Kullanım Kılavuzu
Neden sadece 3 sonuç görüntüleyebiliyorum?
Sadece üye olan kurumların ağından bağlandığınız da tüm sonuçları görüntüleyebilirsiniz. Üye olmayan kurumlar için kurum yetkililerinin başvurması durumunda 1 aylık ücretsiz deneme sürümü açmaktayız.
Benim olmayan çok sonuç geliyor?
Birçok kaynakça da atıflar "Soyad, İ" olarak gösterildiği için özellikle Soyad ve isminin baş harfi aynı olan akademisyenlerin atıfları zaman zaman karışabilmektedir. Bu sorun tüm dünyadaki atıf dizinlerinin sıkça karşılaştığı bir sorundur.
Sadece ilgili makaleme yapılan atıfları nasıl görebilirim?
Makalenizin ismini arattıktan sonra detaylar kısmına bastığınız anda seçtiğiniz makaleye yapılan atıfları görebilirsiniz.
 Görüntüleme 123
 İndirme 6
 Sesli Dinleme 1
Makine Öğrenmesi Yöntemleri Kullanılarak Oltalama Websitelerinin Tespiti
2018
Dergi:  
II. Uluslararası Multidisipliner Çalışmaları Kongresi
Yazar:  
Özet:

Phishing is to capture sensitive information such as username and password of online users through the impersonation of a trustworthy website by internet fraudsters. This information theft, which first appeared in 1996 and is described the victims as a fish, is called phishing. This problem, known as website phishing, is becoming more and more critical as the number of online payments increases, particularly in the electronic banking and electronic commerce sectors. The purpose of this work is to separate legitimate web sites from phishing websites using a database that UCI has published. There are 9 feature vectors and 1353 samples in the used database. The feature vector consists of information about the destination URL address. Samples are labeled in three classes: Legitimate, Suspicious, and Phishing. Collected features are converted into categorical values. For example: Legitimate, Suspicious, and Phishing are listed as 1, 0, and -1, respectively. The purpose of the study is to determine whether the visited website is phishing by giving the feature vector as input to known machine learning methods. As a result of the experimental studies made, all samples were classified with 90% accuracy.

Anahtar Kelimeler:

Identification of Statistical Websites By Using Machine Learning Methods
2018
Yazar:  
Özet:

Phishing is to capture sensitive information such as username and password of online users through the impersonation of a trustworthy website by internet fraudsters. This information theft, which first appeared in 1996 and is described the victims as a fish, is called phishing. This problem, known as website phishing, is becoming more and more critical as the number of online payments increases, in the electronic banking and electronic commerce sectors. The purpose of this work is to separate legitimate websites from phishing websites using a database that UCI has published. There are 9 feature vectors and 1353 samples in the used database. The feature vector consists of information about the destination URL address. Samples are labeled in three classes: Legitimate, Suspicious, and Phishing. Collected features are converted into category values. For example: Legitimate, Suspicious, and Phishing are listed as 1, 0, and -1, respectively. The purpose of the study is to determine whether the visited website is phishing by giving the feature vector as input to known machine learning methods. As a result of the experimental studies made, all samples were classified with 90% accuracy.

Anahtar Kelimeler:

Atıf Yapanlar
Bilgi: Bu yayına herhangi bir atıf yapılmamıştır.
Benzer Makaleler












II. Uluslararası Multidisipliner Çalışmaları Kongresi
II. Uluslararası Multidisipliner Çalışmaları Kongresi