{"id":53278,"date":"2019-11-28T00:00:00","date_gmt":"2019-11-27T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/9-podhodov-dlya-vyyavleniya-anomalij"},"modified":"2020-02-18T14:01:10","modified_gmt":"2020-02-18T11:01:10","slug":"9-podhodov-dlya-vyyavleniya-anomalij","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hu\/blog\/news\/9-podhodov-dlya-vyyavleniya-anomalij","title":{"rendered":"9 megk\u00f6zel\u00edt\u00e9s az anom\u00e1li\u00e1k azonos\u00edt\u00e1s\u00e1ra","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

A el\u0151z\u0151 cikkben<\/a><\/noindex> Besz\u00e9lt\u00fcnk az id\u0151beli adatok el\u0151rejelz\u00e9s\u00e9r\u0151l. Logikus folytat\u00e1sk\u00e9nt j\u00f6het egy cikk az anom\u00e1li\u00e1k azonos\u00edt\u00e1s\u00e1r\u00f3l. <\/p>\n

Alkalmaz\u00e1s<\/h3>\n

\nAz anom\u00e1li\u00e1k azonos\u00edt\u00e1sa olyan ter\u00fcleteken alkalmazhat\u00f3, mint: <\/p>\n

1) Berendez\u00e9sek meghib\u00e1sod\u00e1s\u00e1nak el\u0151rejelz\u00e9se<\/h3>\n

\nP\u00e9ld\u00e1ul 2010-ben az ir\u00e1ni centrifug\u00e1kat a Stuxnet v\u00edrus t\u00e1madta meg, amely nem optim\u00e1lis m\u0171k\u00f6d\u00e9si m\u00f3dba \u00e1ll\u00edtotta a berendez\u00e9seket, \u00e9s a fokozott kop\u00e1s miatt r\u00e9szben t\u00f6nkretette azokat. <\/p>\n

Ha az eszk\u00f6z\u00f6k\u00f6n anom\u00e1li\u00e1k keres\u00e9s\u00e9re alkalmas algoritmusok lettek volna, a meghib\u00e1sod\u00e1s elker\u00fclhet\u0151 lett volna. <\/p>\n

\"9
\n<\/a><\/noindex>
\nAz anom\u00e1li\u00e1k keres\u00e9se nemcsak az atomiparban, hanem a f\u00e9mfeldolgoz\u00e1sban \u00e9s a rep\u00fcl\u0151t\u00e9ri turbin\u00e1k m\u0171k\u00f6d\u00e9s\u00e9ben is haszn\u00e1latos. Tov\u00e1bb\u00e1 m\u00e1s ter\u00fcleteken is, ahol a predikt\u00edv diagnosztika alkalmaz\u00e1sa olcs\u00f3bb a nem tervezett meghib\u00e1sod\u00e1s okozta lehets\u00e9ges vesztes\u00e9gekn\u00e9l.<\/p>\n

2) Csal\u00e1s gyan\u00fas tev\u00e9kenys\u00e9gek el\u0151rejelz\u00e9se<\/h3>\n

\nHa egy Podolskban haszn\u00e1lt k\u00e1rty\u00e1r\u00f3l p\u00e9nzt vesznek fel Alb\u00e1ni\u00e1ban,\u53ef\u80fd \u00e9rdemes a tranzakci\u00f3kat alaposabban ellen\u0151rizni.<\/p>\n

3) Anom\u00e1lis fogyaszt\u00f3i mint\u00e1zatok azonos\u00edt\u00e1sa<\/h3>\n

\nHa egyes \u00fcgyfelek anom\u00e1lis magatart\u00e1st mutatnak, lehet, hogy van egy probl\u00e9ma, amir\u0151l nem tud.<\/p>\n

4) Anom\u00e1lis kereslet \u00e9s terhel\u00e9s azonos\u00edt\u00e1sa<\/h3>\n

\nHa az FMCG boltban a bev\u00e9telek a progn\u00f3zis megb\u00edzhat\u00f3s\u00e1gi hat\u00e1r\u00e9rt\u00e9ke alatt cs\u00f6kkennek, \u00e9rdemes megkeresni az okokat.<\/p>\n

Az anom\u00e1li\u00e1k azonos\u00edt\u00e1s\u00e1nak megk\u00f6zel\u00edt\u00e9sei<\/h2>\n

<\/p>\n

1) Egys\u00e9ges oszt\u00e1ly\u00fa t\u00e1mogat\u00f3 vektorok m\u00f3dszere - One-Class SVM<\/h3>\n

\nAlkalmas, amikor a tanul\u00e1si adathalmazon az adatok norm\u00e1l eloszl\u00e1st k\u00f6vetnek, \u00e9s a tesztel\u00e9s sor\u00e1n anom\u00e1li\u00e1k tal\u00e1lhat\u00f3k.<\/p>\n

Az egys\u00e9ges oszt\u00e1ly\u00fa t\u00e1mogat\u00f3 vektoros m\u00f3dszer nemline\u00e1ris fel\u00fcletet \u00e9p\u00edt a koordin\u00e1ta-rendszer k\u00f6r\u00fcl. Megadhat\u00f3 a hat\u00e1r\u00e9rt\u00e9k, hogy mely adatokat tekints\u00fck anom\u00e1lisnak.<\/p>\n

A DATA4 csapatunk tapasztalatai alapj\u00e1n a One-Class SVM a leggyakrabban haszn\u00e1lt algoritmus az anom\u00e1li\u00e1k keres\u00e9s\u00e9re. <\/p>\n

\"9
\n<\/p>\n

2) Izol\u00e1l\u00f3 erd\u0151 m\u00f3dszere - isolate forest<\/h3>\n

\nA 'v\u00e9letlenszer\u0171' fa\u00e9p\u00edt\u00e9si m\u00f3dszer mellett az extr\u00e9m \u00e9rt\u00e9kek hamarabb el\u00e9rik a leveleket (alacsony fa m\u00e9lys\u00e9g\u00e9n), teh\u00e1t az extr\u00e9m \u00e9rt\u00e9keket k\u00f6nnyebben 'izol\u00e1lhatjuk'. Az anom\u00e1lis \u00e9rt\u00e9kek kiemel\u00e9se az algoritmus els\u0151 iter\u00e1ci\u00f3iban t\u00f6rt\u00e9nik.<\/p>\n

\"9
\n<\/p>\n

3) Elliptikus burkolat \u00e9s statisztikai m\u00f3dszerek<\/h3>\n

\nHaszn\u00e1latos, amikor az adatok norm\u00e1lisan oszlanak el. Min\u00e9l k\u00f6zelebb \u00e1ll a m\u00e9r\u00e9s az eloszl\u00e1si kever\u00e9k fark\u00e1hoz, ann\u00e1l ink\u00e1bb anom\u00e1lis az \u00e9rt\u00e9k.<\/p>\n

Ebben a kateg\u00f3ri\u00e1ba m\u00e1s statisztikai m\u00f3dszerek is besorolhat\u00f3k.<\/p>\n

\"9
\n
\n\"9
\nK\u00e9p a dyakonov.org weboldalr\u00f3l.<\/i><\/p>\n

4) Metrikus m\u00f3dszerek<\/h3>\n

\nA m\u00f3dszerek k\u00f6z\u00e9 tartoznak olyan algoritmusok, mint a k legk\u00f6zelebbi szomsz\u00e9d, k-edik legk\u00f6zelebbi szomsz\u00e9d, ABOD (sz\u00f6galap\u00fa elt\u00e9r\u00e9s \u00e9szlel\u00e9s) vagy LOF (helyi elt\u00e9r\u0151 t\u00e9nyez\u0151).<\/p>\n

Akkor alkalmasak, ha a jellemz\u0151k k\u00f6z\u00f6tti t\u00e1vols\u00e1gok azonosak vagy normaliz\u00e1ltak (hogy ne m\u00e9rj\u00fck a pitont papag\u00e1jban). <\/p>\n

A k legk\u00f6zelebbi szomsz\u00e9d algoritmus azt felt\u00e9telezi, hogy a norm\u00e1lis \u00e9rt\u00e9kek a t\u00f6bbdimenzi\u00f3s t\u00e9r egy adott ter\u00fclet\u00e9n helyezkednek el, m\u00edg az anom\u00e1li\u00e1khoz val\u00f3 t\u00e1vols\u00e1g nagyobb lesz, mint a d\u00f6nt\u0151 hiperfel\u00fclethez. <\/p>\n

\"9
\n<\/p>\n

5) Klaszterez\u00e9si m\u00f3dszerek<\/h3>\n

\nA klaszterez\u00e9si m\u00f3dszerek l\u00e9nyege, hogy ha egy \u00e9rt\u00e9k t\u00e1volabb van a klaszterek k\u00f6z\u00e9p\u00e9rt\u00e9keit\u0151l, mint egy meghat\u00e1rozott nagys\u00e1grend, akkor az \u00e9rt\u00e9k anom\u00e1li\u00e1nak sz\u00e1m\u00edt. <\/p>\n

A legfontosabb a megfelel\u0151en klaszterez\u0151 algoritmus haszn\u00e1lata, ami a konkr\u00e9t feladatt\u00f3l f\u00fcgg. <\/p>\n

\"9
\n<\/p>\n

6) F\u0151komponens m\u00f3dszer<\/h3>\n

\nAhol kiemelik a legnagyobb sz\u00f3r\u00e1sv\u00e1ltoz\u00e1s ir\u00e1nyait.<\/p>\n

7) Id\u0151soros el\u0151rejelz\u0151 algoritmusok<\/h3>\n

\nAz \u00f6tlet az, hogy ha egy \u00e9rt\u00e9k kil\u00f3g a predikci\u00f3s intervallumb\u00f3l, az \u00e9rt\u00e9k anom\u00e1li\u00e1nak sz\u00e1m\u00edt. Id\u0151soros el\u0151rejelz\u00e9shez olyan algoritmusokat haszn\u00e1lnak, mint a tripla sim\u00edt\u00e1s, S(ARIMA), boosting stb.<\/p>\n

Az id\u0151szoros el\u0151rejelz\u00e9si algoritmusokr\u00f3l a kor\u00e1bbi cikkben volt sz\u00f3.<\/p>\n

\"9
\n<\/p>\n

8) Fel\u00fcgyelt tanul\u00e1s (regresszi\u00f3, klasszifik\u00e1ci\u00f3)<\/h3>\n

\nHa az adatok lehet\u0151v\u00e9 teszik, olyan algoritmusokat haszn\u00e1lunk, amelyek a line\u00e1ris regresszi\u00f3t\u00f3l a visszat\u00e9r\u0151 h\u00e1l\u00f3zatokig terjednek. Megm\u00e9rj\u00fck a k\u00fcl\u00f6nbs\u00e9get a predikci\u00f3 \u00e9s a t\u00e9nyleges \u00e9rt\u00e9k k\u00f6z\u00f6tt, \u00e9s levonunk egy k\u00f6vetkeztet\u00e9st arr\u00f3l, hogy az adatok mennyire l\u00f3g ki a norm\u00e1lisb\u00f3l. Fontos, hogy az algoritmusnak megfelel\u0151 \u00e1ltal\u00e1nos\u00edt\u00f3 k\u00e9pess\u00e9ggel rendelkezzen, \u00e9s a tanul\u00f3halmaz ne tartalmazzon anom\u00e1lis \u00e9rt\u00e9keket.<\/p>\n

9) Modelltesztek<\/h3>\n

\nAz anom\u00e1li\u00e1k keres\u00e9s\u00e9t aj\u00e1nl\u00e1sok keres\u00e9sek\u00e9nt k\u00f6zel\u00edtj\u00fck meg. Az SVD vagy faktor\u00e1l\u00e1si g\u00e9pek seg\u00edts\u00e9g\u00e9vel sz\u00e9tdaraboljuk a jellemz\u0151 m\u00e1trixot, \u00e9s az \u00faj m\u00e1trixban l\u00e9v\u0151, l\u00e9nyegesen elt\u00e9r\u0151 \u00e9rt\u00e9keket anom\u00e1li\u00e1nak tekintj\u00fck. <\/p>\n

\"9
\n
\nK\u00e9p a dyakonov.org weboldalr\u00f3l.<\/i><\/p>\n

Z\u00e1r\u00e1s<\/h3>\n

\nEbben a cikkben az anom\u00e1li\u00e1k \u00e9szlel\u00e9s\u00e9nek f\u0151bb megk\u00f6zel\u00edt\u00e9seit vizsg\u00e1ltuk. <\/p>\n

Az anom\u00e1li\u00e1k keres\u00e9se sok tekintetben m\u0171v\u00e9szetnek nevezhet\u0151. Nincs t\u00f6k\u00e9letes algoritmus vagy megk\u00f6zel\u00edt\u00e9s, amely minden feladatot megoldana. Gyakrabban egy komplex m\u00f3dszert alkalmaznak a konkr\u00e9t esetek megold\u00e1s\u00e1ra. Az anom\u00e1li\u00e1k keres\u00e9se egyoszt\u00e1lyos t\u00e1mogat\u00f3 vektorokkal, elszigetelt erd\u0151kkel, metrikus \u00e9s klaszterez\u00e9si m\u00f3dszerekkel, valamint f\u0151komponens-anal\u00edzissel \u00e9s id\u0151beli sorozatok el\u0151rejelz\u00e9s\u00e9vel t\u00f6rt\u00e9nik.<\/p>\n

Ha ismer m\u00e1s m\u00f3dszereket, \u00edrja meg \u0151ket a cikk al\u00e1 kommentben.
\n
Forr\u00e1s: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u0412 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0435\u0439 \u0441\u0442\u0430\u0442\u044c\u0435 \u043c\u044b \u0433\u043e\u0432\u043e\u0440\u0438\u043b\u0438 \u043e \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0438 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0440\u044f\u0434\u043e\u0432. \u041b\u043e\u0433\u0438\u0447\u043d\u044b\u043c \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0435\u043d\u0438\u0435\u043c \u0431\u0443\u0434\u0435\u0442 \u0441\u0442\u0430\u0442\u044c\u044f \u043e \u0432\u044b\u044f\u0432\u043b\u0435\u043d\u0438\u0438 \u0430\u043d\u043e\u043c\u0430\u043b\u0438\u0439. \u041f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0412\u044b\u044f\u0432\u043b\u0435\u043d\u0438\u0435 \u0430\u043d\u043e\u043c\u0430\u043b\u0438\u0439 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0432 \u0442\u0430\u043a\u0438\u0445 \u043e\u0431\u043b\u0430\u0441\u0442\u044f\u0445 \u043a\u0430\u043a: 1) \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u043f\u043e\u043b\u043e\u043c\u043e\u043a \u043e\u0431\u043e\u0440\u0443\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u0422\u0430\u043a, \u0432 2010 \u0433\u043e\u0434\u0443 \u0418\u0440\u0430\u043d\u0441\u043a\u0438\u0435 \u0446\u0435\u043d\u0442\u0440\u0438\u0444\u0443\u0433\u0438 \u0431\u044b\u043b\u0438 \u0430\u0442\u0430\u043a\u043e\u0432\u0430\u043d\u044b \u0432\u0438\u0440\u0443\u0441\u043e\u043c Stuxnet, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0437\u0430\u0434\u0430\u043b \u043d\u0435\u043e\u043f\u0442\u0438\u043c\u0430\u043b\u044c\u043d\u044b\u0439 \u0440\u0435\u0436\u0438\u043c \u0440\u0430\u0431\u043e\u0442\u044b \u043e\u0431\u043e\u0440\u0443\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u0438 \u0432\u044b\u0432\u0435\u043b \u0438\u0437 \u0441\u0442\u0440\u043e\u044f \u0447\u0430\u0441\u0442\u044c \u043e\u0431\u043e\u0440\u0443\u0434\u043e\u0432\u0430\u043d\u0438\u044f \u0437\u0430 \u0441\u0447\u0435\u0442 \u0443\u0441\u043a\u043e\u0440\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u0437\u043d\u043e\u0441\u0430. \u0415\u0441\u043b\u0438 […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-53278","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd479 m\u00f3dszer az anom\u00e1li\u00e1k azonos\u00edt\u00e1s\u00e1ra | ProHoster","description":"Az el\u0151z\u0151 cikkben az id\u0151beli sorozatok el\u0151rejelz\u00e9s\u00e9r\u0151l besz\u00e9lt\u00fcnk. Logikus folytat\u00e1s lenne egy cikk az anom\u00e1li\u00e1k azonos\u00edt\u00e1s\u00e1r\u00f3l.","canonical_url":"https:\/\/prohoster.info\/hu\/blog\/news\/9-podhodov-dlya-vyyavleniya-anomalij","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"hu_HU","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b","og:type":"article","og:title":"\ud83e\udd479 \u043f\u043e\u0434\u0445\u043e\u0434\u043e\u0432 \u0434\u043b\u044f \u0432\u044b\u044f\u0432\u043b\u0435\u043d\u0438\u044f \u0430\u043d\u043e\u043c\u0430\u043b\u0438\u0439 | ProHoster","og:description":"\u0412 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0435\u0439 \u0441\u0442\u0430\u0442\u044c\u0435 \u043c\u044b \u0433\u043e\u0432\u043e\u0440\u0438\u043b\u0438 \u043e \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0438 \u0432\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445 \u0440\u044f\u0434\u043e\u0432. \u041b\u043e\u0433\u0438\u0447\u043d\u044b\u043c \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0435\u043d\u0438\u0435\u043c \u0431\u0443\u0434\u0435\u0442 \u0441\u0442\u0430\u0442\u044c\u044f \u043e \u0432\u044b\u044f\u0432\u043b\u0435\u043d\u0438\u0438 \u0430\u043d\u043e\u043c\u0430\u043b\u0438\u0439.","og:url":"https:\/\/prohoster.info\/hu\/blog\/news\/9-podhodov-dlya-vyyavleniya-anomalij","og:image":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:secure_url":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:width":350,"og:image:height":350,"article:published_time":"2019-11-27T21:00:00+00:00","article:modified_time":"2020-02-18T11:01:10+00:00","article:publisher":"https:\/\/www.facebook.com\/prohoster","article:author":"https:\/\/www.facebook.com\/prohoster"},"aioseo_meta_data":{"post_id":"53278","title":null,"description":null,"keywords":null,"keyphrases":null,"primary_term":null,"canonical_url":null,"og_title":null,"og_description":null,"og_object_type":"default","og_image_type":"default","og_image_url":null,"og_image_width":null,"og_image_height":null,"og_image_custom_url":null,"og_image_custom_fields":null,"og_video":null,"og_custom_url":null,"og_article_section":null,"og_article_tags":null,"twitter_use_og":false,"twitter_card":"default","twitter_image_type":"default","twitter_image_url":null,"twitter_image_custom_url":null,"twitter_image_custom_fields":null,"twitter_title":null,"twitter_description":null,"schema":{"blockGraphs":[],"customGraphs":[],"default":{"data":{"Article":[],"Course":[],"Dataset":[],"FAQPage":[],"Movie":[],"Person":[],"Product":[],"ProductReview":[],"Car":[],"Recipe":[],"Service":[],"SoftwareApplication":[],"WebPage":[]},"graphName":"","isEnabled":true},"graphs":[]},"schema_type":null,"schema_type_options":null,"pillar_content":false,"robots_default":true,"robots_noindex":false,"robots_noarchive":false,"robots_nosnippet":false,"robots_nofollow":false,"robots_noimageindex":false,"robots_noodp":false,"robots_notranslate":false,"robots_max_snippet":null,"robots_max_videopreview":null,"robots_max_imagepreview":"large","priority":null,"frequency":null,"local_seo":null,"seo_analyzer_scan_date":"2026-01-24 06:46:20","breadcrumb_settings":null,"limit_modified_date":false,"reviewed_by":null,"ai":null,"created":"2021-02-28 20:28:47","updated":"2026-01-24 06:46:20","focus_keyword":null,"additional_keywords":null,"truseo_locale":null},"gt_translate_keys":[{"key":"link","format":"url"}],"_links":{"self":[{"href":"https:\/\/prohoster.info\/hu\/wp-json\/wp\/v2\/posts\/53278","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/prohoster.info\/hu\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/prohoster.info\/hu\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/prohoster.info\/hu\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/prohoster.info\/hu\/wp-json\/wp\/v2\/comments?post=53278"}],"version-history":[{"count":0,"href":"https:\/\/prohoster.info\/hu\/wp-json\/wp\/v2\/posts\/53278\/revisions"}],"wp:attachment":[{"href":"https:\/\/prohoster.info\/hu\/wp-json\/wp\/v2\/media?parent=53278"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/prohoster.info\/hu\/wp-json\/wp\/v2\/categories?post=53278"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/prohoster.info\/hu\/wp-json\/wp\/v2\/tags?post=53278"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}