ANOMALİYALARIN AŞKARLANMASINDA MAŞIN TƏLİMİ METODLARININ EKSPERİMENTAL TƏDQİQİ - İnformasiya Texnologiyaları Problemləri

ANOMALİYALARIN AŞKARLANMASINDA MAŞIN TƏLİMİ METODLARININ EKSPERİMENTAL TƏDQİQİ - İnformasiya Texnologiyaları Problemləri

ANOMALİYALARIN AŞKARLANMASINDA MAŞIN TƏLİMİ METODLARININ EKSPERİMENTAL TƏDQİQİ - İnformasiya Texnologiyaları Problemləri

ANOMALİYALARIN AŞKARLANMASINDA MAŞIN TƏLİMİ METODLARININ EKSPERİMENTAL TƏDQİQİ - İnformasiya Texnologiyaları Problemləri

ANOMALİYALARIN AŞKARLANMASINDA MAŞIN TƏLİMİ METODLARININ EKSPERİMENTAL TƏDQİQİ - İnformasiya Texnologiyaları Problemləri
ANOMALİYALARIN AŞKARLANMASINDA MAŞIN TƏLİMİ METODLARININ EKSPERİMENTAL TƏDQİQİ - İnformasiya Texnologiyaları Problemləri
AZƏRBAYCAN MİLLİ ELMLƏR AKADEMİYASI

№1, 2022

ANOMALİYALARIN AŞKARLANMASINDA MAŞIN TƏLİMİ METODLARININ EKSPERİMENTAL TƏDQİQİ

Məkrufə Ş.Hacırəhimova, Leyla R. Yusifova

Son zamanlar kompüter şəbəkələrindən geniş istifadə şəbəkə təhdidləri və hücumlarının artmasına səbəb olmuşdur. Mövcud təhlükəsizlik sistemləri və alətləri isə hücumçuların şəbəkə infrastrukturuna olan hücumlarını aşkarlamaqda yetərli deyildir, ölçü, sürət və müxtəliflik baxımından böyük şəbəkə trafiki verilənlərinin saxlanması və analizi üçün köhnəlmiş hesab olunur. Şəbəkə trafiki verilənlərində anomaliyaların aşkarlanması şəbəkə təhlükəsizliyinin təmin edilməsində çox vacib məsələlərdəndir, həmçinin elmi tədqiqatların əsas istiqamətlərindəndir. Şəbəkə trafikində anomaliyaların aşkarlanması sahəsində kifayət qədər tədqiqatların olmasına baxmayaraq, daha dəqiq aşkarlama modellərinin işlənməsinə ehtiyac vardır. Məqalədə anomaliyaların aşkarlanmasında istifadə olunan bəzi maşın təlimi alqoritmləri analiz olunmuşdur. Kompüter şəbəkə trafiki verilənlərində anomaliyaların - DoS hücumların aşkarlanmasında maşın təlimi alqoritmlərinin istifadəsinin mümkünlüyü WEKA proqram platformasında eksperimental olaraq tədqiq edilmişdir. Təsnifatlandırma alqoritmlərinin effektivliyini artırmaq məqsədi ilə bir neçə klassifikasiya alqoritmlərindən təşkil olunmuş ansambl modeli təklif edilmişdir. Təklif olunan modelin effektivliyi NSL-KDD verilənlər bazası istifadə edilərək tədqiq olunmuşdur. Klassifikasiya alqoritmlərinin ayrı-ayrılıqda göstərdiyi nəticələrlə müqayisədə təklif olunan yanaşma anomaliyanın aşkarlanmasında daha yüksək dəqiqlik nümayiş etdirmişdir (səh.9-21).

Açar sözlər: Big data, Anomaliya, DoS hücum, IDS, Maşın təlimi, Klassifikasiya ansamblı
DOI : 10.25045/jpit.v13.i1.02
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