{"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":"model-eficient-de-detectare-a-site-urilor-phishing-multietapa","status":"publish","type":"post","link":"https:\/\/securitybriefing.net\/ro\/phishing\/model-eficient-de-detectare-a-site-urilor-phishing-multietapa\/","title":{"rendered":"Model eficient de detectare a site-urilor Phishing \u00een mai multe etape"},"content":{"rendered":"

Phishingul este cea mai comun\u0103 metod\u0103 prin care infractorii cibernetici fur\u0103 informa\u021bii ast\u0103zi, iar aceast\u0103 amenin\u021bare cibernetic\u0103 se \u00eenr\u0103ut\u0103\u021be\u0219te pe m\u0103sur\u0103 ce tot mai multe rapoarte apar despre scurgeri de confiden\u021bialitate \u0219i pierderi financiare cauzate de acest tip de atac cibernetic. Este important s\u0103 \u021binem cont de faptul c\u0103 modul \u00een care ODS detecteaz\u0103 phishingul nu analizeaz\u0103 pe deplin ceea ce face ca phishingul s\u0103 func\u021bioneze.<\/p>\n\n\n\n

De asemenea, modelele de detectare func\u021bioneaz\u0103 bine doar pe un num\u0103r mic de seturi de date. Ele trebuie \u00eembun\u0103t\u0103\u021bite \u00eenainte de a putea fi utilizate \u00een mediul real de web. Din acest motiv, utilizatorii doresc noi modalit\u0103\u021bi de a g\u0103si rapid \u0219i precis site-urile de phishing. Pentru aceasta, principiile ingineriei sociale ofer\u0103 puncte interesante care pot fi folosite pentru a crea modalit\u0103\u021bi eficiente de a identifica site-urile de phishing \u00een diferite etape, \u00een special pe web-ul real.<\/p>\n\n\n

Istoria Phishingului<\/h2>\n\n\n

\u021aine\u021bi cont de faptul c\u0103 phishingul este un tip comun de atac de inginerie social\u0103, ceea ce \u00eenseamn\u0103 c\u0103 hackerii folosesc instinctele naturale, \u00eencrederea, frica \u0219i l\u0103comia oamenilor pentru a-i p\u0103c\u0103li s\u0103 fac\u0103 lucruri rele. Cercet\u0103rile \u00een domeniul securit\u0103\u021bii cibernetice arat\u0103 c\u0103 phishingul a crescut cu 350% \u00een timpul carantinei COVID-19<\/a>. Se crede c\u0103 costul phishingului este acum 1\/4 din costul atacurilor cibernetice tradi\u021bionale, dar veniturile sunt duble fa\u021b\u0103 de cele din trecut. Atacurile de phishing cost\u0103 companiile de dimensiuni medii \u00een medie $1,6 milioane de dolari pentru a le gestiona, deoarece aceast\u0103 amenin\u021bare cibernetic\u0103 face mai u\u0219or s\u0103 pierzi clien\u021bi dec\u00e2t s\u0103-i c\u00e2\u0219tigi.<\/p>\n\n\n\n

Atacurile de phishing pot ar\u0103ta diferit \u0219i de obicei folosesc o serie de modalit\u0103\u021bi de a comunica cu oamenii, cum ar fi e-mailul, mesajele text \u0219i re\u021belele sociale. Indiferent de canalul utilizat, atacatorii se prefac adesea a fi b\u0103nci cunoscute, companii de carduri de credit sau site-uri de comer\u021b electronic pentru a speria utilizatorii s\u0103 se conecteze la site-ul de phishing \u0219i s\u0103 fac\u0103 lucruri pe care le vor regreta mai t\u00e2rziu sau pentru a-i for\u021ba s\u0103 fac\u0103 acest lucru.<\/p>\n\n\n\n

De exemplu, un utilizator ar putea primi din nou un mesaj instantaneu care spune c\u0103 exist\u0103 o problem\u0103 cu contul s\u0103u bancar \u0219i s\u0103 fie trimis la un link web care seam\u0103n\u0103 foarte mult cu linkul folosit de banc\u0103. Utilizatorul nu se g\u00e2nde\u0219te de dou\u0103 ori \u00eenainte de a introduce numele de utilizator \u0219i parola \u00een c\u00e2mpurile oferite de criminal. El noteaz\u0103 informa\u021biile ei, pe care le folose\u0219te apoi pentru a accesa sesiunea utilizatorului.<\/p>\n\n\n

Ce ar trebui \u00eembun\u0103t\u0103\u021bit \u00een metodele de Antiphishing?<\/h2>\n\n\n

Pentru a r\u0103spunde la aceast\u0103 \u00eentrebare, trebuie s\u0103 \u00een\u021belegem procesul tipic de phishing, care este prezentat \u00een figura urm\u0103toare.<\/p>\n\n\n\n

\"Ce<\/figure>\n\n\n\n

Metoda actual\u0103 principal\u0103 de antiphishing este detectarea site-urilor de phishing bazat\u0103 pe \u00eenv\u0103\u021barea automat\u0103. Acest mod de detectare online, care se bazeaz\u0103 pe \u00eenv\u0103\u021barea statistic\u0103, este metoda principal\u0103 de antiphishing; cu toate acestea, robuste\u021bea \u0219i eficien\u021ba sa \u00een medii web complexe trebuie \u00eembun\u0103t\u0103\u021bite. Problemele principale cu metodele de antiphishing bazate pe \u00eenv\u0103\u021barea automat\u0103 sunt rezumate mai jos.<\/p>\n\n\n\n

\u2022 Un num\u0103r tot mai mare de caracteristici sunt eliminate de metodele de antiphishing, dar nu este clar de ce aceste caracteristici sunt eliminate. Caracteristicile existente nu reflect\u0103 cu acurate\u021be natura phishingului, care folose\u0219te \u00een\u0219el\u0103ciunea pentru a fura informa\u021bii sensibile. Drept urmare, func\u021biile devin valabile doar \u00een c\u00e2teva scenarii limitate \u0219i specifice, cum ar fi seturi de date specifice sau un plugin de browser.<\/p>\n\n\n\n

\u2022 Algoritmii existen\u021bi trateaz\u0103 toate site-urile web \u00een acela\u0219i mod, rezult\u00e2nd \u00eentr-un model statistic ineficient. Cu alte cuvinte, modelele nu sunt potrivite pentru utilizarea \u00eentr-un mediu web real cu un num\u0103r mare de pagini web complexe.<\/p>\n\n\n\n

\u2022 Majoritatea seturilor de date nu au suficiente mostre, iar diversitatea mostrelor nu este luat\u0103 \u00een considerare. De asemenea, raportul dintre mostrele pozitive \u0219i cele negative nu este realist. \u00cen general, modelele construite din aceste tipuri de seturi de date au mult overfitting, iar robuste\u021bea modelelor trebuie \u00eembun\u0103t\u0103\u021bit\u0103.<\/p>\n\n\n

Care a fost progresul \u00een metodele de Antiphishing?<\/h2>\n\n\n

\u00cen ultimii ani, s-a \u00eencercat dezvoltarea unei metode de antiphishing la scar\u0103 larg\u0103, fiabil\u0103 \u0219i eficient\u0103, care s\u0103 func\u021bioneze \u00eentr-un mediu web real. Aceast\u0103 metod\u0103 se bazeaz\u0103 pe algoritmi de \u00eenv\u0103\u021bare automat\u0103 statistic\u0103 care sunt \u00eembun\u0103t\u0103\u021bi\u021bi prin urm\u0103toarele teste:<\/p>\n\n\n\n

\u2022 Folosirea unei analize detaliate a tiparului atacurilor de phishing pentru a g\u0103si func\u021bii statistice pentru antiphishing. Func\u021biile \u201ccontrafacere\u201d, \u201cafiliere\u201d, \u201cfurt\u201d \u0219i \u201cevaluare\u201d sunt toate extrase de modelele actuale. Acestea sunt numite \u201cfunc\u021bii \u201dCASE\u201d<\/a>. Acest model arat\u0103 cum este utilizat\u0103 ingineria social\u0103 \u00een atacurile de phishing \u0219i c\u00e2t de relevant \u0219i bun este con\u021binutul pe web. Modelul CASE ia \u00een considerare faptul c\u0103 \u00een\u0219el\u0103ciunea este o parte a phishingului, asigur\u0103 c\u0103 caracteristicile pot fi distinse \u0219i generalizate \u0219i ofer\u0103 suport la nivel de caracteristic\u0103 pentru detectarea eficient\u0103 a phishingului.<\/p>\n\n\n\n

\u2022 Deoarece site-urile web legitime \u0219i cele de phishing nu au acela\u0219i volum de trafic, modelele actuale de detectare se bazeaz\u0103 pe un sistem de securitate multietap\u0103. Ideea din spatele modelelor cu mai multe etape de detectare este de a asigura \u201cfiltrarea rapid\u0103 \u0219i recunoa\u0219terea precis\u0103\u201d. \u00cen timpul etapei de filtrare rapid\u0103, site-urile web legitime sunt eliminate. Recunoa\u0219terea supravegheat\u0103 precis\u0103 este apoi realizat\u0103 prin \u00eenv\u0103\u021barea unor mostre pozitive \u0219i negative specifice \u00eentr-un interval mai mic. Aceast\u0103 filozofie de detectare asigur\u0103 o performan\u021b\u0103 ridicat\u0103 cu un timp de detectare mai scurt, ceea ce este mai realist pentru web-ul real.<\/p>\n\n\n\n

\u2022 Noile modele de Antiphishing se bazeaz\u0103 pe construirea de seturi de date care sunt c\u00e2t mai aproape posibil de mediul web real, cu limbi diferite, calit\u0103\u021bi de con\u021binut \u0219i m\u0103rci. De asemenea, deoarece detectarea phishingului este o problem\u0103 de dezechilibru de clas\u0103, acum se consider\u0103 c\u0103 multe mostre pozitive \u0219i negative sunt amestecate, ceea ce le face foarte greu de g\u0103sit. Toate aceste lucruri fac mai dificil\u0103 g\u0103sirea pentru a crea modele de detectare a Antiphishingului care s\u0103 func\u021bioneze bine \u0219i s\u0103 poat\u0103 fi utilizate \u00eentr-un mediu web real.<\/p>\n\n\n\n

Chiar \u0219i multe modele de Antiphishing func\u021bioneaz\u0103 prin compararea URL-urilor, titlurilor, linkurilor, casetelor de autentificare, informa\u021biilor despre drepturile de autor, termenilor confiden\u021biali, informa\u021biilor motoarelor de c\u0103utare \u0219i chiar a logo-urilor m\u0103rcilor site-urilor web, \u0219i s-a demonstrat c\u0103 pot fi folosite pentru a g\u0103si site-uri de phishing. \u00cen ultimii ani, s-a acordat mai mult\u0103 aten\u021bie caracteristicilor de \u00een\u0219el\u0103ciune vizual\u0103 \u0219i caracteristicilor de evaluare. Dar nu au fost suficient de puternice pentru a spune dac\u0103 un site este un site de phishing.<\/p>\n\n\n\n

\u201cPeste 98% din site-urile de phishing folosesc nume de domenii false<\/a>,\u201d <\/em>spune APWG. Pentru modelele de antiphishing, cercet\u0103torii folosesc informa\u021bii din \u0219irul URL. Cu toate acestea, ob\u021binerea informa\u021biilor din spatele numelui de domeniu, cum ar fi \u00eenregistrarea \u0219i rezolu\u021bia domeniului, este de asemenea foarte important\u0103 pentru detectarea phishingului. Aceste informa\u021bii pot ar\u0103ta adesea dac\u0103 un nume de domeniu este permis s\u0103 ofere servicii legate de o marc\u0103. A\u0219adar, sarcina principal\u0103 a cercet\u0103torilor \u00een domeniul antiphishing este s\u0103 g\u0103seasc\u0103 caracteristici eficiente. Ei fac acest lucru analiz\u00e2nd atacurile de inginerie social\u0103 \u0219i propun\u00e2nd un cadru de caracteristici complet \u0219i u\u0219or de \u00een\u021beles care acoper\u0103 nu doar toate aspectele atacurilor de phishing, ci \u0219i calitatea \u0219i relevan\u021ba con\u021binutului web.<\/p>\n\n\n\n

Detectarea phishingului \u00een mai multe etape<\/strong><\/p>\n\n\n\n

Etapa 1<\/strong>: Filtrarea pe lista alb\u0103 \u00cen aceast\u0103 etap\u0103, paginile web reale sunt separate de cele suspecte pe baza numelui de domeniu de phishing al site-ului pentru marca \u021bint\u0103.<\/p>\n\n\n\n

Etapa 2<\/strong>: Aceasta este etapa de eliminare rapid\u0103 a facturilor false. Aceasta include eliminarea func\u021biei de titlu fals, func\u021biei de text fals \u0219i func\u021biei vizuale false folosind un model de detectare. Ultimul pas este extragerea \u0219i combinarea func\u021biilor de recunoa\u0219tere precis\u0103 a contrafacerii, furtului, afilierii, evalu\u0103rii, instruirii \u0219i detect\u0103rii modelelor folosind func\u021bia CASE, care este o detectare a phishingului bazat\u0103 pe instruire pe modele modificate.<\/p>\n\n\n

\u00cen concluzie<\/h2>\n\n\n

Cu aceast\u0103 abordare de oprire a phishingului, care se bazeaz\u0103 pe detectarea la scar\u0103 multipl\u0103, vor exista 883 de atacuri de phishing asupra China Mobile, 86 asupra Bank of China, 19 asupra Facebook \u0219i 13 asupra Apple \u00een 2022. demonstr\u00e2nd c\u0103 modelul CASE acoper\u0103 spa\u021biul de caracteristici care reflect\u0103 natura de \u00een\u0219el\u0103ciune a phishingului, asigur\u00e2ndu-se c\u0103 caracteristicile pot fi distinse \u0219i generalizate \u0219i oferind suport eficient pentru detectarea phishingului la nivel de caracteristic\u0103.<\/p>","protected":false},"excerpt":{"rendered":"

Phishing-ul este cea mai comun\u0103 metod\u0103 prin care infractorii cibernetici fur\u0103 informa\u021bii \u00een prezent, iar aceast\u0103 amenin\u021bare cibernetic\u0103 se agraveaz\u0103 pe m\u0103sur\u0103 ce apar tot mai multe rapoarte cu privire la scurgeri de informa\u021bii... Continue reading Model eficient de detectare a site-urilor Phishing \u00een mai multe etape<\/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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