Öğrencilerin Siber Güvenlik Farkındalık Düzeylerinin Makine Öğrenmesi Yöntemleri ile Belirlenmesi

Year 2023, Volume: 28 Issue: 2, 451 - 466, 31.08.2023

Mahmut Tokmak

https://doi.org/10.53433/yyufbed.1181694

Abstract

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.

Keywords

Makine öğrenmesi, Siber güvenlik, Siber güvenlik farkındalık düzeyi

Determination of Cyber Security Awareness Levels of Students with Machine Learning Methods

Abstract

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.

Keywords

Cyber security, Cyber security awareness level, Machine learning

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