Araştırma Makalesi
BibTex RIS Kaynak Göster

Makine Öğrenimi Yöntemlerini Kullanarak Kötü Amaçlı Yazılımların Statik Analiz ile Tespiti

Yıl 2023, , 27 - 35, 30.12.2023
https://doi.org/10.54047/bibted.1309960

Öz

Siber saldırılardaki artış internet ve bilişim teknolojileri kullanımını da tehdit etmeye başlamıştır. Bu durum, siber saldırılardan sorumlu kötü amaçlı yazılımları tespit etmenin önemini vurgulamaktadır. Günümüzde, kötü amaçlı yazılımları algılamak için makine öğrenmesi yöntemlerinin geliştirilmesi üzerine çalışmalar bulunmaktadır. Kötü amaçlı yazılım dedektörleri, kötü amaçlı yazılımlara karşı savunmada birincil araçlardır. Böyle bir dedektörün kalitesi, kullandığı tekniklerle belirlenir. Makine öğrenmesi, derin öğrenme ve statik ve dinamik analiz gibi zararlı yazılım analiz yöntemleri bu teknikler arasında yer almaktadır. Bu çalışma kötü amaçlı yazılım analizi ve sınıflandırma tekniklerini sunmaktadır. Kötü amaçlı yazılım tespiti için, K-En Yakın Komşular, Saf Bayes, Karar Ağaçları ve Rastgele Orman gibi iyi bilinen makine öğrenmesi algoritmaları kullanılmıştır. Çalışma, Karar Ağaçları sınıflandırma tekniği kullanımının %97,75 sınıflandırma ile en iyi doğruluğu ürettiğini, Saf Bayes'in ise %53 ile en düşük doğruluğu ürettiğini göstermektedir.

Kaynakça

  • Azeez, N. A., Odufuwa, O. E., Misra, S., Oluranti, J., & Damaševičius, R. (2021). Windows PE malware detection using ensemble learning. In Informatics (Vol. 8, No. 1, p. 10). MDPI.
  • Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
  • Chumachenko, K. (2017). Machine learning methods for malware detection and classification.
  • Deshpande, N. M., Gite, S., & Aluvalu, R. (2021). A review of microscopic analysis of blood cells for disease detection with AI perspective. PeerJ Computer Science, 7, e460.
  • Gandotra, E., Bansal, D., & Sofat, S. (2014). Malware analysis and classification: A survey. Journal of Information Security, 2014.
  • Harshalatha, P., & Mohanasundaram, R. (2020). Classification Of Malware Detection Using Machine Learn-ing Algorithms: A Survey. International Journal of Scientific & Technology Research, 9(02).
  • Hassen, M., Carvalho, M. M., & Chan, P. K. (2017, November). Malware classification using static analysis based features. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE.
  • Maimon, O., & Rokach, L. (Eds.). (2005). Data mining and knowledge discovery handbook.
  • Markel, Z., & Bilzor, M. (2014, October). Building a machine learning classifier for malware detection. In 2014 second workshop on anti-malware testing research (WATeR) (pp. 1-4). IEEE.
  • Patil, R., & Deng, W. (2020, March). Malware analysis using machine learning and deep learning techniques. In 2020 SoutheastCon (Vol. 2, pp. 1-7). IEEE.
  • Santos, I., Devesa, J., Brezo, F., Nieves, J., & Bringas, P. G. (2013). Opem: A static-dynamic approach for machine-learning-based malware detection. In International joint conference CISIS’12-ICEUTE´ 12-SOCO´ 12 special sessions (pp. 271-280). Springer Berlin Heidelberg.
  • Sapountzoglou, N., Lago, J., & Raison, B. (2020). Fault diagnosis in low voltage smart distribution grids using gradient boosting trees. Electric Power Systems Research, 182, 106254.
  • TAHTACI, B., & CANBAY, B. (2020, October). Android malware detection using machine learning. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-6). IEEE.
  • Tian, R., Batten, L., Islam, R., & Versteeg, S. (2009, October). An automated classification system based on the strings of trojan and virus families. In 2009 4th International conference on malicious and unwanted software (MALWARE) (pp. 23-30). IEEE.
  • Yang, F. J. (2018, December). An implementation of naive bayes classifier. In 2018 International conference on computational science and computational intelligence (CSCI) (pp. 301-306). IEEE.

Detection of Malware by Static Analysis Using Machine Learning Methods

Yıl 2023, , 27 - 35, 30.12.2023
https://doi.org/10.54047/bibted.1309960

Öz

The increase in cyber-attacks has also started to threaten the use of internet and information technologies. This situation emphasizes the importance of detecting malicious software that is responsible for cyber-attacks. Nowadays, there are studies on the development of machine learning methods for malicious software detection. Malicious software detectors are the primary tools in defense against malicious software. The quality of such a detector is determined by the techniques it uses. Malware analysis methods such as machine learning, deep learning, and static and dynamic analysis are among these techniques. This study presents malware analysis and classification techniques. For malware detection, well-known algorithms for machine learning including such K-Nearest Neighbors, Naive Bayes, Decision Trees, and Random Forest were used. The research shows that the use of Random Forest classification technique produces the best accuracy with 97.75% classification, while Naive Bayes produces the lowest accuracy of 53%.

Kaynakça

  • Azeez, N. A., Odufuwa, O. E., Misra, S., Oluranti, J., & Damaševičius, R. (2021). Windows PE malware detection using ensemble learning. In Informatics (Vol. 8, No. 1, p. 10). MDPI.
  • Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
  • Chumachenko, K. (2017). Machine learning methods for malware detection and classification.
  • Deshpande, N. M., Gite, S., & Aluvalu, R. (2021). A review of microscopic analysis of blood cells for disease detection with AI perspective. PeerJ Computer Science, 7, e460.
  • Gandotra, E., Bansal, D., & Sofat, S. (2014). Malware analysis and classification: A survey. Journal of Information Security, 2014.
  • Harshalatha, P., & Mohanasundaram, R. (2020). Classification Of Malware Detection Using Machine Learn-ing Algorithms: A Survey. International Journal of Scientific & Technology Research, 9(02).
  • Hassen, M., Carvalho, M. M., & Chan, P. K. (2017, November). Malware classification using static analysis based features. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE.
  • Maimon, O., & Rokach, L. (Eds.). (2005). Data mining and knowledge discovery handbook.
  • Markel, Z., & Bilzor, M. (2014, October). Building a machine learning classifier for malware detection. In 2014 second workshop on anti-malware testing research (WATeR) (pp. 1-4). IEEE.
  • Patil, R., & Deng, W. (2020, March). Malware analysis using machine learning and deep learning techniques. In 2020 SoutheastCon (Vol. 2, pp. 1-7). IEEE.
  • Santos, I., Devesa, J., Brezo, F., Nieves, J., & Bringas, P. G. (2013). Opem: A static-dynamic approach for machine-learning-based malware detection. In International joint conference CISIS’12-ICEUTE´ 12-SOCO´ 12 special sessions (pp. 271-280). Springer Berlin Heidelberg.
  • Sapountzoglou, N., Lago, J., & Raison, B. (2020). Fault diagnosis in low voltage smart distribution grids using gradient boosting trees. Electric Power Systems Research, 182, 106254.
  • TAHTACI, B., & CANBAY, B. (2020, October). Android malware detection using machine learning. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1-6). IEEE.
  • Tian, R., Batten, L., Islam, R., & Versteeg, S. (2009, October). An automated classification system based on the strings of trojan and virus families. In 2009 4th International conference on malicious and unwanted software (MALWARE) (pp. 23-30). IEEE.
  • Yang, F. J. (2018, December). An implementation of naive bayes classifier. In 2018 International conference on computational science and computational intelligence (CSCI) (pp. 301-306). IEEE.
Toplam 15 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Öğrenme (Diğer), Siber Güvenlik ve Gizlilik (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Nisa Vuran Sarı 0000-0001-7042-3031

Mehmet Acı 0000-0002-7245-8673

Erken Görünüm Tarihi 30 Ağustos 2023
Yayımlanma Tarihi 30 Aralık 2023
Gönderilme Tarihi 6 Haziran 2023
Kabul Tarihi 8 Ağustos 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Vuran Sarı, N., & Acı, M. (2023). Detection of Malware by Static Analysis Using Machine Learning Methods. Bilgisayar Bilimleri Ve Teknolojileri Dergisi, 4(2), 27-35. https://doi.org/10.54047/bibted.1309960