{"id":1837,"date":"2023-03-02T00:18:09","date_gmt":"2023-03-02T00:18:09","guid":{"rendered":"https:\/\/securitybriefing.net\/?p=1520"},"modified":"2023-03-02T00:18:09","modified_gmt":"2023-03-02T00:18:09","slug":"effektiv-multistadie-phishing-websted-detektion-model","status":"publish","type":"post","link":"https:\/\/securitybriefing.net\/da\/phishing\/effektiv-multistadie-phishing-websted-detektion-model\/","title":{"rendered":"Effektiv model til detektering af phishing-hjemmesider i flere trin"},"content":{"rendered":"

Phishing er den mest almindelige metode for cyberkriminelle til at stj\u00e6le information i dag, og denne cybertrussel bliver v\u00e6rre, da flere og flere rapporter kommer ind om brud p\u00e5 privatlivets fred og \u00f8konomiske tab for\u00e5rsaget af denne type cyberangreb. Det er vigtigt at huske, at den m\u00e5de, ODS opdager phishing p\u00e5, ikke fuldt ud ser p\u00e5, hvad der f\u00e5r phishing til at fungere.<\/p>\n\n\n\n

Desuden fungerer detektionsmodellerne kun godt p\u00e5 et lille antal datas\u00e6t. De skal forbedres, f\u00f8r de kan bruges i det virkelige webmilj\u00f8. P\u00e5 grund af dette \u00f8nsker brugerne nye m\u00e5der til hurtigt og pr\u00e6cist at finde phishing-websteder. Til dette tilbyder principperne for social engineering interessante punkter, der kan bruges til at skabe effektive m\u00e5der at opdage phishing-websteder p\u00e5 forskellige stadier, is\u00e6r p\u00e5 det virkelige web.<\/p>\n\n\n

Historien om Phishing<\/h2>\n\n\n

Husk, at phishing er en almindelig type social engineering-angreb, hvilket betyder, at hackere bruger folks naturlige instinkter, tillid, frygt og gr\u00e5dighed til at narre dem til at g\u00f8re d\u00e5rlige ting. Forskning i cybersikkerhed viser, at phishing steg med 350% under COVID-19 karant\u00e6nen<\/a>. Det ansl\u00e5s, at omkostningerne ved phishing nu er 1\/4 af omkostningerne ved traditionelle cyberangreb, men indt\u00e6gterne er dobbelt s\u00e5 h\u00f8je som tidligere. Phishing-angreb koster mellemstore virksomheder i gennemsnit $1,6 millioner at h\u00e5ndtere, fordi denne cybertrussel g\u00f8r det lettere at miste kunder end at vinde dem.<\/p>\n\n\n\n

Phishing-angreb kan se forskellige ud og bruger normalt en r\u00e6kke m\u00e5der at kommunikere med folk p\u00e5, s\u00e5som e-mail, tekstbeskeder og sociale medier. Uanset hvilken kanal der bruges, udgiver angribere sig ofte for at v\u00e6re kendte banker, kreditkortselskaber eller e-handelswebsteder for at skr\u00e6mme brugere til at logge ind p\u00e5 phishing-siden og g\u00f8re ting, de senere vil fortryde, eller for at tvinge dem til at g\u00f8re det.<\/p>\n\n\n\n

For eksempel kan en bruger igen f\u00e5 en \u00f8jeblikkelig besked om, at der er et problem med deres bankkonto og blive sendt til et weblink, der ligner meget det link, banken bruger. Brugeren t\u00e6nker ikke to gange om at indtaste sit brugernavn og adgangskode i de felter, som kriminelle giver ham. Han noterer sig hendes oplysninger, som han derefter bruger til at f\u00e5 adgang til brugerens session.<\/p>\n\n\n

Hvad b\u00f8r forbedres i Antiphishing-metoder?<\/h2>\n\n\n

For at besvare dette sp\u00f8rgsm\u00e5l skal vi forst\u00e5 den typiske phishing-proces, som er vist i f\u00f8lgende figur.<\/p>\n\n\n\n

\"Hvad<\/figure>\n\n\n\n

Den nuv\u00e6rende mainstream antiphishing-metode er maskinl\u00e6ringsbaseret phishing-webstedsdetektion. Denne online detektionsmetode, som er baseret p\u00e5 statistisk l\u00e6ring, er den prim\u00e6re antiphishing-metode; dog skal dens robusthed og effektivitet i komplekse webmilj\u00f8er forbedres. De vigtigste problemer med maskinl\u00e6ringsbaserede antiphishing-metoder er opsummeret nedenfor.<\/p>\n\n\n\n

\u2022 Et stigende antal funktioner fjernes af antiphishing-metoder, men det er ikke klart, hvorfor disse funktioner fjernes. Eksisterende funktioner afspejler ikke n\u00f8jagtigt phishingens natur, som bruger spoofing til at stj\u00e6le f\u00f8lsomme oplysninger. Som et resultat bliver funktionerne kun gyldige i f\u00e5 begr\u00e6nsede og specifikke scenarier, s\u00e5som specifikke datas\u00e6t eller en browser-plugin.<\/p>\n\n\n\n

\u2022 Eksisterende algoritmer behandler alle websteder p\u00e5 samme m\u00e5de, hvilket resulterer i, at den statistiske model er ineffektiv. Med andre ord er modellerne uegnede til brug i et virkeligt webmilj\u00f8 med et stort antal komplekse websider.<\/p>\n\n\n\n

\u2022 De fleste datas\u00e6t har ikke nok pr\u00f8ver, og mangfoldigheden af pr\u00f8verne tages ikke i betragtning. Desuden er forholdet mellem positive og negative pr\u00f8ver ikke realistisk. Generelt har modeller bygget p\u00e5 denne slags datas\u00e6t meget overtilpasning, og modellernes robusthed skal forbedres.<\/p>\n\n\n

Hvad har v\u00e6ret fremskridtet i Antiphishing-metoder?<\/h2>\n\n\n

I de seneste \u00e5r har folk fors\u00f8gt at udvikle en storstilet, p\u00e5lidelig og effektiv anti-phishing-metode, der fungerer i et virkeligt webmilj\u00f8. Denne metode er baseret p\u00e5 statistiske maskinl\u00e6ringsalgoritmer, der forbedres af f\u00f8lgende tests:<\/p>\n\n\n\n

\u2022 Ved hj\u00e6lp af en detaljeret analyse af m\u00f8nsteret af phishing-angreb for at finde statistiske funktioner til anti-phishing. Funktionerne \u201cforfalskning\u201d, \u201ctilknytning\u201d, \u201ctyveri\u201d og \u201cvurdering\u201d udtr\u00e6kkes alle af de nuv\u00e6rende modeller. Disse kaldes \u201cCASE\u201d-funktioner<\/a>. Denne model viser, hvordan social engineering bruges i phishing-angreb, og hvor relevant og godt indholdet er p\u00e5 nettet. CASE-modellen tager h\u00f8jde for, at spoofing er en del af phishing, sikrer, at funktioner kan skelnes og generaliseres, og giver st\u00f8tte p\u00e5 funktionsniveau til effektiv phishing-detektion.<\/p>\n\n\n\n

\u2022 Fordi legitime og phishing-websteder ikke har den samme m\u00e6ngde trafik, er de nuv\u00e6rende detektionsmodeller baseret p\u00e5 et sikkerhedssystem i flere trin. Ideen bag modeller med flere detektionstrin er at sikre \u201churtig filtrering og pr\u00e6cis genkendelse\u201d. Under det hurtige filtertrin sorteres legitime websteder fra. N\u00f8jagtig overv\u00e5get genkendelse udf\u00f8res derefter ved at l\u00e6re specifikke positive og negative pr\u00f8ver i et mindre omr\u00e5de. Denne detektionsfilosofi sikrer h\u00f8j ydeevne med en kortere detektionstid, hvilket er mere realistisk for det virkelige web.<\/p>\n\n\n\n

\u2022 De nye Antiphishing-modeller er baseret p\u00e5 at bygge datas\u00e6t, der er s\u00e5 t\u00e6t som muligt p\u00e5 det virkelige webmilj\u00f8, med forskellige sprog, indholdskvaliteter og m\u00e6rker. Desuden, da Phishing-detektion er et klasse-ubalanceret problem, menes mange positive og negative pr\u00f8ver nu at v\u00e6re blandet sammen, hvilket g\u00f8r dem meget sv\u00e6re at finde. Alle disse ting g\u00f8r det sv\u00e6rere at finde for at lave Antiphishing-detektion modeller, der fungerer godt og kan bruges i et virkeligt webmilj\u00f8.<\/p>\n\n\n\n

Selv mange Antiphishing-modeller fungerer ved at sammenligne URL'er, titler, links, login-bokse, copyright-information, fortrolige vilk\u00e5r, s\u00f8gemaskineinformation og endda logoerne p\u00e5 webstedernes m\u00e6rker, og det er blevet vist, at de kan bruges til at finde phishing-websteder. I de seneste \u00e5r er der blevet lagt mere v\u00e6gt p\u00e5 visuelle spoofing-funktioner og evalueringsfunktioner. Men de har ikke v\u00e6ret st\u00e6rke nok til at afg\u00f8re, om et websted er et Phishing-websted.<\/p>\n\n\n\n

\u201cMere end 98% af phishing-websteder bruger falske dom\u00e6nenavne<\/a>,\u201d <\/em>siger APWG. For antiphishing-modeller bruger forskere information fra URL-strengen. Det er dog ogs\u00e5 meget vigtigt for phishing-detektion at f\u00e5 informationen bag dom\u00e6nenavnet, s\u00e5som dom\u00e6neregistrering og opl\u00f8sning. Denne information kan ofte vise, om et dom\u00e6nenavn har lov til at tilbyde tjenester relateret til et m\u00e6rke. S\u00e5 den vigtigste opgave for antiphishing-forskere er at finde effektive funktioner. De g\u00f8r dette ved at se p\u00e5 social engineering-angreb og foresl\u00e5 en komplet og letforst\u00e5elig funktionsramme, der d\u00e6kker ikke kun alle aspekter af phishing-angreb, men ogs\u00e5 kvaliteten og relevansen af webindhold.<\/p>\n\n\n\n

Multistage phishing-detektion<\/strong><\/p>\n\n\n\n

Trin 1<\/strong>: Hvidliste-filtrering P\u00e5 dette trin adskilles rigtige websider fra mist\u00e6nkelige baseret p\u00e5 webstedets phishing-dom\u00e6nenavn for m\u00e5lbrandet.<\/p>\n\n\n\n

Trin 2<\/strong>: Dette er stadiet for hurtigt at slippe af med falske regninger. Dette inkluderer at slippe af med den falske titelfunktion, den falske tekstfunktion og den falske visuelle funktion ved hj\u00e6lp af en detektionsmodel. Det sidste trin er at udtr\u00e6kke og kombinere funktionerne for n\u00f8jagtig genkendelse af forfalskning, tyveri, tilknytning, evaluering, tr\u00e6ning og detektion af modeller ved hj\u00e6lp af CASE-funktionen, som er en tr\u00e6ningsbaseret phishing-detektion baseret p\u00e5 \u00e6ndrede modeller.<\/p>\n\n\n

Som konklusion<\/h2>\n\n\n

Med denne tilgang til at stoppe phishing, som er baseret p\u00e5 multi-skala detektion, vil der v\u00e6re 883 phishing-angreb p\u00e5 China Mobile, 86 p\u00e5 Bank of China, 19 p\u00e5 Facebook og 13 p\u00e5 Apple i 2022. Dette demonstrerer, at CASE-modellen d\u00e6kker funktionsrummet, der afspejler phishingens spoofing-natur, sikrer, at funktioner kan skelnes og generaliseres, og giver effektiv phishing-detektion st\u00f8tte p\u00e5 funktionsniveau.<\/p>","protected":false},"excerpt":{"rendered":"

Phishing er den mest almindelige metode for cyberkriminelle til at stj\u00e6le oplysninger i dag, og denne cybertrussel bliver v\u00e6rre, efterh\u00e5nden som der kommer flere og flere rapporter om l\u00e6kager af privatlivets fred og finansielle... Read more Effektiv model til detektering af phishing-hjemmesider i flere trin<\/span><\/a><\/p>","protected":false},"author":1,"featured_media":1527,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25],"tags":[],"class_list":["post-1837","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-phishing","entry"],"yoast_head":"\nProtect Your Data with an Efficient Phishing Detection Model<\/title>\n<meta name=\"description\" content=\"Learn how a phishing detection model can help you prevent phishing attacks. 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