Utilizing Machine Learning for Anomaly Detection in Cybersecurity Systems

Authors

  • Budi Utami Fahnun Universitas Gunadarma, Indonesia
  • Eel Susilowati Universitas Gunadarma, Indonesia
  • Mardhi Fadlillah Universitas Gunadarma, Indonesia
  • Irawaty Universitas Gunadarma, Indonesia

Keywords:

Machine Learning, Anomaly, Cybersecurity System.

Abstract

Anomalies in cybersecurity systems are increasingly complex and sophisticated, making detection difficult using traditional rule-based and signature-based approaches. In facing these challenges, machine learning is crucial to improve real-time anomaly detection capabilities. This study aims to explore the role of machine learning in detecting anomalies in cybersecurity systems. The research method is carried out using a qualitative approach, collecting data from relevant literature and interviews with experts in the fields of cybersecurity and machine learning. The results of this study indicate that machine learning can effectively improve the ability of cybersecurity systems to detect and respond to threats more quickly and accurately. Implementing machine learning allows for deeper analysis of complex cybersecurity data, recognizing unexpected anomalous patterns, and adapting to new attacks. Despite challenges such as data variability and dynamic operational environments, the evaluation of model performance shows significant progress in protecting information systems from increasingly complex threats. The future of anomaly detection in cybersecurity promises the possibility of developing more sophisticated technologies, strengthening defenses against evolving threats, and improving overall security.

References

Akalin, N., & Loutfi, A. (2021). Reinforcement learning approaches in social robotics. Sensors, 21(4), 1292.

Al-amri, R., Murugesan, R. K., Man, M., Abdulateef, A. F., Al-Sharafi, M. A., & Alkahtani, A. A. (2021). A review of machine learning and deep learning techniques for anomaly detection in IoT data. Applied Sciences, 11(12), 5320.

Aslan, Ö., Aktuğ, S. S., Ozkan-Okay, M., Yilmaz, A. A., & Akin, E. (2023). A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions. Electronics, 12(6), 1333.

Balaji, T. K., Annavarapu, C. S. R., & Bablani, A. (2021). Machine learning algorithms for social media analysis: A survey. Computer Science Review, 40, 100395.

Corallo, A., Lazoi, M., & Lezzi, M. (2020). Cybersecurity in the context of industry 4.0: A structured classification of critical assets and business impacts. Computers in industry, 114, 103165.

Djenna, A., Harous, S., & Saidouni, D. E. (2021). Internet of things meet internet of threats: New concern cyber security issues of critical cyber infrastructure. Applied Sciences, 11(10), 4580.

Dong, S. (2021). Multi class SVM algorithm with active learning for network traffic classification. Expert Systems with Applications, 176, 114885.

Heidari, A., & Jabraeil Jamali, M. A. (2023). Internet of Things intrusion detection systems: a comprehensive review and future directions. Cluster Computing, 26(6), 3753-3780.

Jacobsen, J. T. (2021). Cyber offense in NATO: challenges and opportunities. International affairs, 97(3), 703-720.

Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023). Artificial intelligence for cybersecurity: Literature review and future research directions. Information Fusion, 97, 101804.

Klenka, M. (2021). Aviation cyber security: legal aspects of cyber threats. Journal of transportation security, 14(3), 177-195.

Nartin, S. E., Faturrahman, S. E., Ak, M., Deni, H. A., MM, C., Santoso, Y. H., ... & Eliyah, S. K. (2024). Metode penelitian kualitatif. Cendikia Mulia Mandiri.

Omolara, A. E., Alabdulatif, A., Abiodun, O. I., Alawida, M., Alabdulatif, A., & Arshad, H. (2022). The internet of things security: A survey encompassing unexplored areas and new insights. Computers & Security, 112, 102494.

Omuya, E. O., Okeyo, G. O., & Kimwele, M. W. (2021). Feature selection for classification using principal component analysis and information gain. Expert Systems with Applications, 174, 114765.

Pandey, S., Singh, R. K., Gunasekaran, A., & Kaushik, A. (2020). Cyber security risks in globalized supply chains: conceptual framework. Journal of Global Operations and Strategic Sourcing, 13(1), 103-128.

Quatrini, E., Costantino, F., Di Gravio, G., & Patriarca, R. (2020). Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities. Journal of Manufacturing Systems, 56, 117-132.

Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Ali Naqvi, R. (2021). Machine learning (ML) in medicine: Review, applications, and challenges. Mathematics, 9(22), 2970.

Saxena, N., Hayes, E., Bertino, E., Ojo, P., Choo, K. K. R., & Burnap, P. (2020). Impact and key challenges of insider threats on organizations and critical businesses. Electronics, 9(9), 1460.

Smith, R., Friston, K. J., & Whyte, C. J. (2022). A step-by-step tutorial on active inference and its application to empirical data. Journal of mathematical psychology, 107, 102632.

Sturgeon, T. J. (2021). Upgrading strategies for the digital economy. Global strategy journal, 11(1), 34-57.

Sujith, A. V. L. N., Qureshi, N. I., Dornadula, V. H. R., Rath, A., Prakash, K. B., & Singh, S. K. (2022). A comparative analysis of business machine learning in making effective financial decisions using structural equation model (SEM). Journal of Food Quality, 2022(1), 6382839.

Taye, M. M. (2023). Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers, 12(5), 91.

Trim, P. R., & Lee, Y. I. (2021). The global cyber security model: counteracting cyber attacks through a resilient partnership arrangement. Big Data and Cognitive Computing, 5(3), 32.

Turk, Ž., de Soto, B. G., Mantha, B. R., Maciel, A., & Georgescu, A. (2022). A systemic framework for addressing cybersecurity in construction. Automation in Construction, 133, 103988.

Vercio, L. L., Amador, K., Bannister, J. J., Crites, S., Gutierrez, A., MacDonald, M. E., ... & Forkert, N. D. (2020). Supervised machine learning tools: a tutorial for clinicians. Journal of Neural Engineering, 17(6), 062001.

Downloads

Published

10-07-2024

How to Cite

Fahnun, B. U. ., Susilowati, E. ., Fadlillah, M. ., & Irawaty. (2024). Utilizing Machine Learning for Anomaly Detection in Cybersecurity Systems. ENDLESS: INTERNATIONAL JOURNAL OF FUTURE STUDIES, 7(1), 249–260. Retrieved from http://endless-journal.com/index.php/endless/article/view/276