{"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\/et\/blog\/novosti-interneta\/9-podhodov-dlya-vyyavleniya-anomalij","title":{"rendered":"9 l\u00e4henemist anomaaliate tuvastamiseks","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

V eelnevas artiklis<\/a><\/noindex> Me r\u00e4\u00e4kisime ajas\u00e9rie ennustamisest. Loogiline j\u00e4tk oleks artikkel anomaaliate tuvastamisest. <\/p>\n

Kasutamine<\/h3>\n

\nAnomaaliate tuvastamist kasutatakse sellistes valdkondades nagu: <\/p>\n

1) Seadmete riketest ennustamine<\/h3>\n

\nNii juhtus 2010. aastal, et Iraani tsentrifugid said Stuxnet'i viiruse r\u00fcnnaku osaliseks, mis seadis seadmed ebat\u00e4pse t\u00f6\u00f6re\u017eiimi ja p\u00f5hjustas seadmete osa riketeid kiirenenud kulumise t\u00f5ttu. <\/p>\n

Kui seadmetes oleks kasutatud anomaaliate otsimise algoritme, oleks rikkeid saanud v\u00e4ltida. <\/p>\n

\"9
\n<\/a><\/noindex>
\nAnomaaliate otsimist seadmete t\u00f6\u00f6s kasutatakse mitte ainult tuumaenergeetikas, vaid ka metallurgias ja lennuturbiinide t\u00f6\u00f6s. Samuti teistes valdkondades, kus ennustav diagnostika on odavam kui etten\u00e4gematud kahjud rikke korral.<\/p>\n

2) Pettuste ennustamine<\/h3>\n

\nKui teie Podolskis kasutatavalt kaardilt v\u00f5etakse raha Albaanias, siis tuleb tehingud t\u00f5en\u00e4oliselt t\u00e4iendavalt kontrollida.<\/p>\n

3) Anomaalsete tarbimismustrite tuvastamine<\/h3>\n

\nKui osa klientidest n\u00e4itab anomaalset k\u00e4itumist, v\u00f5ib olla probleem, millest te ei tea.<\/p>\n

4) Ebanormaalse n\u00f5udluse ja koormuse tuvastamine<\/h3>\n

\nKui FMCG kaupluses m\u00fc\u00fck j\u00e4\u00e4b prognoosi usaldusintervalli alt, tasub leida selle p\u00f5hjused.<\/p>\n

Tuvastamisv\u00f5tted ebanormaalsustele<\/h2>\n

<\/p>\n

1) \u00dche klassiga tugivektorite meetod One-Class SVM<\/h3>\n

\nSobib siis, kui treeningandmed j\u00e4rgivad normaaljaotust ja testandmed sisaldavad ebanormaalsusi.<\/p>\n

\u00dche klassiga tugivektorite meetod loob mittelineaarse pinna koordinaatide alguses. V\u00f5imalik on seada l\u00f5ikepiir, et m\u00e4\u00e4rata, millised andmed on ebanormaalsed.<\/p>\n

Meie DATA4 tiimi kogemuste p\u00f5hjal on One-Class SVM k\u00f5ige sagedamini kasutatav algoritm ebanormaalsuste tuvastamise \u00fclesannetes. <\/p>\n

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

2) Isolatsioonipuu meetod \u2013 isolate forest<\/h3>\n

\nKui puud on juhuslikult \u00fcles ehitatud, satuvad k\u00f5rvalekalded varakult lehtedesse (puu v\u00e4ikese s\u00fcgavuse juures), seega on k\u00f5rvalekaldeid lihtsam 'isoleerida'. Ebanormaalsete v\u00e4\u00e4rtuste tuvastamine toimub algoritmi varastes iteratsioonides.<\/p>\n

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

3) Elliptiline \u00fcmbris ja statistilised meetodid<\/h3>\n

\nKasutatakse, kui andmed on normaaljaotusega. Mida l\u00e4hemal on m\u00f5\u00f5tmine jaotuse sabasse, seda ebanormaalsem on v\u00e4\u00e4rtus.<\/p>\n

Selle klassi alla kuuluvad ka teised statistilised meetodid.<\/p>\n

\"9
\n
\n\"9
\nPilt saidilt dyakonov.org<\/i><\/p>\n

4) Metrilised meetodid<\/h3>\n

\nMeetodite seas on sellised algoritmid nagu k l\u00e4hima naabri, k-kohaliku naabri, ABOD (nurgap\u00f5hine \u00e4\u00e4rmuslike v\u00e4\u00e4rtuste tuvastamine) v\u00f5i LOF (kohalik \u00e4\u00e4rmuslik tegur).<\/p>\n

Sobivad, kui omaduste v\u00e4\u00e4rtuste vahemaa on v\u00f5rdselt m\u00f5\u00f5detud v\u00f5i normeeritud (et mitte m\u00f5\u00f5ta \u00fchte krahhi papagoidesse). <\/p>\n

K l\u00e4hima naabri algoritm eeldab, et normaalsed v\u00e4\u00e4rtused asuvad teatud mitmem\u00f5\u00f5tmelise ruumi piirkonnas, ning vahemaa anomaaliate poole on suurem kui eraldus h\u00fcperplaadi poole. <\/p>\n

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

5) Klastri meetodid<\/h3>\n

\nKlastri meetodite olemus on see, et kui v\u00e4\u00e4rtus on klastrite keskustest kaugemal kui teatud suurus, v\u00f5ib seda pidada anomaaliaks. <\/p>\n

Peamine on kasutada algoritmi, mis klasterdab andmeid \u00f5igesti, mis s\u00f5ltub konkreetsest \u00fclesandest. <\/p>\n

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

6) Peamiste komponentide meetod<\/h3>\n

\nSobib, kus tuvastatakse suurima muutuva dispersiooni suunad.<\/p>\n

7) Aja seeriaprognosi meetodid<\/h3>\n

\nIdee on selles, et kui v\u00e4\u00e4rtus \u00fcletab prognoosimise usaldusvahemikku, loetakse see anomaaliaks. Ajavahemiku prognoosimiseks kasutatakse algoritme nagu kolmekordne silumise, S(ARIMA), boosting jne.<\/p>\n

Ajavahemiku prognoosimise algoritmidest r\u00e4\u00e4giti eelnevas artiklis.<\/p>\n

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

8) Juhendatud \u00f5ppimine (regressioon, klassifitseerimine)<\/h3>\n

\nKui andmed lubavad, kasutame algoritme alates lineaarsetest regressioonidest kuni rekurentsete v\u00f5rkudeni. M\u00d5\u00d5DAME erinevust prognoosi ja tegeliku v\u00e4\u00e4rtuse vahel ning teeme j\u00e4relduse, kuiv\u00f5rd andmed v\u00e4ljavad normist. Oluline on, et algoritm omaks piisavat \u00fcldistamisv\u00f5imet ning treeninguandmete hulk ei sisaldaks anomaalseid v\u00e4\u00e4rtusi.<\/p>\n

9) Mudelite testimine<\/h3>\n

\nL\u00e4heneme anomaaliate leidmise \u00fclesandele nagu soovituste otsimine. Kasutame meie tunnuste maatriksi jagamiseks SVD-d v\u00f5i faktorisatsiooni masinaid, ja uues maatriksis, mis oluliselt erineb algsetest, peame anomaalseteks. <\/p>\n

\"9
\n
\nPilt saidilt dyakonov.org<\/i><\/p>\n

Kokkuv\u00f5te<\/h3>\n

\nSelles artiklis vaatasime \u00fcle peamised l\u00e4henemised anomaaliate avastamiseks. <\/p>\n

Anomaaliate avastamist v\u00f5ib nimetada kunstiks. Ei ole ideaalset algoritmi v\u00f5i l\u00e4henemist, mille rakendamine lahendaks k\u00f5ik probleemid. Sageli kasutatakse konkreetse juhtumi lahendamiseks meetodite kompleksi. Anomaaliate avastamine toimub \u00fchekaupa toetavate vektortehnikate, isolatsioonimetsade, m\u00f5\u00f5tmise ja klastri meetodite abil, samuti peamiste komponentide anal\u00fc\u00fcsi ja ajasarjade prognoosimise abil.<\/p>\n

Kui tead teisi meetodeid, kirjuta neist artikli kommentaarides.
\n
Allikas: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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Loogiline j\u00e4tk on artikkel anomaaliate avastamisest. Anomaaliate avastamist kasutatakse sellistes valdkondades nagu: 1) Seadmete rikke ennustamine. N\u00e4iteks 2010. aastal r\u00fcndas Iraani tsentrifuuge Stuxneti viirus, mis seadis seadme t\u00f6\u00f6 mitteoptimaalsesse re\u017eiimi ja p\u00f5hjustas seadmete h\u00e4vitamise kiirendatud kulumise t\u00f5ttu. 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