{"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\/nb\/2023\/08\/ai-powered-acoustic-attack-extracts-data-from-keystrokes-with-95-precision\/","title":{"rendered":"AI-drevet akustisk angrep trekker ut data fra tastetrykk med 95%-presisjon"},"content":{"rendered":"

Forskere fra de britiske universitetene Durham University, University of Surrey og Royal Holloway University of London har utviklet en innovativ AI-st\u00f8ttet teknikk for \u00e5 trekke ut tastetrykk fra akustiske opptak.\u00a0<\/b><\/p>\n

Den <\/span>studie<\/span><\/a> utviklet en teknikk for \u00e5 kunne lese tastetrykk fra tastaturer ved hjelp av lydopptak. <\/span><\/p>\n

Denne teknikken kan gj\u00f8re det mulig for hackere \u00e5 analysere tastetrykk ved \u00e5 ta over mikrofonen p\u00e5 en enhet og samle inn personlig informasjon som passord, private samtaler, meldinger og andre sensitive data.\u00a0<\/span><\/p>\n

Tastetrykk tas opp via en mikrofon og behandles og analyseres av en maskinl\u00e6ringsmodell (ML) som bestemmer avstanden og plasseringen p\u00e5 tastaturet.\u00a0<\/span><\/p>\n

Modellen kan identifisere individuelle tastetrykk med en sjokkerende n\u00f8yaktighet p\u00e5 95% n\u00e5r tastetrykkene tas opp via en telefonmikrofon i n\u00e6rheten. N\u00f8yaktigheten ble redusert til 93% n\u00e5r opptak gjort via Zoom ble brukt til \u00e5 trene opp lydklassifiseringsalgoritmen.\u00a0<\/span><\/p>\n

Akustiske hackingangrep har blitt stadig mer sofistikerte p\u00e5 grunn av den utbredte tilgjengeligheten av enheter utstyrt med mikrofoner som kan ta opp lyd av h\u00f8y kvalitet.\u00a0<\/span><\/p>\n

Hvordan fungerer modellen?<\/span><\/h2>\n

Angrepet begynner med \u00e5 registrere tastetrykk p\u00e5 m\u00e5lets tastatur. Disse dataene er avgj\u00f8rende for \u00e5 trene opp den prediktive algoritmen.\u00a0<\/span><\/p>\n

Dette opptaket kan gj\u00f8res ved hjelp av en mikrofon i n\u00e6rheten eller ved hjelp av m\u00e5lpersonens skadevareinfiserte telefon med tilgang til mikrofonen.\u00a0<\/span><\/p>\n

Alternativt kan en useri\u00f8s deltaker i en Zoom-samtale korrelere meldinger som er skrevet av m\u00e5let, med lydopptaket deres. Det kan ogs\u00e5 v\u00e6re mulig \u00e5 hacke datamaskinens mikrofon ved hjelp av skadelig programvare eller s\u00e5rbarheter i programvaren.\u00a0<\/span><\/p>\n

Forskerne samlet inn treningsdata ved \u00e5 trykke 25 ganger p\u00e5 hver av de 36 tastene p\u00e5 en moderne MacBook Pro, og registrere lyden som hvert tastetrykk genererte. B\u00f8lgeformer og spektrogrammer ble produsert fra disse opptakene, og visualiserte identifiserbare variasjoner for hver tast.\u00a0<\/span><\/p>\n

\"AI-tastetrykk\"
Forskerne bearbeidet lydopptak til spektrogrammer som ble behandlet av en maskinl\u00e6ringsmodell (ML). Kilde: Et praktisk Deep Learning-basert akustisk sidekanalangrep p\u00e5 tastaturer<\/a>.<\/figcaption><\/figure>\n

Ytterligere databehandling ble utf\u00f8rt for \u00e5 forbedre signalene som ble brukt til \u00e5 identifisere tastetrykk.<\/span><\/p>\n

Disse spektrogrammene ble brukt til \u00e5 trene opp \"CoAtNet\", en bildeklassifiseringsmekanisme som tilordner ulike lydspektrogrammer til ulike tastetrykk.\u00a0<\/span><\/p>\n

I testene brukte forskerne en b\u00e6rbar datamaskin med et tastatur som ligner p\u00e5 nyere b\u00e6rbare Apple-maskiner. Mikrofoner og opptaksmetoder inkluderte en iPhone 13 Mini plassert 17 cm fra m\u00e5let, Zoom og Skype.<\/span><\/p>\n

CoANet-klassifiseringen viste en n\u00f8yaktighet p\u00e5 95% fra smarttelefonopptak og 93% fra opptak gjort via Zoom. De testet ogs\u00e5 tastetrykk tatt opp via Skype, noe som ga en n\u00f8yaktighet p\u00e5 91,7%.<\/span><\/p>\n

Forskningsrapporten foresl\u00e5r \u00e5 endre skrivestil eller bruke randomiserte passord for \u00e5 forhindre slike angrep, men det er langt fra praktisk gjennomf\u00f8rbart.\u00a0<\/span><\/p>\n

Andre mulige forsvarstiltak kan v\u00e6re \u00e5 bruke programvare som reproduserer tastetrykkslyder, hvit st\u00f8y eller programvarebaserte lydfiltre for tastetrykk. <\/span>Studien viste imidlertid at selv et lydl\u00f8st tastatur kunne analyseres for tastetrykk.\u00a0<\/span><\/p>\n

Dette er et annet nytt eksempel p\u00e5 hvordan maskinl\u00e6ring kan muliggj\u00f8re sofistikerte svindelteknikker<\/a>. A fersk unders\u00f8kelse<\/a> fant at dype falske lydstemmer kunne lure s\u00e5 mange som 25% av mennesker.\u00a0<\/span><\/p>\n

Lydangrep kan rettes mot h\u00f8yt profilerte personer som politikere og administrerende direkt\u00f8rer for \u00e5 stjele sensitiv informasjon eller starte l\u00f8sepengevirusangrep basert p\u00e5 stj\u00e5lne samtaler.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Forskere fra de britiske universitetene Durham University, University of Surrey og Royal Holloway University of London har utviklet en innovativ AI-st\u00f8ttet teknikk for \u00e5 utvinne tastetrykk fra lydopptak. I studien ble det utviklet en teknikk for \u00e5 kunne lese tastetrykk fra tastaturer ved hjelp av lydopptak. Denne teknikken kan gj\u00f8re det mulig for hackere \u00e5 analysere tastetrykk ved \u00e5 ta over mikrofonen p\u00e5 en enhet og samle inn personlig informasjon som passord, private samtaler, meldinger og andre sensitive data. Tastetrykk tas opp gjennom en mikrofon og behandles og analyseres av en maskinl\u00e6ringsmodell (ML) som bestemmer avstanden og plasseringen p\u00e5 tastaturet. Modellen kan identifisere individuelle tastetrykk med<\/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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