{"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\/pt\/2023\/08\/ai-powered-acoustic-attack-extracts-data-from-keystrokes-with-95-precision\/","title":{"rendered":"O ataque ac\u00fastico alimentado por IA extrai dados das teclas premidas com uma precis\u00e3o de 95%"},"content":{"rendered":"

Investigadores das universidades brit\u00e2nicas Durham University, University of Surrey e Royal Holloway University of London desenvolveram uma t\u00e9cnica inovadora apoiada por IA para extrair as teclas premidas de grava\u00e7\u00f5es ac\u00fasticas.\u00a0<\/b><\/p>\n

O <\/span>estudo<\/span><\/a> desenvolveu uma t\u00e9cnica para ler com \u00eaxito as teclas premidas nos teclados utilizando grava\u00e7\u00f5es \u00e1udio. <\/span><\/p>\n

Esta t\u00e9cnica pode permitir que os piratas inform\u00e1ticos analisem as teclas premidas, controlando o microfone de um dispositivo e recolhendo informa\u00e7\u00f5es pessoais, como palavras-passe, conversas privadas, mensagens e outros dados sens\u00edveis.\u00a0<\/span><\/p>\n

Os toques nas teclas s\u00e3o gravados atrav\u00e9s de um microfone e processados e analisados por um modelo de aprendizagem autom\u00e1tica (ML) que determina o seu espa\u00e7amento e posicionamento no teclado.\u00a0<\/span><\/p>\n

O modelo consegue identificar toques de tecla individuais com uma taxa de precis\u00e3o chocante de 95% quando os toques s\u00e3o gravados atrav\u00e9s de um microfone de telefone pr\u00f3ximo. A precis\u00e3o da previs\u00e3o diminuiu para 93% quando as grava\u00e7\u00f5es efectuadas atrav\u00e9s do Zoom foram utilizadas para treinar o algoritmo de classifica\u00e7\u00e3o do som.\u00a0<\/span><\/p>\n

Os ataques de pirataria ac\u00fastica tornaram-se cada vez mais sofisticados devido \u00e0 disponibilidade generalizada de dispositivos equipados com microfones que podem captar \u00e1udio de alta qualidade.\u00a0<\/span><\/p>\n

Como \u00e9 que o modelo funciona?<\/span><\/h2>\n

O ataque come\u00e7a por registar as teclas premidas no teclado do alvo. Estes dados s\u00e3o cruciais para treinar o algoritmo de previs\u00e3o.\u00a0<\/span><\/p>\n

Esta grava\u00e7\u00e3o pode ser efectuada utilizando um microfone pr\u00f3ximo ou o telem\u00f3vel infetado com malware do alvo com acesso ao seu microfone.\u00a0<\/span><\/p>\n

Em alternativa, um participante desonesto numa chamada Zoom poderia correlacionar as mensagens digitadas pelo alvo com a sua grava\u00e7\u00e3o de som. Tamb\u00e9m podem existir formas de piratear o microfone do computador utilizando malware ou vulnerabilidades de software.\u00a0<\/span><\/p>\n

Os investigadores recolheram dados de treino premindo 25 vezes cada uma das 36 teclas de um MacBook Pro moderno, gravando o som gerado por cada premir de tecla. As formas de onda e os espectrogramas foram produzidos a partir destas grava\u00e7\u00f5es, visualizando varia\u00e7\u00f5es identific\u00e1veis para cada tecla.\u00a0<\/span><\/p>\n

\"Teclas
Os investigadores transformaram grava\u00e7\u00f5es de \u00e1udio em espectrogramas processados por um modelo de aprendizagem autom\u00e1tica (ML). Fonte: Um ataque pr\u00e1tico de canal lateral ac\u00fastico baseado em aprendizagem profunda em teclados<\/a>.<\/figcaption><\/figure>\n

Foi efectuado um processamento de dados adicional para melhorar os sinais utilizados para identificar as teclas premidas.<\/span><\/p>\n

Estas imagens de espectrogramas foram utilizadas para treinar o \"CoAtNet\", um classificador de imagens que atribui diferentes espectrogramas de \u00e1udio a diferentes toques nas teclas.\u00a0<\/span><\/p>\n

Nos seus testes, os investigadores utilizaram um computador port\u00e1til com um teclado semelhante ao dos computadores port\u00e1teis mais recentes da Apple. Os microfones e m\u00e9todos de grava\u00e7\u00e3o inclu\u00edram um iPhone 13 Mini colocado a 17 cm de dist\u00e2ncia do alvo, Zoom e Skype.<\/span><\/p>\n

O classificador CoANet demonstrou uma precis\u00e3o de 95% a partir de grava\u00e7\u00f5es de smartphones e de 93% a partir de grava\u00e7\u00f5es captadas atrav\u00e9s do Zoom. Tamb\u00e9m foram testadas as teclas gravadas atrav\u00e9s do Skype, o que produziu uma precis\u00e3o de 91,7%.<\/span><\/p>\n

O documento de investiga\u00e7\u00e3o sugere a altera\u00e7\u00e3o dos estilos de escrita ou a utiliza\u00e7\u00e3o de palavras-passe aleat\u00f3rias para evitar tais ataques, mas isso est\u00e1 longe de ser pr\u00e1tico.\u00a0<\/span><\/p>\n

Outras potenciais medidas defensivas incluem a utiliza\u00e7\u00e3o de software para reproduzir sons de teclas, ru\u00eddo branco ou filtros de \u00e1udio de teclas baseados em software. <\/span>No entanto, o estudo concluiu que mesmo um teclado silencioso pode ser analisado com \u00eaxito para detetar as teclas premidas.\u00a0<\/span><\/p>\n

Este \u00e9 outro exemplo inovador de como a aprendizagem autom\u00e1tica pode permitir t\u00e9cnicas sofisticadas de fraude<\/a>. A estudo recente<\/a> descobriram que vozes falsas e profundas podiam enganar at\u00e9 25% de pessoas.\u00a0<\/span><\/p>\n

Os ataques de \u00e1udio podem ser dirigidos a indiv\u00edduos de alto n\u00edvel, como pol\u00edticos e directores executivos, para roubar informa\u00e7\u00f5es sens\u00edveis ou lan\u00e7ar ataques de ransomware com base em conversas roubadas.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Investigadores das universidades brit\u00e2nicas Durham University, University of Surrey e Royal Holloway University of London desenvolveram uma t\u00e9cnica inovadora apoiada por IA para extrair as teclas premidas de grava\u00e7\u00f5es ac\u00fasticas. O estudo desenvolveu uma t\u00e9cnica para ler com \u00eaxito as teclas premidas nos teclados utilizando grava\u00e7\u00f5es \u00e1udio. Esta t\u00e9cnica pode permitir que os hackers analisem as teclas premidas atrav\u00e9s do microfone de um dispositivo e recolham informa\u00e7\u00f5es pessoais, como palavras-passe, conversas privadas, mensagens e outros dados sens\u00edveis. As teclas s\u00e3o gravadas atrav\u00e9s de um microfone e processadas e analisadas por um modelo de aprendizagem autom\u00e1tica (ML) que determina o seu espa\u00e7amento e posicionamento no teclado. O modelo pode identificar toques de tecla individuais com<\/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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