DNS TUNNEL DETECTION WITH ARTIFICIAL INTELLIGENCE / (Kayıt no. 290544)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 03112nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20230710115442.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 230710d2023 cy ||||| m||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | CY-NiCIU |
| Kataloglama Dili | eng |
| Çeviri Kurumu | CY-NiCIU |
| Açıklama Kuralları | rda |
| 041 ## - DİL KODU | |
| Metin ya da ses kaydının dil kodu | eng |
| 090 ## - Yerel Tasnif No | |
| tasnif no | YL 2893 |
| Cutter no | E93 2023 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Eyabi, Gideon Ebi |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | DNS TUNNEL DETECTION WITH ARTIFICIAL INTELLIGENCE / |
| Sorumluluk Bildirimi | GIDEON EBI EYABI; SUPERVISOR: ASST. PROF. DR. DEVRIM SERAL |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | viii, 50 sheets; |
| Boyutları | 31 cm. |
| Birlikteki Materyal | Includes CD |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| Content type code | txt |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| Media type code | n |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| Carrier type code | nc |
| 502 ## - TEZ NOTU | |
| Tez Notu | Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering Department |
| 504 ## - BİBLİYOGRAFİ NOTU | |
| Bibliyografi Notu | Includes bibliography (sheets 49-50) |
| 520 ## - ÖZET NOTU | |
| Özet notu | ABSTRACT<br/>The network protocol that translates human-readable names like afraid.com into <br/>computational figures like IP addresses that the computer can understand is called <br/>DNS. Without DNS, it would be impossible to memorize all the IP addresses of <br/>various sites on the internet. In the OSI hierarchy of layers, the DNS appears in the <br/>application layer. The DNS uses the UDP and TCP to transmit data.<br/>In this paper, the effects of DNS tunneling on corporate networks will be investigated <br/>and a possible solution will be approached using artificial intelligence. Basically, this <br/>paper will focus on the use of models like K Nearest neighbors, Gaussian Naïve Bias, <br/>and the Decision tree Classifiers. These 3 models were chosen due to their individual <br/>capabilities. The Nearest K Neighbors is best for its ability to store trained data, and <br/>ease the process by letting the algorithm almost bypass the trained dataset phase <br/>directly to the testing phase. Thus, when using the K Nearest neighbor as the chosen <br/>algorithm and a new test data x is observed, it immediately searches new data in the <br/>trained data closest to the data x and gets a prediction. Thus, limiting the phase of <br/>training each time new data is presented. Also, Decision tree was used because it has <br/>the ability to group similar data in the trained phase as nodes, such that if new data is <br/>presented at the test data, it searches prediction based on the closeness of the data to <br/>defined node groups. Also, the Gaussian Naïve Bias uses some probability functions <br/>to predict future events. Thus, having the knowledge of the trained data B, the Naïve <br/>Bias will be able to predict the probability P(A/B). The above algorithms were chosen <br/>because they give predictions similar to what is expected in DNS tunnel detection. <br/>DNS tunneling various ways through which the tunnels are setup on target systems. <br/>One way is through email poisoning, another is through malicious messages and so <br/>on. These classifies best fit these scenarios as they are fully equipped for feature <br/>predictions based on stored trained data. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Internet domain names |
| Alt başlık biçimi | Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Computer networks |
| Alt başlık biçimi | Dissertations, Academic |
| Genel Alt Konu | Security measures |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) | |
| Sınıflama Kaynağı | Dewey Onlu Sınıflama Sistemi |
| Materyal Türü | Thesis |
| Geri Çekilme Durumu | Kayıp Durumu | Sınıflandırma Kaynağı | Kredi için değil | Koleksiyon Kodu | Kalıcı Konum | Mevcut Konum | Raf Yeri | Kayıt Tarih | Source of acquisition | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu | Toplam Ödünçverme |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Tez Koleksiyonu | 10.07.2023 | Bağış | YL 2893 E93 2023 | T3276 | 10.07.2023 | 10.07.2023 | Thesis | Computer Engineering Department | ||||
| Dewey Onlu Sınıflama Sistemi | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 10.07.2023 | Bağış | YL 2893 E93 2023 | CDT3276 | 10.07.2023 | 10.07.2023 | Suppl. CD | Computer Engineering Department |