Classification of VPN/NoVPN and Tor/NoTor Using CIC-Darknet2020 Dataset in Cybersecurity: Utilizing Simple and Complex Models

Yusuf Alaca

Makale detayları

Classification of VPN/NoVPN and Tor/NoTor Using CIC-Darknet2020 Dataset in Cybersecurity: Utilizing Simple and Complex Models

Yazarlar:

Yusuf Alaca

Yayıncı:

Fırat University

Ana Başlıklar:

Mühendislik

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.35 Issue 2, pp.569-579

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001287552

pISSN:

1308-9072

Anahtar kelimeler:

Özet:

 Internet usage is rapidly increasing today, and many transactions are being carried out in the digital environment. However, this situation also paves the way for the misuse of the internet. Cybercrimes and attacks are increasing day by day, and the issue of cybersecurity has become extremely important. The CIC-Darknet2020 dataset, prepared by researchers working in the field of cybersecurity, contains traffic occurring in Darknet networks. The analysis of this traffic can provide important information about activities on Darknet networks. In this study, VPNNoVPN and TorNoTor classification were made using models on the CIC-Darknet2020 dataset. The classification results obtained using OneR and Ensemble OneR models were examined. The results showed that the ROC-AUC value of the Ensemble OneR model was 0.779 for VPNNoVPN classification. For TorNoTor classification, the Ensemble OneR model achieved excellent results with a ROC-AUC value of 0.980. This study demonstrates that even simple models can achieve significant results in the field of cybersecurity and are usable. However, the use of more complex models also becomes necessary. It is concluded that both simple and complex models need to be used in the field of cybersecurity. In conclusion, the results obtained from the studies conducted on the CIC-Darknet2020 dataset demonstrate the possibility of performing VPNNoVPN and TorNoTor classification using different models in the field of cybersecurity. Although the results of this study emphasize the necessity of using more complex models, they also demonstrate that even simple models can achieve significant results.

 Internet usage is rapidly increasing today, and many transactions are being carried out in the digital environment. However, this situation also paves the way for the misuse of the internet. Cybercrimes and attacks are increasing day by day, and the issue of cybersecurity has become extremely important. The CIC-Darknet2020 dataset, prepared by researchers working in the field of cybersecurity, contains traffic occurring in Darknet networks. The analysis of this traffic can provide important information about activities on Darknet networks. In this study, VPNNoVPN and TorNoTor classification were made using models on the CIC-Darknet2020 dataset. The classification results obtained using OneR and Ensemble OneR models were examined. The results showed that the ROC-AUC value of the Ensemble OneR model was 0.779 for VPNNoVPN classification. For TorNoTor classification, the Ensemble OneR model achieved excellent results with a ROC-AUC value of 0.980. This study demonstrates that even simple models can achieve significant results in the field of cybersecurity and are usable. However, the use of more complex models also becomes necessary. It is concluded that both simple and complex models need to be used in the field of cybersecurity. In conclusion, the results obtained from the studies conducted on the CIC-Darknet2020 dataset demonstrate the possibility of performing VPNNoVPN and TorNoTor classification using different models in the field of cybersecurity. Although the results of this study emphasize the necessity of using more complex models, they also demonstrate that even simple models can achieve significant results.

 Internet usage is rapidly increasing today, and many transactions are being carried out in the digital environment. However, this situation also paves the way for the misuse of the internet. Cybercrimes and attacks are increasing day by day, and the issue of cybersecurity has become extremely important. The CIC-Darknet2020 dataset, prepared by researchers working in the field of cybersecurity, contains traffic occurring in Darknet networks. The analysis of this traffic can provide important information about activities on Darknet networks. In this study, VPNNoVPN and TorNoTor classification were made using models on the CIC-Darknet2020 dataset. The classification results obtained using OneR and Ensemble OneR models were examined. The results showed that the ROC-AUC value of the Ensemble OneR model was 0.779 for VPNNoVPN classification. For TorNoTor classification, the Ensemble OneR model achieved excellent results with a ROC-AUC value of 0.980. This study demonstrates that even simple models can achieve significant results in the field of cybersecurity and are usable. However, the use of more complex models also becomes necessary. It is concluded that both simple and complex models need to be used in the field of cybersecurity. In conclusion, the results obtained from the studies conducted on the CIC-Darknet2020 dataset demonstrate the possibility of performing VPNNoVPN and TorNoTor classification using different models in the field of cybersecurity. Although the results of this study emphasize the necessity of using more complex models, they also demonstrate that even simple models can achieve significant results.

 İnternet kullanımı günümüzde hızla artmakta ve birçok işlem dijital ortamda gerçekleştirilmektedir. Ancak, bu durum aynı zamanda internetin kötüye kullanımına zemin hazırlamaktadır. Siber suçlar ve saldırılar her geçen gün artmaktadır ve siber güvenlik konusu son derece önemli hale gelmiştir. CIC-Darknet2020 adlı veri seti, siber güvenlik alanında çalışan araştırmacılar tarafından hazırlanmış ve Darknet ağlarında gerçekleşen trafiği içermektedir. Bu trafiğin analizi, Darknet ağlarındaki faaliyetler hakkında önemli bilgiler sağlayabilmektedir. Bu çalışmada, CIC-Darknet2020 veri seti üzerinde modeller kullanılarak VPNNoVPN ve TorNoTor sınıflandırması yapılmıştır. OneR ve Ensemble OneR modelleri kullanılarak yapılan sınıflandırma sonuçları incelenmiştir. Sonuçlar, VPNNoVPN sınıflandırması için Ensemble OneR modelinin ROC-AUC değerinin 0.779 olduğunu göstermiştir. TorNoTor sınıflandırması için ise Ensemble OneR modeli, son derece iyi sonuçlar elde ederek ROC-AUC değeri 0.980 olmuştur. Bu çalışma, siber güvenlik alanında basit modellerin bile önemli sonuçlar elde edebileceğini ve kullanılabilir olduğunu göstermektedir. Ancak, daha karmaşık modellerin kullanımının da gerekliliği ortaya çıkmaktadır. Siber güvenlik alanında hem basit hem de karmaşık modellerin kullanılması gerektiği sonucuna varılmaktadır. Sonuç olarak, CIC-Darknet2020 veri seti üzerinde yapılan çalışmalar sonucunda elde edilen sonuçlar siber güvenlik alanında farklı modeller kullanarak VPNNoVPN ve TorNoTor sınıflandırması yapılabilirliğini göstermektedir. Bu çalışmanın sonuçları, daha karmaşık modellerin kullanımının gerekliliği ortaya koysa da, basit modellerin bile önemli sonuçlar elde edebileceğini göstermektedir.

Makale detayları

Yazarlar: Yusuf Alaca

Yayıncı: Fırat University

Ana Başlıklar: Mühendislik

Dergi: Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.35 Issue 2, pp.569-579

Belge Tipi: Makale

Yayın Tarihi: 1/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001287552

pISSN: 1308-9072

Sayı

Yazarlar:

Yusuf Alaca

Yayıncı:

Fırat University

Ana Başlıklar:

Mühendislik

Dergi:

Fırat Üniversitesi Mühendislik Bilimleri Dergisi Vol.35 Issue 2, pp.569-579

Belge Tipi:

Makale

Yayın Tarihi:

1/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001287552

pISSN:

1308-9072