 Limbola lolenge moko ya koyeba ba email ya spam.
Lolenge moko oyo bato mingi basalelaka mpo na koyeba ba email ya spam ezali na nzela ya kosalela ba filtres, oyo esalelaka ba algorithmes mpo na koyeba bizaleli oyo bato mingi basalelaka na ba messages ya spam. Ba filtres oyo elukaka maloba to bafraze ya sikisiki oyo ezwamaka mingi na ba spam, lokola "fina awa," "ofele," "crédit," mpe misusu. Longola yango, ba filtres ekoki kotala adresse email ya motindi na liste ya ba spammers oyo eyebani, kotala soki email ezali kotindama na motango monene ya bato oyo bazwi yango, mpe kotalela makambo oyo ezali na email mpo na koyeba soki ezali na ba liens to ba pièces jointes oyo ekoki kozala likama. Lolenge mosusu ya koyeba ba email ya spam ezali na nzela ya apprentissage ya machine, esika système epesameli formasyo mpo na koyeba ba modèles oyo ezwami na ba email ya spam oyo eyebani. Méthode oyo esɛngaka mingi mingi ko analyser échantillon monene ya ba emails mpe ko étiqueter yango soit spam to spam te. Na sima algorithme ya apprentissage machine esalela ba données oyo pona ko identifier ba caractéristiques communes ya ba emails spam pe ko incorporer ba caractéristiques wana na processus ya filtrage. Longola yango, ba services mosusu ya email epesaka basaleli nzela ya kopesa lapolo ya ba email ya spam, oyo esalisaka mpo na kobongisa bosikisiki ya ba filtres ya spam na boumeli ya ntango. Ba rapports oyo ekoki kosalisa service eyeba ba tactiques ya sika oyo ba spammers basalelaka mpe e ajuster ba algorithmes na bango ya filtrage en conséquence. Na mobimba, koyeba ba email ya spam esɛngaka kosangisa filtrage, koyekola na masini, mpe kopesa lapolo ya basaleli mpo na koyeba malamu mpe kokanga ba messages oyo olingi te.