USB is not only used for communication, but also to charge gadgets. However, the transferal of data between devices using USB is prone to various security threats. It is necessary to maintain the confidentiality and sensitivity of data on the bus line to maintain integrity. Therefore, in this paper, a juice jacking attack is analyzed, using the maximum possible means through which a system can be affected using USB. Ten different malware attacks are used for experimental purposes. Various machine learning and deep learning models are used to predict malware attacks. An extensive experimental analysis reveals that the deep learning model can efficiently recognize the juice jacking attack. Finally, various techniques are discussed that can either prevent or avoid juice jacking attacks.

MODELO DE APRENDIZAJE PARA LA GENERACIÓN Y ALCANCE COGNITIVO TECNOLÓGICO EN BIOMEDICINA

El objetivo del estudio fue proponer un modelo de aprendizaje para la generación y alcance cognitivo tecnológico en biomedicina. A partir, de considerar las palabras claves: learning model, cognitive technology domain, biomedicine en la plataforma ScienceDirect se realizó una búsqueda de los últimos tres años completos (2018, 2017, 2016), además, de lo publicado hasta la fecha del presente año 2019. Se consideró solamente el artículo de investigación y las revistas: Computer Methods and Programs in Biomedicine; Future Generation Computer Systems, Procedia Computer Science, Computers in Biology and Medicine, Data & Knowledge Engineering, Technological Forecasting and Social Change, Social Science & Medicine. La revistas Computer Methods and Programs in Biomedicine presentaron el mayor número de artículos, encontrándose diferencias estadísticamente significativas con relación al resto pero, no se evidenció artículos que mostraran modelos cognitivos para el aprendizaje tecnológico durante la formación profesional. Se propuso un modelo que inicia con la misión de la docencia, orienta a los problemas sociales prioritarios y estos a su vez, posibilitan desarrollar enfoques pedagógicos para generar información tecnológica y dominio cognitivo tecnológico pudiendo ser una garantía durante el proceso de formación profesional en el campo de la biomedicina.

Artificial Intelligence and Japan’s Fifth Generation

In 1982, Japan launched its Fifth Generation Computer Systems project (FGCS), designed to develop intelligent software that would run on novel computer hardware. As the first national, large-scale artificial intelligence (AI) research and development (R&D) project to be free from military influence and corporate profit motives, the FGCS was open, international, and oriented around public goods. Although the FGCS did not plan any commercialized technologies, many American computer experts portrayed it as an economic threat to U.S. dominance in computing and the global economy—and policymakers around the developed world believed them and funded AI projects of their own. Later, however, the FGCS was remembered as a failure. Why? This article recasts the FGCS as an interstice in the shift from a state-funded regime of American science organization to the neoliberal privatized regime of R&D now ascendant around the world. By exploring how notions of economic competitiveness and national security shaped R&D, this article reveals AI to be a product of contingent choices by multiple actors—nation-states, government bureaucracies, corporations, and individuals—rather than the outcome of deterministic technological forces.