Ransomware Detection in Cyber Security Domain

Ömer ASLAN

doi:https://doi.org/10.17798/bitlisfen.1038966

Makale detayları

Ransomware Detection in Cyber Security Domain

Yazarlar:

Ömer ASLAN

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 2022 Vol.11 Issue 2, pp.508-518

Belge Tipi:

Makale

Yayın Tarihi:

6/2022

Belge Dili:

İngilizce

Document ID (Org No.):

2000888219

Sıklık:

Üç Aylık

eISSN:

2147-3188

pISSN:

2147-3129

DOI:

https://doi.org/10.17798/bitlisfen.1038966

Anahtar kelimeler:

Özet:

 

In recent years, ransomware has become highly profitable cyber attacks. This is because, everyday there are several new devices attending to computer networks before testing their security strength. In addition, it is easy to launch ransomware attacks by using Ransomware-as-a-Service. This paper proposed a new method that creates the ransomware specific features by using ransomware behaviors which are performed on file, registry, and network resources. The weights are assigned to the behaviors based upon where the actions are performed. The most feasible features are selected based on the assigned weights as well as Information Gain. The selected features are classified by using ML classifiers including J48 (C4.5), RF (Random Forest), AdaBoost (Adaptive Boosting), SLR (Simple Logistic Regression), KNN (K-Nearest Neighbors), BN (Bayesian Network), and SMO (Sequential Minimal Optimization). The experiments are performed on several ransomware variants as well as benign samples. The test results show that our proposed method is feasible and effective. The DR, FPR, f-measure, and accuracy are measured as 100%, 1.4%, 99.4%, 99.38%, respectively.

 

In recent years, ransomware has become highly profitable cyber attacks. This is because, everyday there are several new devices attending to computer networks before testing their security strength. In addition, it is easy to launch ransomware attacks by using Ransomware-as-a-Service. This paper proposed a new method that creates the ransomware specific features by using ransomware behaviors which are performed on file, registry, and network resources. The weights are assigned to the behaviors based upon where the actions are performed. The most feasible features are selected based on the assigned weights as well as Information Gain. The selected features are classified by using ML classifiers including J48 (C4.5), RF (Random Forest), AdaBoost (Adaptive Boosting), SLR (Simple Logistic Regression), KNN (K-Nearest Neighbors), BN (Bayesian Network), and SMO (Sequential Minimal Optimization). The experiments are performed on several ransomware variants as well as benign samples. The test results show that our proposed method is feasible and effective. The DR, FPR, f-measure, and accuracy are measured as 100%, 1.4%, 99.4%, 99.38%, respectively.

Makale detayları

Ransomware Detection in Cyber Security Domain

Yazarlar:

Ömer ASLAN

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 2022 Vol.11 Issue 2, pp.508-518

Belge Tipi:

Makale

Yayın Tarihi:

6/2022

Belge Dili:

İngilizce

Document ID (Org No.):

2000888219

Sıklık:

Üç Aylık

eISSN:

2147-3188

pISSN:

2147-3129

DOI:

https://doi.org/10.17798/bitlisfen.1038966

Cyber security

ransomware

ransomware detection

behavior-based detection

Sayı

Makale detayları

Ransomware Detection in Cyber Security Domain

Yazarlar: Ömer ASLAN

Yayıncı: Bitlis Eren University

Ana Başlıklar: Uygulamalı fizik

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 2022 Vol.11 Issue 2, pp.508-518

Belge Tipi: Makale

Yayın Tarihi: 6/2022

Belge Dili: İngilizce

Document ID (Org No.): 2000888219

Sıklık: Üç Aylık

eISSN: 2147-3188

pISSN: 2147-3129

DOI: https://doi.org/10.17798/bitlisfen.1038966

Cyber security

ransomware

ransomware detection

behavior-based detection

Sayı

Yazarlar:

Ömer ASLAN

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 2022 Vol.11 Issue 2, pp.508-518

Belge Tipi:

Makale

Yayın Tarihi:

6/2022

Belge Dili:

İngilizce

Document ID (Org No.):

2000888219

Sıklık:

Üç Aylık

eISSN:

2147-3188

pISSN:

2147-3129

DOI:

https://doi.org/10.17798/bitlisfen.1038966