{"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\/it\/2023\/08\/ai-powered-acoustic-attack-extracts-data-from-keystrokes-with-95-precision\/","title":{"rendered":"L'attacco acustico con intelligenza artificiale estrae dati dalla pressione dei tasti con una precisione di 95%"},"content":{"rendered":"

I ricercatori delle universit\u00e0 britanniche Durham University, University of Surrey e Royal Holloway University of London hanno sviluppato una tecnica innovativa supportata dall'intelligenza artificiale per estrarre i tasti dalle registrazioni acustiche.\u00a0<\/b><\/p>\n

Il <\/span>studio<\/span><\/a> ha sviluppato una tecnica per leggere con successo i tasti della tastiera utilizzando registrazioni audio. <\/span><\/p>\n

Questa tecnica potrebbe consentire agli hacker di analizzare le sequenze di tasti, prendendo il controllo del microfono di un dispositivo e raccogliendo informazioni personali come password, conversazioni private, messaggi e altri dati sensibili.\u00a0<\/span><\/p>\n

I tasti vengono registrati attraverso un microfono ed elaborati e analizzati da un modello di apprendimento automatico (ML) che ne determina la spaziatura e la posizione sulla tastiera.\u00a0<\/span><\/p>\n

Il modello \u00e8 in grado di identificare i singoli tasti premuti con un tasso di precisione sconvolgente di 95% quando i tasti vengono registrati attraverso il microfono di un telefono vicino. L'accuratezza della previsione \u00e8 scesa a 93% quando le registrazioni effettuate tramite Zoom sono state utilizzate per addestrare l'algoritmo di classificazione del suono.\u00a0<\/span><\/p>\n

Gli attacchi di hacking acustico sono diventati sempre pi\u00f9 sofisticati grazie alla disponibilit\u00e0 diffusa di dispositivi dotati di microfoni in grado di catturare audio di alta qualit\u00e0.\u00a0<\/span><\/p>\n

Come funziona il modello?<\/span><\/h2>\n

L'attacco inizia registrando le battute sulla tastiera dell'obiettivo. Questi dati sono fondamentali per l'addestramento dell'algoritmo predittivo.\u00a0<\/span><\/p>\n

La registrazione pu\u00f2 essere effettuata utilizzando un microfono nelle vicinanze o il telefono infettato da malware dell'obiettivo con accesso al suo microfono.\u00a0<\/span><\/p>\n

In alternativa, un partecipante disonesto a una chiamata Zoom potrebbe correlare i messaggi digitati dall'obiettivo con la sua registrazione audio. \u00c8 inoltre possibile violare il microfono del computer utilizzando malware o vulnerabilit\u00e0 del software.\u00a0<\/span><\/p>\n

I ricercatori hanno raccolto i dati dell'addestramento premendo 25 volte ciascuno dei 36 tasti di un moderno MacBook Pro, registrando il suono generato da ciascun tasto. Le forme d'onda e gli spettrogrammi sono stati prodotti da queste registrazioni, visualizzando le variazioni identificabili per ogni tasto.\u00a0<\/span><\/p>\n

\"Tasti
I ricercatori hanno elaborato le registrazioni audio in spettrogrammi elaborati da un modello di apprendimento automatico (ML). Fonte: Un attacco pratico ai canali laterali acustici delle tastiere basato sul Deep Learning<\/a>.<\/figcaption><\/figure>\n

\u00c8 stata eseguita un'ulteriore elaborazione dei dati per migliorare i segnali utilizzati per l'identificazione dei tasti premuti.<\/span><\/p>\n

Queste immagini di spettrogrammi sono state utilizzate per addestrare \"CoAtNet\", un classificatore di immagini che attribuisce diversi spettrogrammi audio a diverse sequenze di tasti.\u00a0<\/span><\/p>\n

Nei loro test, i ricercatori hanno utilizzato un computer portatile con una tastiera simile ai pi\u00f9 recenti laptop Apple. I microfoni e i metodi di registrazione comprendevano un iPhone 13 Mini posto a 17 cm di distanza dall'obiettivo, Zoom e Skype.<\/span><\/p>\n

Il classificatore CoANet ha dimostrato un'accuratezza di 95% dalle registrazioni su smartphone e di 93% da quelle acquisite tramite Zoom. Sono state inoltre testate le sequenze di tasti registrate tramite Skype, che hanno dato un'accuratezza di 91,7%.<\/span><\/p>\n

Il documento di ricerca suggerisce di modificare gli stili di digitazione o di utilizzare password randomizzate per prevenire tali attacchi, ma ci\u00f2 \u00e8 tutt'altro che pratico.\u00a0<\/span><\/p>\n

Altre potenziali misure difensive includono l'impiego di software per riprodurre i suoni dei tasti, il rumore bianco o i filtri audio dei tasti basati su software. <\/span>Tuttavia, lo studio ha rilevato che anche una tastiera silenziosa pu\u00f2 essere analizzata con successo alla ricerca di sequenze di tasti.\u00a0<\/span><\/p>\n

Si tratta di un altro esempio inedito di come l'apprendimento automatico possa consentire tecniche di frode sofisticate<\/a>. A studio recente<\/a> ha scoperto che le voci audio false e profonde possono ingannare fino a 25% di persone.\u00a0<\/span><\/p>\n

Gli attacchi audio potrebbero essere rivolti a persone di alto profilo, come politici e amministratori delegati, per rubare informazioni sensibili o lanciare attacchi ransomware basati sulle conversazioni rubate.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Ricercatori delle universit\u00e0 britanniche Durham University, University of Surrey e Royal Holloway University of London hanno sviluppato una tecnica innovativa supportata dall'intelligenza artificiale per estrarre i tasti dalle registrazioni acustiche. Lo studio ha sviluppato una tecnica per leggere con successo i tasti delle tastiere utilizzando registrazioni audio. Questa tecnica potrebbe consentire agli hacker di analizzare i tasti premuti impossessandosi del microfono di un dispositivo e raccogliendo informazioni personali come password, conversazioni private, messaggi e altri dati sensibili. Le battute dei tasti vengono registrate attraverso un microfono ed elaborate e analizzate da un modello di apprendimento automatico (ML) che ne determina la spaziatura e la posizione sulla tastiera. Il modello \u00e8 in grado di identificare le singole battiture 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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