Abstract
Rising technology generates an increasing data flow on the internet by the day. Specific security systems are vital to manage this growing data traffic. One such security system is the firewall used in Turkey Maritime Enterprises Inc. (TME). All TME internet traffic is controlled by a security firewall that operates within existing rules. The security firewall produces output based on traffic categorized as “allowed” or “blocked.” This study gathered several log records from this firewall on five different days to create datasets. 26 variables were extracted from the firewall logs, excluding the output. The study aimed to identify the most significant parameters impacting the output using linear regression (LR) and principal component analysis (PCA). It was analyzed, and mutually influential log variables affecting the output were identified using two methods. After, the initial dataset with 26 variables and a reduced dataset with six variables were used to predict output using the artificial neural network (ANN) for five datasets. The prediction accuracy and precision ranges were between 85% and 88% and 92% and 98%, respectively. The F1-score showed results between 89% and 92% in addition to accuracy and precision. ML and PCA methods successfully identified crucial variables to estimate output and decreased the number of variables from 26 to 5. Moreover, it was noted that the ANN could decide whether the firewall traffic would be blocked or allowed with a high degree of accuracy based on the reduced datasets. Reducing the feature space from 26 to 6 variables identified via MLR+PCA improved ANN performance across hours, indicating that compact, interpretable inputs can support accurate firewall-traffic prediction.
Keywords
Cyber Security Firewall Log Linear Regression Principal Component Analysis Artificial Neural Networks
Ethical Statement
The author declares that this document does not require ethics committee approval or special permission. The author of the paper declares that he complies with the scientific, ethical, and quotation rules of SAUJS in all processes of the paper and that he does not make any falsification of the data collected. In addition, he declares that Sakarya University Journal of Science and its editorial board have no responsibility for any ethical violations that may be encountered and that this study has not been evaluated in any academic publication environment other than Sakarya University Journal of Science.
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Abstract
References
- H. N. K. Al-Bedahili, “Decision Tree for Multiclass of Firewall Access,” in International Journal of Intelligent Engineering and Systems, Vol. 14, no 3, pp. 294-302, 2021.
- İnternet Ortamında Yapılan Yayınların Düzenlenmes ve Bu Yayınlar Yoluyla İşlenen Suçlarla Mücadele Edilmesi Hakkında Kanun, 23 Mayıs 2007 26530 Sayılı Karar, Resmi Gazete, 2007
- E. Özhan, “Güvenlik Duvarı Günlüklerinin Makine Öğrenmesi Yöntemleri ile Analizi ve Bir Model Çıkartılması,” PhD Dissertation, Trakya University, Turkey, 2013.
- E. Akbaş, “Bilgi Güvenliği ve Log Yönetimi Sistemlerinin Analizi,”, Web Sayfası: http://www. academia. edu/9203287/Bilgi_G% C3% BCvenli% C4% 9Fi _ve_Log_Y% C3% B6netimi_Sistemlerinin_Analizi, 2017
- A. E. Mohammed, “Güvenlik Duvarı Kurallarına Ait Anomalilerin Tespiti ve Optimizasyonu,” M.S. Dissertation, Sakarya University, Turkey, 2018.
- Aljabri, M., Alahmadi, A. A., Mohammad, R. M. A., Aboulnour, M., Alomari, D. M., & Almotiri, S. H., “Classification of firewall log data using multiclass machine learning models,” in Electronics Vol. 11, No.12, 1851, 2022.
- Q. A. Al-Haija, A. Ishtaiwi, “Machine Learning Based Model to Identify Firewall Decisions to Improve Cyber-Defense,” International Journal on Advanced Science, Engineering and Information Technology, Vol. 11, No. 4, pp. 1688-1695, 2021.
- F. Ertam, M. Kaya, “Classification of Firewall Log Files With Multiclass Support Vector Machine,” in International Symposium on Digital Forensic and Security (ISDFS), Antalya, Turkey, 2018, pp. 1-4.
- B. A. Al-Tarawneh, H. Bani-Salameh, “Classification of Firewall Logs Actions Using Machine Learning Techniques and Deep Neural Network,” in AIP Conference Proceedings, Jordan, Amman, 2023, Vol. 2979, No. 1.
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- K. Ovaz Akpinar, I. Ozcelik, “Anomaly detection on EtherCAT based water level control automation,” in 2020 5th International Conference on Computer Science and Engineering (UBMK), Diyarbakir, Turkey, 2020, pp. 79-82.
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- M. Şahin, “Uygulama Katmanı için Güvenlik Duvarı Geliştirilmesi,” M.S. Dissertation, Gebze Teknik University, Turkey, 2017.
- C. Lillmond, G. Suddul, “A Deep Neural Network Approach for Analysis of Firewall Log Data,” in International Conference on Advances in Computing and Technology (ICACT–2021) Faculty of Computing and Technology (FCT), University of Kelaniya, Sri Lanka, 2021, pp. 42-46.
- S. Applebaum, T. Gaber, A. Ahmed, “Signature-based and Machine Learning-based Web Application Firewalls: A Short Survey” in Procedia Computer Science, Vol. 189, pp. 359-367, 2021.
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- A. Shaheed, M. H. D. Bassam Kurdy, “Werb Application Firewall Using Machine Learning and Features Engineering,”, in Security and Communication Networks, Vol. 2022, 2021.
- E. Tosunoğlu, R. Yılmaz, E. Özeren, Z. Sağlam, “Eğitimde Makine Öğrenmesi: Araştırmalardaki Güncel Eğilimler Üzerine İnceleme”, in Ahmet Keleşoğlu Eğitim Fakültesi Dergisi, Vol. 2. No., 2., 2021.
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- A. Şenol, Y. Canbay, M. Kaya, “Trends in Outbreak Detection in Early Stage by Using Machine Learning Approaches,” in Bilişim Teknolojileri Dergisi, Vol. 14. No. 4, pp. 355-366, 2021.
- D. H. Maulud, A. M. Abdulazeez, “A Review on Linear Regression Comprehensive in Machine Learning”, in Journal of Applied Science and Technology Trends. Vol. 1, No. 4, pp. 140-147, 2020.
- K.P. Burnham, Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research, 33, 2, 261–304, 2004.
- S. Makridakis, S.C. Wheelwright, R.J. Hyndman, Forecasting: Methods and Applications, NY, USA, John Wiley: New York, 2008.
- İ. M. Nasser, M. O. Al-Shawwa, S. S. Abu-Naser, “A Proposed Artifical Neural Network for Predicting Movies Rates Category” in Interrnational Journal of Academic Engineering Research (IJAER), Vol. 3. No. 2, pp. 21-25, 2019.
- M. Kayakuş, M. Terzioğlu, “Yapay Sinir Ağları ve Çoklu Doğrusal Regresyon Kullanarak Emeklilik Fonu Net Varlık Değerlerinin Tahmin Edilmesi”, Bilişim Teknolojileri Dergisi, Vol. 14, No. 1, pp. 95-103, 2021.
- I. T. Jolliffe, Principal component analysis: With 28 illustrations. New York, USA: Springer, 2002.
- T. Hastie, J. Friedman, R. Tisbshirani, The elements of Statistical Learning: Data Mining, Inference, and prediction. New York: Springer, 2017.
- A. Akhtar, S. Akhtar, B. Bakhtawar, A. A. Kashif, N. Aziz, M. S. Javeid, “Covid-19 Detection From CBC Using Machine Learning Techniques,” in International Journal of Technology, Innovation and Management (IJTIM), Vol. 1, No. 2, pp. 65-78, 2021.
Details
| Primary Language | English |
|---|---|
| Subjects | Software Engineering (Other) |
| Journal Section | Research Article |
| Authors | |
| Early Pub Date | October 13, 2025 |
| Publication Date | October 16, 2025 |
| Submission Date | May 12, 2025 |
| Acceptance Date | September 1, 2025 |
| Published in Issue | Year 2025 Volume: 8 Issue: 4 |
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