USING MACHINE LEARNING ALGORITHMS TO DETECT MALICIOUS ADVERTISEMENTS ON WEB PAGES

The article examines the problem of the spread of malicious advertising programs through web pages that pose a serious threat to the privacy and security of Internet users. Using machine learning algorithms to detect and neutralize malicious advertising programs embedded in Web pages. By focusing on data processing, tag extraction, and classification techniques, machine learning analyzes in detail how it can improve malware detection processes. Various machine learning algorithms, including logistic regression, decision trees, random forest, naive Bayesian and ensemble methods, are being studied to determine their effectiveness in distinguishing malicious and legitimate advertising content.
A methodology for building training and test models, including data on malicious and secure advertising modules, is described. Various approaches to machine learning, including teacher-led learning, unsupervised learning, and deep learning techniques, are being analyzed to identify hidden patterns of harmful behavior. The results of the study show that the use of machine learning algorithms makes it possible to detect malicious advertising programs with high accuracy, which can become the basis for the development of more effective cybersecurity tools. Potential problems and limitations of existing methods are also discussed, as well as directions for further research on detecting malicious advertising programs using machine learning.

1. Oshingbesan A. Detection of Malicious Websites Using Machine Learning Techniques. Cryptography and Security (cs.CR) / A. Oshingbesan, et al // Machine Learning (cs.LG)/ – 2022. Vol. 1. http://dx.doi.org/10.13140/RG.2.2.30165.14565.

2. Akhtar M.S. Malware Analysis and Detection Using Machine Learning Algorithms. / M. S. Akhtar & T. Feng // Symmetry. – 2022. – № 14(11). – Р. 2304. http://dx.doi.org/10.3390/sym14112304.

3. Saxe J. Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features / J. Saxe & K. Berlin // 10th International Conference on Malicious and Unwanted Software (MALWARE). – 2015. http://dx.doi.org/10.1109/MALWARE.2015.7413680.

4. Shijo P.V. Integrated Static and Dynamic Analysis for Malware Detection / P.V. Shijo & A. Salim // Procedia Computer Science. – 2015. – № 46. Р. 804-811. http://dx.doi.org/10.1016/j.procs.2015.02.149.

5. Alazab M. Malware Detection Systems: The Need for Machine Learning Algorithms / M. Alazab M. et al // Springer Nature. – 2020. http://dx.doi.org/10.1007/978-981-16-7618-5_53.

6. Shin S.-S. A Heterogeneous Machine Learning Ensemble Framework for Malicious Webpage Detection / S.-S. Shin, S.-G. Ji & S.-S. Hong // Applied Sciences. – 2022. – № 12(23). Р. 12070. http://dx.doi.org/10.3390/app10030936.

7. Anis F.M. Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI) / F.M. Anis, R.M. Aljuaid & R. Baageel // Sustainability. – 2022. – № 14(12). Р. 7375. http://dx.doi.org/10.3390/su14127375.

8. Khan T.A. Significance of Machine Learning for Detection of Malicious Websites on an Unbalanced Dataset / T.A Khan & R. Kouatly // Digital. – 2020. № 2(4). Р. 501-519. http://dx.doi.org/10.3390/digital2040027.

9. Akhtar M.S. Malware Analysis and Detection Using Machine Learning Algorithms / M.S. Akhtar & T. Feng // Symmetry. – 2022. № 14(11). Р. 2304. http://dx.doi.org/10.3390/sym14112304.

10. Detection of Malicious Websites Using Machine Learning Techniques / A. Oshingbesan et al. – 2022. http://dx.doi.org/10.13140/RG.2.2.30165.14565.