{"id":3052,"date":"2026-02-26T22:29:38","date_gmt":"2026-02-26T22:29:38","guid":{"rendered":"https:\/\/renewasoft.com.tr\/?p=3052"},"modified":"2026-02-28T00:35:56","modified_gmt":"2026-02-28T00:35:56","slug":"gercek-zamanli-anomali-tespiti-scada-verisi-uzerinden-siber-saldiri-algilama","status":"publish","type":"post","link":"https:\/\/renewasoft.com.tr\/index.php\/tr\/2026\/02\/26\/gercek-zamanli-anomali-tespiti-scada-verisi-uzerinden-siber-saldiri-algilama\/","title":{"rendered":"Ger\u00e7ek Zamanl\u0131 Anomali Tespiti: SCADA Verisi \u00dczerinden Siber Sald\u0131r\u0131 Alg\u0131lama"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text css=””]<\/p>\n

Ger\u00e7ek Zamanl\u0131 Anomali Tespiti: SCADA Verisi \u00dczerinden Siber Sald\u0131r\u0131 Alg\u0131lama<\/h1>\n

Fiziksel S\u00fcre\u00e7 Modelleme, ML Hibrit ve Hydrowise AI Destekli Erken Uyar\u0131<\/em>
\nRenewasoft | 2026<\/strong><\/p>\n

Seviye: \u0130leri<\/span> \u00a0 Hedef Kitle: SCADA M\u00fchendisi, HES \u0130\u015fletmecisi, CTO, Altyap\u0131 Yat\u0131r\u0131mc\u0131s\u0131<\/p>\n

Giri\u015f: SCADA Verisi Bir Siber G\u00fcvenlik Sinyal Hatt\u0131d\u0131r<\/h1>\n

Enerji \u00fcretim tesislerinde SCADA verisi uzun y\u0131llar boyunca operasyonel raporlama i\u00e7in kullan\u0131ld\u0131. Ancak bug\u00fcn SCADA ak\u0131\u015flar\u0131 ayn\u0131 zamanda bir siber g\u00fcvenlik sinyal hatt\u0131 haline geldi. Modern sald\u0131r\u0131lar \u00e7o\u011fu zaman do\u011frudan a\u011f trafi\u011fi ile de\u011fil, fiziksel s\u00fcre\u00e7teki \u00e7ok k\u00fc\u00e7\u00fck ama anlaml\u0131 sapmalar ile kendini ele veriyor: setpoint oynatmalar\u0131, sensor drift\u2019i, aktuator komutlar\u0131n\u0131n beklenmedik s\u0131kl\u0131\u011f\u0131, debi\u2013bas\u0131n\u00e7\u2013frekans ili\u015fkilerinin bozulmas\u0131, titre\u015fim spektrumunda anl\u0131k harmonik art\u0131\u015flar[1].<\/p>\n

Bu yakla\u015f\u0131m\u0131n \u00f6nemi; NIST ICS rehberlerinde[1], IEC 62443 serisinde[2], MITRE ATT&CK for ICS\u2019de[3] ve CISA\u2019nin ICS odakl\u0131 uyar\u0131lar\u0131nda[4] a\u00e7\u0131k\u00e7a vurgulan\u0131r. Bu b\u00f6l\u00fcmde ama\u00e7; SCADA\u2019dan beslenen ger\u00e7ek zamanl\u0131 anomali tespitinin nas\u0131l kurgulanaca\u011f\u0131n\u0131, fiziksel s\u00fcre\u00e7 modellemeden ML tabanl\u0131 y\u00f6ntemlere, feature engineering\u2019e, false positive y\u00f6netimine, SIEM entegrasyonuna ve Hydrowise\u2019in erken uyar\u0131 yakla\u015f\u0131m\u0131na kadar u\u00e7tan uca bir \u00e7er\u00e7eve ile anlatmakt\u0131r.<\/p>\n

TL;DR — Y\u00f6netici \u00d6zeti<\/h2>\n
\n
    \n
  1. \n

    “Normal” kavram\u0131 sabit bir baseline de\u011fil; i\u015fletme modu, mevsim, y\u00fck ve ekipman durumuna g\u00f6re ba\u011flamsal bir fonksiyondur[1].<\/p>\n<\/li>\n

  2. \n

    Fiziksel s\u00fcre\u00e7 modelleme (k\u00fctle\/enerji dengesi, dijital ikiz), istatistiksel sapma ile siber manipulasyonu ay\u0131rt etmenin temelidir[1][3].<\/p>\n<\/li>\n

  3. \n

    En iyi mimari hibrit: SPC\/EWMA (h\u0131zl\u0131 sinyal) + ML (anlamland\u0131rma) + fiziksel model (tutarl\u0131l\u0131k)[1][2].<\/p>\n<\/li>\n

  4. \n

    False positive y\u00f6netimi; \u00e7ok a\u015famal\u0131 do\u011frulama, ba\u011flam e\u015fikleri ve alarm birle\u015ftirme ile sa\u011flan\u0131r[1].<\/p>\n<\/li>\n

  5. \n

    Hydrowise; veri entegrasyonu, ba\u011flam etiketleme, anomali skorlama, a\u00e7\u0131klanabilirlik ve olay y\u00f6netimini tek platformda birle\u015ftirir[1][4].<\/p>\n<\/li>\n<\/ol>\n<\/div>\n

    \"\"<\/p>\n

    \u0130nfografik: SCADA Anomali Tespit Boru Hatt\u0131– HES Referans Mimarisi [1][2][3]<\/em><\/p>\n

    “Normal” Kavram\u0131n\u0131 Tan\u0131mlamadan Anomali Bulamazs\u0131n\u0131z<\/h1>\n

    Anomali tespitinin ilk ve en kritik ad\u0131m\u0131 “normal”in tan\u0131m\u0131d\u0131r. SCADA ortam\u0131nda “normal” kavram\u0131, ofis IT sistemlerindeki gibi sabit bir baseline de\u011fildir. HES\u2019lerde s\u00fcre\u00e7 dinami\u011fi do\u011fas\u0131 gere\u011fi de\u011fi\u015fkendir: mevsimsel debi de\u011fi\u015fimleri, rezervuar seviyesi davran\u0131\u015flar\u0131, t\u00fcrbin y\u00fcklenme profili, operat\u00f6r m\u00fcdahaleleri, sensor kalibrasyonlar\u0131 ve hidrolik\/mekanik y\u0131pranma[1][2]<\/p>\n\n\n\n\n\n\n\n\n
    \u0130\u015fletme Modu<\/th>\nNormal Davran\u0131\u015f Deseni<\/th>\nAnomali Esik Yakla\u015f\u0131m\u0131<\/th>\n<\/tr>\n
    Start-up \/ Senkronizasyon<\/strong><\/td>\nH\u0131zl\u0131 RPM art\u0131s\u0131, ge\u00e7ici titre\u015fim pikleri<\/td>\nGeni\u015f bantl\u0131 esikler; s\u00fcre bazl\u0131 timeout<\/td>\n<\/tr>\n
    Nominal \u00dcretim<\/strong><\/td>\nKararli debi–guc–frekans korelasyonu<\/td>\nDar bantli; mod-conditioned SPC<\/td>\n<\/tr>\n
    Y\u00fck Art\u0131rma\/Azaltma<\/strong><\/td>\nRampa profili; ge\u00e7ici overshoot\/undershoot<\/td>\nRampa suresi ve y\u00f6n\u00fc ile e\u015fle\u015ftirme<\/td>\n<\/tr>\n
    Bakim \/ Bypass<\/strong><\/td>\nSensor offline, kontrol d\u00f6ng\u00fcs\u00fc devre d\u0131\u015f\u0131<\/td>\nAnomali tespiti ask\u0131ya al\u0131n\u0131r veya \u00f6zel mod<\/td>\n<\/tr>\n
    Acil Durum<\/strong><\/td>\nTrip, h\u0131zl\u0131 kapat, koruma rolesi devrede<\/td>\nOlay sonras\u0131 forensik analiz modu<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Tablo 1: \u0130\u015fletme Modlar\u0131 ve Anomali Esik Yakla\u015f\u0131mlar\u0131 [1][2]<\/em><\/p>\n

    “Normal”i tan\u0131mlamak i\u00e7in \u00f6nerilen yakla\u015f\u0131m, veri katman\u0131nda etiketli bir operasyonel durum alan\u0131 \u00fcretmektir. Bu etiket, do\u011frudan SCADA\u2019dan (\u00f6rn. \u00e7al\u0131\u015fma modu tag\u2019leri) veya kural tabanl\u0131 t\u00fcretimle (\u00f6rn. breaker a\u00e7\u0131k\/kapal\u0131, RPM, aktif g\u00fc\u00e7) olu\u015fturulabilir[1].<\/p>\n

    Fiziksel S\u00fcre\u00e7 Modelleme: Korelasyon Yetmez, Nedensellik Laz\u0131m<\/h1>\n

    SCADA anomali tespitini siber sald\u0131r\u0131 ba\u011flam\u0131na ta\u015f\u0131yan kritik fark: yaln\u0131zca istatistiksel ayk\u0131r\u0131l\u0131k de\u011fil, fiziksel s\u00fcre\u00e7 tutarl\u0131l\u0131\u011f\u0131 aranmal\u0131d\u0131r. Bir sald\u0131rgan, sensor de\u011ferini manip\u00fcle ederek operat\u00f6r\u00fc yanl\u0131\u015f karar almaya zorlayabilir. Bu manipulasyon tek sens\u00f6rde makul g\u00f6r\u00fcn\u00fcrken, s\u00fcre\u00e7 fizi\u011fiyle \u00e7eli\u015febilir[1][3].<\/p>\n

    A) K\u0131s\u0131t ve Invariant Tabanl\u0131 Modelleme<\/h3>\n

    K\u00fctle korunumu, enerji dengesi, hidrolik bas\u0131n\u00e7\u2013debi ili\u015fkileri; t\u00fcrbin verim e\u011frileri; rezervuar seviye de\u011fi\u015fim h\u0131z\u0131 (dS\/dt) ile giri\u015f\u2013\u00e7\u0131k\u0131\u015f ak\u0131\u015f dengesi. \u00d6rne\u011fin: debi artm\u0131\u015f g\u00f6r\u00fcn\u00fcrken aktif g\u00fc\u00e7 ayn\u0131 kal\u0131yorsa \u2192 verim d\u00fc\u015f\u00fc\u015f\u00fc veya telemetri manipulasyonu. Bas\u0131n\u00e7 artmadan debi art\u0131yorsa \u2192 sensor spoofing \u015f\u00fcphesi[1].<\/p>\n

    B) Basitle\u015ftirilmi\u015f Dijital \u0130kiz Mant\u0131\u011f\u0131<\/h3>\n

    Tam CFD\/FEA dijital ikiz pahalidir; operasyonel amacla reduced-order model kullanilir: debi, net dusu, turbin kanat acisi, governor komutu, jenerator yuku \u2192 beklenen guc. Bu model iki sey saglar: (1) Yorumlanabilirlik: ne sapmis, neden sapmis olabilir? (2) Saldiri ayristirma: surec arizasi mi, sensor arizasi mi, siber manipulasyon mu?[1][2]<\/sup><\/p>\n

    \u0130statistiksel Anomali vs ML Tabanl\u0131 Anomali: Hangisi Ne Zaman?<\/h1>\n\n\n\n\n\n\n\n\n\n
    Y\u00f6ntem<\/th>\nAvantaj<\/th>\nDezavantaj<\/th>\n<\/tr>\n
    Z-score \/ MAD<\/strong><\/td>\nH\u0131zl\u0131, ucuz, \u015feffaf<\/td>\n\u00c7ok de\u011fi\u015fkenli ili\u015fkileri ka\u00e7\u0131r\u0131r<\/td>\n<\/tr>\n
    EWMA \/ CUSUM<\/strong><\/td>\nDrift ve kucuk sapmalari iyi yakalar<\/td>\nKarma\u015f\u0131k s\u00fcre\u00e7lerde yetersiz kalabilir<\/td>\n<\/tr>\n
    Hotelling T\u00b2<\/strong><\/td>\nKorelasyonlu tag setlerinde toplu sapma<\/td>\nRoot-cause ayr\u0131\u015ft\u0131rma zay\u0131f<\/td>\n<\/tr>\n
    Isolation Forest<\/strong><\/td>\nEtiketsiz, \u00e7ok boyutlu veride g\u00fc\u00e7l\u00fc<\/td>\nZaman ba\u011f\u0131ml\u0131l\u0131\u011f\u0131n\u0131 do\u011fal olarak modellemez<\/td>\n<\/tr>\n
    Autoencoder \/ LSTM-AE<\/strong><\/td>\nZaman serisi yeniden yap\u0131land\u0131rma hatas\u0131 ile anomali<\/td>\nVeri kalitesi, drift, a\u00e7\u0131klanabilirdik maliyeti<\/td>\n<\/tr>\n
    TCN \/ Transformer<\/strong><\/td>\nGecikme ve \u00e7ok de\u011fi\u015fkenli ili\u015fkileri yakalar<\/td>\nE\u011fitim ve bakim maliyeti y\u00fcksek<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Tablo 2: Anomali Tespit Y\u00f6ntemleri Kar\u015f\u0131la\u015ft\u0131rmas\u0131 [1][2]<\/em><\/p>\n

    \n

    \ud83d\udca1 Pratik Oneri: Uc Katmanli Hibrit Mimari<\/strong><\/p>\n

    Katman 1 (H\u0131z):<\/strong> \u0130statistiksel SPC\/EWMA ile hizli sinyal uretimi.<\/p>\n

    Katman 2 (Derinlik):<\/strong> ML ile anlamland\u0131rma ve s\u0131n\u0131fland\u0131rma (ariza mi, manip\u00fclasyon mu, process change mi).<\/p>\n

    Katman 3 (Tutarl\u0131l\u0131k):<\/strong> Fiziksel s\u00fcre\u00e7 modeli residual kontrol\u00fc (physics-in-the-loop).<\/p>\n

    Bu \u00fc\u00e7l\u00fc; h\u0131z + do\u011fruluk + a\u00e7\u0131klanabilirdik sa\u011flar[1][2]<\/sup>.<\/p>\n<\/div>\n

    Feature Engineering: SCADA Tag Selinden Anlaml\u0131 Sinyale<\/h1>\n

    ML kalitesini belirleyen \u015fey \u00e7o\u011fu zaman modelden \u00e7ok feature engineering’dir. SCADA verisi ham haliyle tag seli gibidir; anlaml\u0131 sinyale \u00e7evirmek gerekir[1]<\/sup>.<\/p>\n\n\n\n\n\n\n\n
    Feature Kategorisi<\/th>\n\u00d6rnek \u00d6zellikler<\/th>\nSiber Tespit De\u011feri<\/th>\n<\/tr>\n
    Zaman Alan\u0131 (Temel)<\/strong><\/td>\nRolling mean\/std, trend, rate-of-change, step-change<\/td>\nSetpoint s\u0131\u00e7ramas\u0131, drift tespiti<\/td>\n<\/tr>\n
    \u00c7ok De\u011fi\u015fkenli \u0130li\u015fkisel<\/strong><\/td>\nDebi\u2194guc, basinc\u2194debi, governor\u2194RPM\u2194frekans<\/td>\nSurec tutarlilik kontrolu, spoofing tespiti<\/td>\n<\/tr>\n
    Frekans Alan\u0131 (Titre\u015fim)<\/strong><\/td>\nFFT bant g\u00fc\u00e7leri (1X, 2X), spectral centroid, envelope<\/td>\nMekanik stres, kavitasyon, manip\u00fclasyon izi<\/td>\n<\/tr>\n
    Alarm\/Olay Logu<\/strong><\/td>\nAlarm burst rate, sequence pattern, ack gecikmeleri<\/td>\nAlarm susturma\/f\u0131rt\u0131na tespiti, sald\u0131r\u0131 izi<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Tablo 3: HES Odakl\u0131 Feature Engineering Kategorileri [1][2][3]<\/em><\/p>\n

    Kritik: Korelasyonu k\u00f6relmesine almak de\u011fil; mode-conditioned correlation<\/strong> kullanmak gerekir. Nominal \u00fcretimde beklenen ili\u015fki start-up modunda farkl\u0131d\u0131r[1]<\/sup>.<\/p>\n

    False Positive Y\u00f6netimi: Alarm \u00dcretmek Kolay, Operat\u00f6r\u00fc Yormamak Zor<\/h1>\n

    S\u00fcrekli yanl\u0131\u015f alarm \u00fcreten bir sistem, sahada alarm fatigue (operat\u00f6r ilgisizli\u011fi) veya esiklerin y\u00fckseltilerek k\u00f6rle\u015ftirme sonucunu do\u011furur[1][2]<\/sup>. Bunu y\u00f6netmek i\u00e7in \u00fc\u00e7 teknik:<\/p>\n\n\n\n\n\n\n
    Teknik<\/th>\nMekanizma<\/th>\nOrnek<\/th>\n<\/tr>\n
    \u00c7ok A\u015famal\u0131 Do\u011frulama<\/strong><\/td>\nStage-1: SPC aday + Stage-2: ML skor + Stage-3: s\u00fcre\u00e7 tutarl\u0131l\u0131k<\/td>\nDebi artm\u0131\u015f \u2192 rezervuar dS\/dt uyuyor mu? Bas\u0131n\u00e7 destekliyor mu?<\/td>\n<\/tr>\n
    Ba\u011flam Esikleri<\/strong><\/td>\nThreshold = f(mod, yuk, mevsim, ekipman_yas)<\/td>\nK\u0131\u015f debisi vs yaz debisi farkl\u0131 esikler<\/td>\n<\/tr>\n
    Alarm Birle\u015ftirme<\/strong><\/td>\nAyni root-cause alarmlar\u0131n\u0131 incident i\u00e7inde grupla<\/td>\nRiskScore = AnomalyScore \u00d7 AssetCriticality \u00d7 Exposure<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Tablo 4: False Positive Azaltma Teknikleri [1][2]<\/em><\/p>\n

    SIEM Entegrasyonu: OT Anomalisini Kurumsal Olay Y\u00f6netimine Ta\u015f\u0131mak<\/h2>\n

    SCADA anomali tespiti tek ba\u015f\u0131na dashboard olarak kal\u0131rsa etkisi s\u0131n\u0131rl\u0131d\u0131r. SIEM entegrasyonu iki prensip gerektirir: (1) OT telemetrisi IT telemetrisiyle korele edilmeli \u2014 \u00f6rne\u011fin ayn\u0131 zaman aral\u0131\u011f\u0131nda EWS uzaktan eri\u015fim + setpoint de\u011fi\u015fimi + debi\u2013g\u00fc\u00e7 tutars\u0131zl\u0131\u011f\u0131 sald\u0131r\u0131 olas\u0131l\u0131\u011f\u0131n\u0131 dramatik art\u0131r\u0131r[3][4]. (2) Olay format\u0131 standartla\u015ft\u0131r\u0131lmal\u0131: timestamp, asset_id, anomaly_type, confidence, severity, recommended_action, supporting_evidence.<\/p>\n

    Ger\u00e7ek Senaryo: Manip\u00fcle Edilmi\u015f Sensor + Kontrol Drift<\/h1>\n
    \n

    \ud83d\udca5 Sald\u0131r\u0131 Senaryosu<\/strong><\/p>\n

    Sald\u0131rgan m\u00fchendislik istasyonuna yetkisiz eri\u015fim elde ediyor. Ama\u00e7: t\u00fcrbini do\u011frudan durdurmak de\u011fil; verim d\u00fc\u015f\u00fc\u015f\u00fc yaratarak ekonomik kay\u0131p ve ekipman stresini art\u0131rmak[6]<\/sup>.<\/p>\n

    Adim 1:<\/strong> Debi sensorunu oldu\u011fundan y\u00fcksek g\u00f6steriyor \u2192 operat\u00f6r daha fazla \u00fcretim karar\u0131 al\u0131yor.<\/p>\n

    Adim 2:<\/strong> Governor setpoint’ini k\u00fc\u00e7\u00fck ad\u0131mlarla art\u0131r\u0131yor \u2192 klasik alarm esiklerini tetiklemeyecek kadar kademeli.<\/p>\n

    Fiziksel Sonu\u00e7:<\/strong> T\u00fcrbin verim e\u011frisi d\u0131\u015f\u0131na \u00e7\u0131kar; kavitasyon riski artar; titre\u015fim spektrumunda harmonik y\u00fckseli\u015f.<\/p>\n<\/div>\n

    Anomali Tespit Sistemi Bunu Nas\u0131l Yakalar?<\/h3>\n\n\n\n\n\n\n\n
    Tespit Katman\u0131<\/th>\nKontrol Mekanizmas\u0131<\/th>\nSonu\u00e7<\/th>\n<\/tr>\n
    \u00c7apraz Tutarl\u0131l\u0131k<\/strong><\/td>\nDebi artarken rezervuar dS\/dt artmal\u0131; artm\u0131yor<\/td>\nSensor spoof \u015f\u00fcphesi<\/td>\n<\/tr>\n
    Enerji Dengesi<\/strong><\/td>\nBeklenen g\u00fc\u00e7 \u2260 \u00f6l\u00e7\u00fclen g\u00fc\u00e7 \u2192 model residual art\u0131\u015f\u0131<\/td>\nFiziksel model alarm\u0131<\/td>\n<\/tr>\n
    Komut Paterni<\/strong><\/td>\nSetpoint de\u011fi\u015fimleri ola\u011fan d\u0131\u015f\u0131 s\u0131k\/ritmik<\/td>\nKomut frekans\u0131 anomalisi<\/td>\n<\/tr>\n
    Titre\u015fim Korelasyonu<\/strong><\/td>\nBeklenenden fazla harmonik art\u0131\u015f<\/td>\nMekanik stres sinyali<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Tablo 5: Sensor Spoofing + Control Drift Tespit Mekanizmalari [1][3]<\/em><\/p>\n

    Bu mekanizmalar birlikte \u00e7al\u0131\u015ft\u0131\u011f\u0131nda olay tek alarm de\u011fil, incident<\/strong> olarak toplan\u0131r: Suspected Sensor Spoofing + Control Drift (Confidence: High). Aksiyon: EWS eri\u015fim logu kontrol\u00fc, redundant sensor kar\u015f\u0131la\u015ft\u0131rma, setpoint kilidi, SIEM playbook tetikleme[3][4]<\/sup>.<\/p>\n

    Hydrowise Yakla\u015f\u0131m\u0131: AI Destekli Erken Uyar\u0131 Katman\u0131<\/h1>\n

    Hydrowise<\/strong>‘in fark yarattigi yer, anomali tespitini yalnizca guvenlik degil, ayni zamanda performans ve risk yonetimi katmanina baglamasidir. Platform, SCADA + IoT + piyasa verisini tek bir unified intelligence layer icinde toplayarak uc ciktiyi ayni cati altinda uretir: uretim tahmini, kestirimci bakim ve erken uyari.<\/p>\n\n\n\n\n\n\n\n
    #<\/th>\nAsama<\/th>\nA\u00e7\u0131klama<\/th>\n<\/tr>\n
    1<\/strong><\/td>\nAI-Powered Data Integration<\/strong><\/td>\nSCADA tag’leri, sens\u00f6rler ve IoT ak\u0131\u015flar\u0131 g\u00fcvenli \u015fekilde toplan\u0131r, normalize edilir, zaman senkronu sa\u011flan\u0131r<\/td>\n<\/tr>\n
    2<\/strong><\/td>\nAI-Powered Data Analysis<\/strong><\/td>\nBa\u011flam etiketleme (operasyon modlari), feature store, model skorlama pipeline \u00e7al\u0131\u015f\u0131r<\/td>\n<\/tr>\n
    3<\/strong><\/td>\nAI-Powered Forecasting<\/strong><\/td>\nPerformans sapmalar\u0131 (verim d\u00fc\u015f\u00fc\u015f\u00fc) ve g\u00fcvenlik anomalileri (spoof\/drift) ayr\u0131\u015ft\u0131rma skoru \u00fcretilir<\/td>\n<\/tr>\n
    4<\/strong><\/td>\nDecision Support<\/strong><\/td>\nOlaylar dashboard, bak\u0131m workflow, operasyon kararlar\u0131 ve SIEM olay y\u00f6netimine ba\u011flan\u0131r<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Tablo 6: Hydrowise Anomali Tespit Pipeline [1][4]<\/em><\/p>\n

    \n

    \ud83d\udd0d Hydrowise Kritik Tasar\u0131m \u0130lkeleri<\/strong><\/p>\n

    Explainable AI:<\/strong> “Alarm verdi” de\u011fil; “hangi sinyal hangi iliskiyi bozdu” a\u00e7\u0131klamas\u0131.<\/p>\n

    Model Drift Y\u00f6netimi:<\/strong> Mevsimsel de\u011fi\u015fime adaptif modeller; farkli i\u015fletme modlar\u0131na \u00f6zel alt modeller.<\/p>\n

    G\u00fcvenli Entegrasyon:<\/strong> IT\/OT ayr\u0131m\u0131n\u0131 bozmadan veri toplama; Purdue\/DMZ mimarisiyle uyum[5]<\/sup>.<\/p>\n

    Operasyonel \u00c7\u0131kt\u0131:<\/strong> Operat\u00f6re “ne yapmal\u0131y\u0131m?” cevab\u0131 veren aksiyon \u00f6nerileri.<\/p>\n<\/div>\n

    S\u0131k Sorulan Sorular (FAQ)<\/h1>\n

    S1: SCADA verisi tek ba\u015f\u0131na siber sald\u0131r\u0131y\u0131 yakalamaya yeter mi?<\/strong>
    \nYeterli olabilir; ancak do\u011fruluk art\u0131s\u0131 i\u00e7in eri\u015fim loglar\u0131, alarm\/komut loglari ve IoT titre\u015fim gibi ek telemetri ile korelasyon \u00f6nerilir[3][4]<\/sup>.<\/p>\n

    S2: ML modeli i\u00e7in etiketli sald\u0131r\u0131 verisi gerekiyor mu?<\/strong>
    \nHer zaman de\u011fil. One-class yakla\u015f\u0131mlar ve autoencoder normalden sapmay\u0131 \u00f6\u011frenebilir. Etiketli veri s\u0131n\u0131fland\u0131rma ve kok neden ayr\u0131\u015ft\u0131rmada faydal\u0131d\u0131r[1]<\/sup>.<\/p>\n

    S3: Konsept drift (mevsimsel de\u011fi\u015fim) modeli bozarsa?<\/strong>
    \nMod bazl\u0131 modeller, adaptif esikler ve periyodik yeniden e\u011fitim gerekir. Model health metrikleriyle drift izlenir[1][2]<\/sup>.<\/p>\n

    S4: False positive azaltman\u0131n en etkili yolu?<\/strong>
    \nTek bir y\u00f6ntem yetmez; \u00e7ok a\u015famal\u0131 do\u011frulama + ba\u011flam fark\u0131ndal\u0131\u011f\u0131 + alarm birle\u015ftirme birlikte tasarlanmal\u0131d\u0131r[1][2]<\/sup>.<\/p>\n

    S5: SIEM’e ham SCADA tag’lerini mi g\u00f6ndermeliyiz?<\/strong>
    \nHay\u0131r. Anlamland\u0131r\u0131lm\u0131\u015f olay nesneleri (incident\/event) g\u00f6ndermek do\u011frudur; ham veri SOC’u bo\u011far[4]<\/sup>.<\/p>\n

    S6: Sensor arizas\u0131 ile manip\u00fclasyonu nas\u0131l ay\u0131r\u0131r\u0131z?<\/strong>
    \nRedundant \u00f6l\u00e7\u00fcm, fiziksel s\u00fcre\u00e7 tutarl\u0131l\u0131\u011f\u0131, komut\/eri\u015fim korelasyonu ve zamansal patern analizi birlikte kullan\u0131l\u0131r[1][3]<\/sup>.<\/p>\n

    S7: Hydrowise bu s\u00fcrece neyi \u00fcr\u00fcnle\u015ftirir?<\/strong>
    \nVeri entegrasyonu, ba\u011flam etiketleme, anomali skorlama, a\u00e7\u0131klanabilirdik, olay y\u00f6netimi ve karar deste\u011fi katmanlar\u0131n\u0131 tek platformda birle\u015ftirir.<\/p>\n

    S8: Gercek zamanli sistemlerde gecikme nasil yonetilir?<\/strong>
    \nEdge on-isleme + merkezde a\u011f\u0131r analiz hibriti uygulanabilir. Kritik esikler edge’de h\u0131zl\u0131, derin analiz merkezde yap\u0131l\u0131r[1]<\/sup>.<\/p>\n

    Sonu\u00e7<\/h1>\n

    Ger\u00e7ek zamanl\u0131 anomali tespiti, HES’lerde yaln\u0131zca operasyonel sa\u011fl\u0131k g\u00f6stergesi de\u011fil, ayni zamanda siber sald\u0131r\u0131lar\u0131n fiziksel izlerini yakalayan erken uyar\u0131 hatt\u0131d\u0131r. Ba\u015far\u0131l\u0131 bir sistem; normalin ba\u011flam\u0131n\u0131 tan\u0131mlar, s\u00fcre\u00e7 modellemesiyle tutarl\u0131l\u0131k arar, hibrit skorlama yapar, false positive’i y\u00f6netir ve SIEM’e ba\u011flanir[1][2][4]<\/sup>.[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

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