{"id":3933,"date":"2023-08-07T17:58:34","date_gmt":"2023-08-07T17:58:34","guid":{"rendered":"https:\/\/dailyai.com\/?p=3933"},"modified":"2023-08-07T18:08:41","modified_gmt":"2023-08-07T18:08:41","slug":"ai-powered-acoustic-attack-extracts-data-from-keystrokes-with-95-precision","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2023\/08\/ai-powered-acoustic-attack-extracts-data-from-keystrokes-with-95-precision\/","title":{"rendered":"El ataque ac\u00fastico basado en IA extrae datos de las pulsaciones de teclas con una precisi\u00f3n 95%"},"content":{"rendered":"

Investigadores de las universidades brit\u00e1nicas Durham University, Surrey University y Royal Holloway University of London han desarrollado una innovadora t\u00e9cnica basada en inteligencia artificial para extraer las pulsaciones de las grabaciones ac\u00fasticas.\u00a0<\/b><\/p>\n

En <\/span>estudiar<\/span><\/a> ha desarrollado una t\u00e9cnica para leer con \u00e9xito las pulsaciones de los teclados mediante grabaciones de audio. <\/span><\/p>\n

Esta t\u00e9cnica podr\u00eda permitir a los hackers analizar las pulsaciones de teclas apoder\u00e1ndose del micr\u00f3fono de un dispositivo y recopilando informaci\u00f3n personal como contrase\u00f1as, conversaciones privadas, mensajes y otros datos sensibles.\u00a0<\/span><\/p>\n

Las pulsaciones se graban a trav\u00e9s de un micr\u00f3fono y son procesadas y analizadas por un modelo de aprendizaje autom\u00e1tico (ML) que determina su espaciado y posici\u00f3n en el teclado.\u00a0<\/span><\/p>\n

El modelo puede identificar pulsaciones individuales con un sorprendente \u00edndice de precisi\u00f3n de 95% cuando las pulsaciones se graban a trav\u00e9s del micr\u00f3fono de un tel\u00e9fono cercano. La precisi\u00f3n de la predicci\u00f3n disminuy\u00f3 a 93% cuando se utilizaron grabaciones realizadas a trav\u00e9s de Zoom para entrenar el algoritmo de clasificaci\u00f3n de sonidos.\u00a0<\/span><\/p>\n

Los ataques de pirater\u00eda ac\u00fastica se han vuelto cada vez m\u00e1s sofisticados debido a la disponibilidad generalizada de dispositivos equipados con micr\u00f3fonos que pueden capturar audio de alta calidad.\u00a0<\/span><\/p>\n

\u00bfC\u00f3mo funciona el modelo?<\/span><\/h2>\n

El ataque comienza registrando las pulsaciones del teclado del objetivo. Estos datos son cruciales para entrenar el algoritmo predictivo.\u00a0<\/span><\/p>\n

Esta grabaci\u00f3n puede realizarse utilizando un micr\u00f3fono cercano o el tel\u00e9fono infectado con malware del objetivo con acceso a su micr\u00f3fono.\u00a0<\/span><\/p>\n

Alternativamente, un participante deshonesto en una llamada Zoom podr\u00eda correlacionar los mensajes tecleados por el objetivo con su grabaci\u00f3n de sonido. Tambi\u00e9n puede haber formas de piratear el micr\u00f3fono del ordenador utilizando malware o vulnerabilidades de software.\u00a0<\/span><\/p>\n

Los investigadores recopilaron datos de entrenamiento pulsando 25 veces cada una de las 36 teclas de un MacBook Pro moderno y registrando el sonido que generaba cada pulsaci\u00f3n. A partir de estas grabaciones se produjeron formas de onda y espectrogramas que permit\u00edan visualizar las variaciones identificables de cada tecla.\u00a0<\/span><\/p>\n

\"Pulsaciones
Los investigadores procesaron grabaciones de audio en espectrogramas procesados por un modelo de aprendizaje autom\u00e1tico (ML). Fuente: Un ataque ac\u00fastico de canal lateral en teclados basado en aprendizaje profundo pr\u00e1ctico<\/a>.<\/figcaption><\/figure>\n

Se realiz\u00f3 un tratamiento adicional de los datos para mejorar las se\u00f1ales utilizadas para identificar las pulsaciones de teclas.<\/span><\/p>\n

Estas im\u00e1genes de espectrogramas se utilizaron para entrenar 'CoAtNet', un clasificador de im\u00e1genes que atribuye diferentes espectrogramas de audio a distintas pulsaciones de teclas.\u00a0<\/span><\/p>\n

En sus pruebas, los investigadores utilizaron un ordenador port\u00e1til con un teclado similar al de los nuevos port\u00e1tiles de Apple. Los micr\u00f3fonos y m\u00e9todos de grabaci\u00f3n inclu\u00edan un iPhone 13 Mini colocado a 17 cm del objetivo, Zoom y Skype.<\/span><\/p>\n

El clasificador CoANet demostr\u00f3 una precisi\u00f3n de 95% a partir de grabaciones de tel\u00e9fonos inteligentes y de 93% a partir de las capturadas mediante Zoom. Tambi\u00e9n probaron las pulsaciones de teclas grabadas a trav\u00e9s de Skype, que arrojaron una precisi\u00f3n de 91,7%.<\/span><\/p>\n

El documento de investigaci\u00f3n sugiere modificar los estilos de escritura o utilizar contrase\u00f1as aleatorias para evitar estos ataques, pero eso dista mucho de ser pr\u00e1ctico.\u00a0<\/span><\/p>\n

Otras posibles medidas defensivas incluyen el empleo de software para reproducir los sonidos de las pulsaciones de teclas, ruido blanco o filtros de audio de pulsaciones de teclas basados en software. <\/span>Sin embargo, el estudio descubri\u00f3 que incluso un teclado silencioso pod\u00eda analizarse con \u00e9xito en busca de pulsaciones.\u00a0<\/span><\/p>\n

Este es otro ejemplo novedoso de c\u00f3mo el aprendizaje autom\u00e1tico puede permitir sofisticadas t\u00e9cnicas de fraude<\/a>. A estudio reciente<\/a> descubri\u00f3 que las voces de audio falsas y profundas pod\u00edan enga\u00f1ar hasta 25% de personas.\u00a0<\/span><\/p>\n

Los ataques de audio podr\u00edan dirigirse a personas de alto perfil, como pol\u00edticos y directores ejecutivos, para robar informaci\u00f3n sensible o lanzar ataques de ransomware basados en conversaciones robadas.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Investigadores de las universidades brit\u00e1nicas Durham University, University of Surrey y Royal Holloway University of London desarrollaron una innovadora t\u00e9cnica basada en inteligencia artificial para extraer las pulsaciones de las teclas a partir de grabaciones ac\u00fasticas. El estudio desarroll\u00f3 una t\u00e9cnica para leer con \u00e9xito las pulsaciones de los teclados a partir de grabaciones ac\u00fasticas. Esta t\u00e9cnica podr\u00eda permitir a los piratas inform\u00e1ticos analizar las pulsaciones de teclas apoder\u00e1ndose del micr\u00f3fono de un dispositivo y recopilando informaci\u00f3n personal como contrase\u00f1as, conversaciones privadas, mensajes y otros datos sensibles. Las pulsaciones se graban a trav\u00e9s de un micr\u00f3fono y se procesan y analizan mediante un modelo de aprendizaje autom\u00e1tico (ML) que determina su espaciado y posici\u00f3n en el teclado. El modelo puede identificar pulsaciones individuales con<\/p>","protected":false},"author":2,"featured_media":3935,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[161,283,105],"class_list":["post-3933","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-fraud","tag-hackers","tag-machine-learning"],"yoast_head":"\nAI-powered acoustic attack extracts data from keystrokes with 95% precision | DailyAI<\/title>\n<meta name=\"description\" content=\"Researchers from UK universities Durham University, University of Surrey, and Royal Holloway University of London developed an innovative AI-supported technique for extracting keystrokes from acoustic recordings.\u00a0\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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