{"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\/sq\/blog\/novosti-interneta\/9-podhodov-dlya-vyyavleniya-anomalij","title":{"rendered":"9 qasje p\u00ebr identifikimin e anomalive","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

N\u00eb artikulli i m\u00ebparsh\u00ebm<\/a><\/noindex> Kemi folur p\u00ebr parashikimin e serive kohore. Nj\u00eb vazhdim logjik do t\u00eb ishte nj\u00eb artikull mbi identifikimin e anomalive. <\/p>\n

Aplikimi<\/h3>\n

\nIdentifikimi i anomalive p\u00ebrdoret n\u00eb fushat si: <\/p>\n

1) Parashikimi i d\u00ebshtimeve t\u00eb pajisjeve<\/h3>\n

\nK\u00ebshtu, n\u00eb vitin 2010, centrifugat e Iranit u sulmuan nga virusi Stuxnet, i cili vendosi nj\u00eb regjim jo optimal t\u00eb pun\u00ebs dhe shkat\u00ebrroi nj\u00eb pjes\u00eb t\u00eb pajisjeve p\u00ebr shkak t\u00eb konsumit t\u00eb p\u00ebrshpejtuar. <\/p>\n

N\u00ebse n\u00eb pajisje do t\u00eb ishin p\u00ebrdorur algoritme p\u00ebr gjetjen e anomalive, situatat e d\u00ebshtimit do t\u00eb ishin evituar. <\/p>\n

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\n<\/a><\/noindex>
\nK\u00ebrkimi i anomalive n\u00eb pun\u00ebn e pajisjeve p\u00ebrdoret jo vet\u00ebm n\u00eb industrin\u00eb b\u00ebrthamore, por gjithashtu n\u00eb metalurgji dhe n\u00eb pun\u00ebn e turbinave avioni. Po ashtu, n\u00eb fusha t\u00eb tjera ku p\u00ebrdorimi i diagnostik\u00ebs parashikuese \u00ebsht\u00eb m\u00eb i lir\u00eb se humbjet e mundshme nga d\u00ebshtimi i paparashikuesh\u00ebm.<\/p>\n

2) Parashikimi i veprimeve mashtruese<\/h3>\n

\nN\u00ebse nga kartela me t\u00eb cil\u00ebn po p\u00ebrdorni n\u00eb Podolsk, hiqen para n\u00eb Shqip\u00ebri, ndoshta transaksionet duhet t\u00eb verifikohen m\u00eb tej.<\/p>\n

3) Identifikimi i modeleve t\u00eb \u00e7uditshme t\u00eb konsumator\u00ebve<\/h3>\n

\nN\u00ebse nj\u00eb pjes\u00eb e klient\u00ebve tregon sjellje anomale, mund t\u00eb ket\u00eb nj\u00eb problem p\u00ebr t\u00eb cilin nuk jeni i vet\u00ebdijsh\u00ebm.<\/p>\n

4) Identifikimi i k\u00ebrkes\u00ebs dhe ngarkes\u00ebs anomale<\/h3>\n

\nN\u00ebse shitjet n\u00eb dyqanin FMCG kan\u00eb r\u00ebn\u00eb n\u00ebn kufirin e intervalit t\u00eb besimit t\u00eb parashikimit, \u00ebsht\u00eb e r\u00ebnd\u00ebsishme t\u00eb gjejm\u00eb shkakun e k\u00ebsaj.<\/p>\n

Qasje p\u00ebr identifikimin e anomali<\/h2>\n

<\/p>\n

1) Metoda e mb\u00ebshtetjes s\u00eb vektor\u00ebve me nj\u00eb klas\u00eb One-Class SVM<\/h3>\n

\nP\u00ebrgjegjshme kur n\u00eb grupin e trajnimit t\u00eb dh\u00ebnat i p\u00ebrkasin shp\u00ebrndarjes normale, nd\u00ebrsa n\u00eb testim p\u00ebrmbajn\u00eb anomali.<\/p>\n

Metoda nj\u00ebklas\u00ebshe e mb\u00ebshtetjes s\u00eb vektor\u00ebve ndryn nj\u00eb sip\u00ebrfaqe jo-lineare rreth fillimit t\u00eb koordinatave. Mund t\u00eb vendosni nj\u00eb kufi ndar\u00ebs, p\u00ebr t\u00eb p\u00ebrcaktuar cilat t\u00eb dh\u00ebna t\u00eb merren si anomali.<\/p>\n

P\u00ebrvoja e ekipit ton\u00eb DATA4 tregon se One-Class SVM \u00ebsht\u00eb algoritmi m\u00eb i p\u00ebrdorur p\u00ebr zgjidhjen e problemit t\u00eb k\u00ebrkimit t\u00eb anomali. <\/p>\n

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\n<\/p>\n

2) Metoda e pyjeve izoluese \u2013 isolate forest<\/h3>\n

\nMe metod\u00ebn 'rast\u00ebsore' t\u00eb nd\u00ebrtimit t\u00eb pem\u00ebve, p\u00ebrjashtimet do t\u00eb bien n\u00eb gjethet n\u00eb etapa t\u00eb hershme (n\u00eb thell\u00ebsi t\u00eb vog\u00ebl t\u00eb pem\u00ebs), dmth. p\u00ebrjashtimet jan\u00eb m\u00eb t\u00eb lehta p\u00ebr t'u 'izoluar'. Ndryshimi i vlerave anomale ndodh n\u00eb iteracionet e para t\u00eb funksionimit t\u00eb algoritmit.<\/p>\n

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\n<\/p>\n

3) Elliptic envelope dhe metoda statistike<\/h3>\n

\nP\u00ebrdoret kur t\u00eb dh\u00ebnat jan\u00eb normalisht t\u00eb shp\u00ebrndara. Sa m\u00eb af\u00ebr t\u00eb jet\u00eb masa me bishtin e p\u00ebrzierjes s\u00eb shp\u00ebrndarjeve, aq m\u00eb anomale \u00ebsht\u00eb vlera.<\/p>\n

K\u00ebtij klasi i p\u00ebrkasin edhe metoda t\u00eb tjera statistikore.<\/p>\n

\"9
\n
\n\"9
\nFigura nga faqja dyakonov.org<\/i><\/p>\n

4) Metodat metrik\u00eb<\/h3>\n

\nMetodat p\u00ebrfshijn\u00eb algoritme si k fqinj\u00ebt e af\u00ebrt, fqinj\u00ebt e af\u00ebrt k, ABOD (n\u00eb baz\u00eb t\u00eb k\u00ebndit p\u00ebr identifikimin e anomalis\u00eb) ose LOF (faktori lokal i anomalis\u00eb).<\/p>\n

Jan\u00eb t\u00eb p\u00ebrshtatshme, n\u00ebse distanca mes vlerave n\u00eb karakteristika \u00ebsht\u00eb e barabart\u00eb ose e normalizuar (p\u00ebr t\u00eb mos i matur gjarp\u00ebrin me papagaj\u00eb). <\/p>\n

Algoritmi i k fqinj\u00ebve t\u00eb af\u00ebrt supozon se vlerat normale jan\u00eb t\u00eb pozicionuara n\u00eb nj\u00eb zon\u00eb t\u00eb caktuar t\u00eb hap\u00ebsir\u00ebs shum\u00ebdimensionale, dhe distanca deri te anomali do t\u00eb jet\u00eb m\u00eb e madhe se deri te hipoiplani ndar\u00ebs. <\/p>\n

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\n<\/p>\n

5) Metodat e klastrave<\/h3>\n

\nThelbi i metodave t\u00eb klastrave \u00ebsht\u00eb se n\u00ebse nj\u00eb vler\u00eb \u00ebsht\u00eb e larg\u00ebt nga qendrat e klastrave m\u00eb shum\u00eb se nj\u00eb sasi e caktuar, ajo mund t\u00eb konsiderohet anomale. <\/p>\n

E r\u00ebnd\u00ebsishme \u00ebsht\u00eb t\u00eb p\u00ebrdoret algoritmi q\u00eb klasifikon t\u00eb dh\u00ebnat n\u00eb m\u00ebnyr\u00eb korrekte, \u00e7ka varet nga detyra specifike. <\/p>\n

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

6) Metoda e komponenteve kryesore<\/h3>\n

\nP\u00ebrshtatshme, ku identifikohen drejtimet e ndryshimit m\u00eb t\u00eb madh t\u00eb varianc\u00ebs.<\/p>\n

7) Algoritmet e bazuara n\u00eb parashikimin e serive t\u00eb koh\u00ebs<\/h3>\n

\nIdeja \u00ebsht\u00eb se n\u00ebse nj\u00eb vler\u00eb del jasht\u00eb intervalit t\u00eb besueshm\u00ebris\u00eb s\u00eb parashikimit, ajo konsiderohet anomale. P\u00ebr parashikimin e nj\u00eb serie kohore p\u00ebrdoren algoritme t\u00eb tilla si glatimi i trefisht\u00eb, S(ARIMA), boosting, etj.<\/p>\n

P\u00ebr algoritmet e parashikimit t\u00eb serive t\u00eb koh\u00ebs u fol n\u00eb artikullin e m\u00ebparsh\u00ebm.<\/p>\n

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

8) M\u00ebsimi me mbik\u00ebqyrje (regresioni, klasifikimi)<\/h3>\n

\nN\u00ebse t\u00eb dh\u00ebnat lejojn\u00eb, ne p\u00ebrdorim algoritme q\u00eb variojn\u00eb nga regresioni linear deri te rrjetet rekurrente. Do t\u00eb matim diferenc\u00ebn midis parashikimeve dhe vler\u00ebs reale, dhe do t\u00eb nxjerrim p\u00ebrfundimin se sa t\u00eb dh\u00ebnat shkojn\u00eb p\u00ebrtej norm\u00ebs. \u00cbsht\u00eb e r\u00ebnd\u00ebsishme q\u00eb algoritmi t\u00eb ket\u00eb aft\u00ebsi t\u00eb mjaftueshme p\u00ebr t\u00eb p\u00ebrgjith\u00ebsuar, dhe grupi i trajnimit t\u00eb mos p\u00ebrmbaj\u00eb vlera anomale.<\/p>\n

9) Testet e modeleve<\/h3>\n

\nT\u00eb afrojm\u00eb problemin e identifikimit t\u00eb anomaleve si nj\u00eb problem rekomandimi. T\u00eb shp\u00ebrndajm\u00eb matric\u00ebn ton\u00eb t\u00eb karakteristikave duke p\u00ebrdorur SVD ose makinat e faktorizimit, dhe vlerat n\u00eb matric\u00ebn e re, q\u00eb dallohet ndjesh\u00ebm nga origjinali, t\u00eb pranohen si anomale. <\/p>\n

\"9
\n
\nFigura nga faqja dyakonov.org<\/i><\/p>\n

P\u00ebrfundimi<\/h3>\n

\nN\u00eb k\u00ebt\u00eb artikull kemi shqyrtuar qasjet kryesore p\u00ebr identifikimin e anomaleve. <\/p>\n

K\u00ebrkimi i anomali\u00ebve mund t\u00eb quhet n\u00eb shum\u00eb aspekte nj\u00eb art. Nuk ka nj\u00eb algorit\u00ebm ose qasje perfekte q\u00eb zgjidh t\u00eb gjitha problemet. Shpesh p\u00ebrdoren nj\u00eb kompleks metodash p\u00ebr t\u00eb zgjidhur nj\u00eb rast specifik. K\u00ebrkimi i anomali\u00ebve realizohet me ndihm\u00ebn e metod\u00ebs s\u00eb vetme t\u00eb klasifikuesve t\u00eb vektor\u00ebve, pyllit izolues, metodave metrik\u00eb dhe klaster\u00eb, si dhe p\u00ebrdorimin e komponent\u00ebve kryesor\u00eb dhe parashikimin e serive temporale.<\/p>\n

N\u00ebse dini metoda t\u00eb tjera, ju lutem shkruani p\u00ebr to n\u00eb komentet e artikullit.
\n
Burimi: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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Nj\u00eb vazhdim logjik do t\u00eb ishte artikulli mbi identifikimin e anomali\u00ebve. P\u00ebrdorimi i Identifikimit t\u00eb anomali\u00ebve p\u00ebrdoret n\u00eb fusha t\u00eb tilla si: 1) Parashikimi i defekteve t\u00eb pajisjeve. K\u00ebshtu, n\u00eb vitin 2010, centrifugat iraniane u sulmuan nga virusi Stuxnet, i cili vendosi nj\u00eb mod optimal funsioni dhe d\u00ebmtoi nj\u00eb pjes\u00eb t\u00eb pajisjeve p\u00ebr shkak t\u00eb konsumit t\u00eb p\u00ebrshpejtuar.","canonical_url":"https:\/\/prohoster.info\/sq\/blog\/novosti-interneta\/9-podhodov-dlya-vyyavleniya-anomalij","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"sq_AL","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 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