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Öğrencilerin Siber Güvenlik Farkındalık Düzeylerinin Makine Öğrenmesi Yöntemleri ile Belirlenmesi

Yıl 2023, Cilt: 28 Sayı: 2, 451 - 466, 31.08.2023
https://doi.org/10.53433/yyufbed.1181694

Öz

Bilgi ve iletişim teknolojilerinin hızla gelişmesi ile birlikte teknoloji ve interneti kullanan cihaz sayısı artmış ve hayatın her alanına girmiştir. Teknolojideki gelişmeler kullanıcıların ve cihazların siber tehditlerle karşılaşma riskini de beraberinde getirmiştir. Bu çalışma; siber tehditlerle ilgili, öğrencilerin siber güvenlik farkındalık düzeylerini makine öğrenme yöntemleri ile tespit etmeyi amaçlamaktadır. Bu nedenle istatistiksel olarak lisans öğrencilerini temsil eden örnek bir kitleden anket tekniğiyle veri toplanmıştır. Elde edilen veriler, betimsel tarama modeli benimsenerek analiz edilmiş ve analiz sonuçları çalışmada ortaya konmuştur. Sonrasında anket verilerinden oluşturulan veri seti ile Naive Bayes, Karar Ağacı, Rastgele Orman, En Yakın Komşu, XGBoost, Gradient Boost, Destek Vektör Makineleri, Çok Katmanlı Algılayıcı algoritmaları kullanılarak öğrencilerin siber güvenlik farkındalık düzeylerinin tespiti yapılmıştır. Yapılan testler sonucunda 0.7-0.98 arasında değişen doğruluk değerleri, 0.7-0.96 arasında değişen F1 skorları elde edilmiştir. En başarılı performans metrikleri 0.98 doğruluk ve 0.96 F1-skoru ile Çok Katmanlı Algılayıcı algoritması ile elde edilmiştir.

Kaynakça

  • Abomhara, M., & Køien, G. M. (2015). Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks. Journal of Cyber Security and Mobility, 4(1), 65-88.
  • Aldayel, M. S. (2012, December). K-Nearest Neighbor classification for glass identification problem. 2012 International Conference on Computer Systems and Industrial Informatics, Sharjah, United Arab Emirates. doi:10.1109/ICCSII.2012.6454522
  • Alzahrani, L. (2021). Statistical analysis of cybersecurity awareness issues in higher education institutes. International Journal of Advanced Computer Science and Applications, 12(11), 630-637. doi:10.14569/IJACSA.2021.0121172
  • Arpaci, I., & Sevinc, K. (2022). Development of the cybersecurity scale (CS-S): Evidence of validity and reliability. Information Development, 38(2), 218-226. doi:10.1177/0266666921997512
  • Balan, S., Gawand, S., & Purushu, P. (2018). Application of machine learning classification algorithm to cybersecurity awareness. Information Technology & Management Science (RTU Publishing House), 21, 45-48. doi:10.7250/itms-2018-0006
  • Berrar, D. (2018). Bayes’ theorem and naive Bayes classifier. In S. Ranganathan, M. Gribskov, K. Nakai, & C. Schönbach (Eds.), Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics (pp. 403-412). Amsterdam, The Netherlands: Elsevier Science Publisher. doi:10.1016/B978-0-12-809633-8.20473-1
  • Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. doi:10.1023/A:1010933404324
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco California, USA. doi:10.1145/2939672.2939785
  • D’Silva, J. L., Samah, B. A., Shaffril, H. A. M., & Hassan, M. A. (2010). Factors that influence attitude towards ICT usage among rural community leaders in Malaysia. Australian Journal of Basic and Applied Sciences, 4(10), 5214-5220.
  • Fraenkel, J. R., Wallen, N. E., & Hyun, H. (2012). How to design and evaluate research in education (C. 7). New York, USA: McGraw-hill Education.
  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. doi:10.1214/aos/1013203451
  • Gibert, D., Mateu, C., & Planes, J. (2020). HYDRA: A multimodal deep learning framework for malware classification. Computers & Security, 95, 101873. doi:10.1016/j.cose.2020.101873
  • Gündüzalp, C. (2021). Üniversite çalışanlarının dijital veri ve kişisel siber güvenlik farkındalıkları (bilgi i̇şlem daire başkanlıkları örneği). Journal of Computer and Education Research, 9(18), 598-625. doi:10.18009/jcer.907022
  • Hassan, M. A., Samah, B. A., Shaffril, H. M., & D’Silva, J. L. (2011). Perceived usefulness of ICT usage among JKKK members in Peninsular Malaysia. Asian Social Science, 7(10). doi:10.5539/ass.v7n10p255
  • Humayun, M., Niazi, M., Jhanjhi, N. Z., Alshayeb, M., & Mahmood, S. (2020). Cyber security threats and vulnerabilities: A systematic mapping study. Arabian Journal for Science and Engineering, 45(4), 3171-3189. doi:10.1007/s13369-019-04319-2
  • IWS. (2022). Internet World Stats. https://www.internetworldstats.com/europa2.htm#tr Erişim Tarihi: 18 Ağustos 2022.
  • İlker, K. (2019). Kaba kuvvet saldırı tespiti ve teknik analizi. Sakarya University Journal of Computer and Information Sciences, 2(2), 61-69. doi:10.35377/saucis.02.02.561844
  • Jabeur, S. B., Mefteh-Wali, S., & Viviani, J.-L. (2021). Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annals of Operations Research, 1-21. doi:10.1007/s10479-021-04187-w
  • Karacı, A., Akyüz, H. İ., & Bilgici, G. (2017). Üniversite öğrencilerinin siber güvenlik davranışlarının incelenmesi. Kastamonu Eğitim Dergisi, 25(6), 2079-2094. doi:10.24106/kefdergi.351517
  • Karakaya, A., & Yetgin, M. A. (2020). Karabük üni̇versi̇tesi̇ çalışanlarına yöneli̇k ki̇şi̇sel si̇ber güvenli̇k üzeri̇ne araştırma. Kahramanmaraş Sütçü İmam Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(2), 157-172. doi:10.47147/ksuiibf.816171
  • Khan, F., Ncube, C., Ramasamy, L. K., Kadry, S., & Nam, Y. (2020). A digital DNA sequencing engine for ransomware detection using machine learning. IEEE Access, 8, 119710-119719. doi:10.1109/ACCESS.2020.3003785
  • Khan, N. F., Ikram, N., Murtaza, H., & Asadi, M. A. (2021). Social media users and cybersecurity awareness: Predicting self-disclosure using a hybrid artificial intelligence approach. Kybernetes, 52(1), 401-421. doi:10.1108/K-05-2021-0377
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  • Patel, H. H., & Prajapati, P. (2018). Study and analysis of decision tree based classification algorithms. International Journal of Computer Sciences and Engineering, 6(10), 74-78. doi:10.26438/ijcse/v6i10.7478
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Determination of Cyber Security Awareness Levels of Students with Machine Learning Methods

Yıl 2023, Cilt: 28 Sayı: 2, 451 - 466, 31.08.2023
https://doi.org/10.53433/yyufbed.1181694

Öz

With the rapid development of information and communication technologies, the number of devices using technology and the internet has increased and has entered all areas of life. Developments in technology have brought the risk of users and devices encountering cyber threats. This work aims to determine students' cyber security awareness levels about cyber threats with machine learning methods. Therefore, data were collected from a sample population that was statistically representative of undergraduate students with the survey technique. The obtained data were analyzed by adopting the descriptive review model and the results of the analysis were presented in the study. Afterwards, the cyber security awareness levels of the students were determined by using the data set created from the survey data, Naive Bayes, Decision Tree, Random Forest, Nearest Neighbor, XGBoost, Gradient Boost, Support Vector Machines, Multi-Layer Perceptron algorithms. As a result of the tests performed, accuracy values ranging from 0.7-0.98 and F1 scores ranging from 0.7-0.96 has been obtained. The most successful performance metrics were obtained with the Multi-Layer Perceptron algorithm with an accuracy of 0.98 and an F1 score of 0.96.

Kaynakça

  • Abomhara, M., & Køien, G. M. (2015). Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks. Journal of Cyber Security and Mobility, 4(1), 65-88.
  • Aldayel, M. S. (2012, December). K-Nearest Neighbor classification for glass identification problem. 2012 International Conference on Computer Systems and Industrial Informatics, Sharjah, United Arab Emirates. doi:10.1109/ICCSII.2012.6454522
  • Alzahrani, L. (2021). Statistical analysis of cybersecurity awareness issues in higher education institutes. International Journal of Advanced Computer Science and Applications, 12(11), 630-637. doi:10.14569/IJACSA.2021.0121172
  • Arpaci, I., & Sevinc, K. (2022). Development of the cybersecurity scale (CS-S): Evidence of validity and reliability. Information Development, 38(2), 218-226. doi:10.1177/0266666921997512
  • Balan, S., Gawand, S., & Purushu, P. (2018). Application of machine learning classification algorithm to cybersecurity awareness. Information Technology & Management Science (RTU Publishing House), 21, 45-48. doi:10.7250/itms-2018-0006
  • Berrar, D. (2018). Bayes’ theorem and naive Bayes classifier. In S. Ranganathan, M. Gribskov, K. Nakai, & C. Schönbach (Eds.), Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics (pp. 403-412). Amsterdam, The Netherlands: Elsevier Science Publisher. doi:10.1016/B978-0-12-809633-8.20473-1
  • Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. doi:10.1023/A:1010933404324
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco California, USA. doi:10.1145/2939672.2939785
  • D’Silva, J. L., Samah, B. A., Shaffril, H. A. M., & Hassan, M. A. (2010). Factors that influence attitude towards ICT usage among rural community leaders in Malaysia. Australian Journal of Basic and Applied Sciences, 4(10), 5214-5220.
  • Fraenkel, J. R., Wallen, N. E., & Hyun, H. (2012). How to design and evaluate research in education (C. 7). New York, USA: McGraw-hill Education.
  • Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. doi:10.1214/aos/1013203451
  • Gibert, D., Mateu, C., & Planes, J. (2020). HYDRA: A multimodal deep learning framework for malware classification. Computers & Security, 95, 101873. doi:10.1016/j.cose.2020.101873
  • Gündüzalp, C. (2021). Üniversite çalışanlarının dijital veri ve kişisel siber güvenlik farkındalıkları (bilgi i̇şlem daire başkanlıkları örneği). Journal of Computer and Education Research, 9(18), 598-625. doi:10.18009/jcer.907022
  • Hassan, M. A., Samah, B. A., Shaffril, H. M., & D’Silva, J. L. (2011). Perceived usefulness of ICT usage among JKKK members in Peninsular Malaysia. Asian Social Science, 7(10). doi:10.5539/ass.v7n10p255
  • Humayun, M., Niazi, M., Jhanjhi, N. Z., Alshayeb, M., & Mahmood, S. (2020). Cyber security threats and vulnerabilities: A systematic mapping study. Arabian Journal for Science and Engineering, 45(4), 3171-3189. doi:10.1007/s13369-019-04319-2
  • IWS. (2022). Internet World Stats. https://www.internetworldstats.com/europa2.htm#tr Erişim Tarihi: 18 Ağustos 2022.
  • İlker, K. (2019). Kaba kuvvet saldırı tespiti ve teknik analizi. Sakarya University Journal of Computer and Information Sciences, 2(2), 61-69. doi:10.35377/saucis.02.02.561844
  • Jabeur, S. B., Mefteh-Wali, S., & Viviani, J.-L. (2021). Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annals of Operations Research, 1-21. doi:10.1007/s10479-021-04187-w
  • Karacı, A., Akyüz, H. İ., & Bilgici, G. (2017). Üniversite öğrencilerinin siber güvenlik davranışlarının incelenmesi. Kastamonu Eğitim Dergisi, 25(6), 2079-2094. doi:10.24106/kefdergi.351517
  • Karakaya, A., & Yetgin, M. A. (2020). Karabük üni̇versi̇tesi̇ çalışanlarına yöneli̇k ki̇şi̇sel si̇ber güvenli̇k üzeri̇ne araştırma. Kahramanmaraş Sütçü İmam Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10(2), 157-172. doi:10.47147/ksuiibf.816171
  • Khan, F., Ncube, C., Ramasamy, L. K., Kadry, S., & Nam, Y. (2020). A digital DNA sequencing engine for ransomware detection using machine learning. IEEE Access, 8, 119710-119719. doi:10.1109/ACCESS.2020.3003785
  • Khan, N. F., Ikram, N., Murtaza, H., & Asadi, M. A. (2021). Social media users and cybersecurity awareness: Predicting self-disclosure using a hybrid artificial intelligence approach. Kybernetes, 52(1), 401-421. doi:10.1108/K-05-2021-0377
  • Khonji, M., Iraqi, Y., & Jones, A. (2013). Phishing detection: A literature survey. IEEE Communications Surveys & Tutorials, 15(4), 2091-2121. doi:10.1109/SURV.2013.032213.00009
  • Kovačević, A., Putnik, N., & Tošković, O. (2020). Factors related to cyber security behavior. IEEE Access, 8, 125140-125148. doi:10.1109/ACCESS.2020.3007867
  • Li, Y., Nie, X., & Huang, R. (2018). Web spam classification method based on deep belief networks. Expert Systems with Applications, 96, 261-270. doi:10.1016/j.eswa.2017.12.016
  • Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Analyzing the effectiveness of semi-supervised learning approaches for opinion spam classification. Applied Soft Computing, 101, 107023. doi:10.1016/j.asoc.2020.107023
  • Makkar, A., & Kumar, N. (2021). PROTECTOR: An optimized deep learning-based framework for image spam detection and prevention. Future Generation Computer Systems, 125, 41-58. doi:10.1016/j.future.2021.06.026
  • Makki, S., Assaghir, Z., Taher, Y., Haque, R., Hacid, M.-S., & Zeineddine, H. (2019). An experimental study with imbalanced classification approaches for credit card fraud detection. IEEE Access, 7, 93010-93022. doi:10.1109/ACCESS.2019.2927266
  • Mittal, S., & Tyagi, S. (2019, January). Performance evaluation of machine learning algorithms for credit card fraud detection. 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). doi:10.1109/CONFLUENCE.2019.8776925
  • Muhirwe, J., & White, N. (2016). Cybersecurity awareness and practice of next generation corporate technology users. Issues in Information Systems, 17(2), 183-192. doi:10.48009/2_iis_2016_183-192
  • Narudin, F. A., Feizollah, A., Anuar, N. B., & Gani, A. (2016). Evaluation of machine learning classifiers for mobile malware detection. Soft Computing, 20(1), 343-357. doi:10.1007/s00500-014-1511-6
  • Nusrat, F., Uzbaş, B., & Baykan, Ö. K. (2020). Prediction of diabetes mellitus by using gradient boosting classification. Avrupa Bilim ve Teknoloji Dergisi, Ejosat Special Issue 2020, 268-272. https://doi.org/10.31590/ejosat.803504
  • Özbek, Y. (2019). Öğretmen adaylarının siber güvenlik farkındalıklarının incelenmesi. (Doktora Tezi), Necmettin Erbakan Üniversitesi, Eğitim Bilimleri Enstitüsü, Konya, Türkiye.
  • Patel, H. H., & Prajapati, P. (2018). Study and analysis of decision tree based classification algorithms. International Journal of Computer Sciences and Engineering, 6(10), 74-78. doi:10.26438/ijcse/v6i10.7478
  • Pisner, D. A., & Schnyer, D. M. (2020). Chapter 6 - Support Vector Machine. A. Mechelli & S. Vieira (Ed.), Machine Learning (pp. 101-121). Cambridge, USA: Academic Press. doi:10.1016/B978-0-12-815739-8.00006-7
  • Potur, E. A., & Erginel, N. (2021). Kalp yetmezliği hastalarının sağ kalımlarının sınıflandırma algoritmaları ile tahmin edilmesi. Avrupa Bilim ve Teknoloji Dergisi, (24), 112-118. doi:10.31590/ejosat.902357
  • Quayyum, F., Cruzes, D. S., & Jaccheri, L. (2021). Cybersecurity awareness for children: A systematic literature review. International Journal of Child-Computer Interaction, 30, 100343. doi:10.1016/j.ijcci.2021.100343
  • Ramli, S. A. B., Omar, S. Z., Bolong, J., D’Silva, J. L., & Shaffril, H. A. M. (2013). Influence of behavioral factors on mobile phone usage among fishermen: The case of Pangkor Island Fishermen. Asian Social Science, 9(5), 162. doi:10.5539/ass.v9n5p162
  • Safa, N. S., Von Solms, R., & Futcher, L. (2016). Human aspects of information security in organisations. Computer Fraud & Security, 2016(2), 15-18. doi:10.1016/S1361-3723(16)30017-3
  • Sahingoz, O. K., Buber, E., Demir, O., & Diri, B. (2019). Machine learning based phishing detection from URLs. Expert Systems with Applications, 117, 345-357. doi:10.1016/j.eswa.2018.09.029
  • Saridewi, V. S., & Sari, R. F. (2021). Implementation of machine learning for human aspect in information security awareness. Journal of Applied Engineering Science, 19(4), 1126-1142. doi:10.5937/jaes0-28530
  • Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: An overview from machine learning perspective. Journal of Big Data, 7, 41. doi:10.1186/s40537-020-00318-5
  • Shahrivari, V., Darabi, M. M., & Izadi, M. (2020). Phishing detection using machine learning techniques. arXiv preprint arXiv:2009.11116. doi:10.48550/arXiv.2009.11116
  • Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., Chen, S., Liu, D., & Li, J. (2020). Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies, 13(10), 2509. doi:10.3390/en13102509
  • Srinivasan, S., Ravi, V., Alazab, M., Ketha, S., Al-Zoubi, A., & Padannayil, S. K. (2021). Spam emails detection based on distributed word embedding with deep learning. In Y. Maleh, M. Shojafar, M. Alazab, & Y. Baddi (Eds.), Machine Intelligence and Big Data Analytics for Cybersecurity Applications (pp. 161-189). Switzerland: Springer Cham.
  • Subramaniam, S. R. (2017, December). Cyber security awareness among Malaysian pre-university students. Proceeding of the 6th Global Summit on Education, Kualalumpur, Malasia.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Boston, USA: Pearson Education.
  • TÜİK. (2021). Türkiye İstatistik Kurumu. https://data.tuik.gov.tr/Bulten/Index?p=Hanehalki-Bilisim-Teknolojileri-(BT)-Kullanim-Arastirmasi-2021-37437 Erişim Tarihi: 18 Ağustos 2022.
  • Venkatraman, S., Alazab, M., & Vinayakumar, R. (2019). A hybrid deep learning image-based analysis for effective malware detection. Journal of Information Security and Applications, 47, 377-389. doi:10.1016/j.jisa.2019.06.006
  • Von Solms, R., & Van Niekerk, J. (2013). From information security to cyber security. Computers & Security, 38, 97-102. doi:10.1016/j.cose.2013.04.004
  • Weamie, S. J. (2022). Cross-site scripting attacks and defensive techniques: A comprehensive survey. International Journal of Communications, Network and System Sciences, 15(8), 126-148. doi:10.4236/ijcns.2022.158010
  • Xuan, S., Liu, G., Li, Z., Zheng, L., Wang, S., & Jiang, C. (2018, March). Random forest for credit card fraud detection. 2018 IEEE 15th international conference on networking, sensing and control (ICNSC), Zhuhai, China. doi:10.1109/ICNSC.2018.8361343
  • Xue, D., Li, J., Lv, T., Wu, W., & Wang, J. (2019). Malware classification using probability scoring and machine learning. IEEE Access, 7, 91641-91656. doi:10.1109/ACCESS.2019.2927552
  • Yalçınkaya, M. A., & Küçüksille, E. (2021). Web uygulama sızma testlerinde kapsam genişletme işlemi için metodoloji geliştirilmesi ve uygulanması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 25(1), 16-27. doi:10.19113/sdufenbed.661867
  • Yiğit, M. F., & Seferoğlu, S. S. (2019). Öğrencilerin siber güvenlik davranışlarının beş faktör kişilik özellikleri ve çeşitli diğer değişkenlere göre incelenmesi. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 15(1), 186-215. doi:10.17860/mersinefd.437610
  • Zhou, J., Qiu, Y., Khandelwal, M., Zhu, S., & Zhang, X. (2021). Developing a hybrid model of Jaya algorithm-based extreme gradient boosting machine to estimate blast-induced ground vibrations. International Journal of Rock Mechanics and Mining Sciences, 145, 104856. doi:10.1016/j.ijrmms.2021.104856
Toplam 56 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Mühendislik ve Mimarlık / Engineering and Architecture
Yazarlar

Mahmut Tokmak 0000-0003-0632-4308

Yayımlanma Tarihi 31 Ağustos 2023
Gönderilme Tarihi 29 Eylül 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 28 Sayı: 2

Kaynak Göster

APA Tokmak, M. (2023). Öğrencilerin Siber Güvenlik Farkındalık Düzeylerinin Makine Öğrenmesi Yöntemleri ile Belirlenmesi. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 28(2), 451-466. https://doi.org/10.53433/yyufbed.1181694