Improving Information Security with Machine Learning

Ahmad Sanmorino; Rendra Gustriansyah; Shinta Puspasari; Juhaini Alie

doi:10.47709/cnahpc.v6i1.3317

Authors

Ahmad Sanmorino Universitas Indo Global Mandiri
Rendra Gustriansyah Universitas Indo Global Mandiri
Shinta Puspasari Universitas Indo Global Mandiri
Juhaini Alie Universitas Indo Global Mandiri

DOI:

10.47709/cnahpc.v6i1.3317

Keywords:

Anomaly Detection, Ethical Considerations, Information Security, Machine Learning, Predictive Analytics

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Abstract

The study Improving Information Security with Machine Learning explores the fusion of machine learning methodologies within information security, aiming to fortify conventional protocols against evolving cyber threats. By conducting a comprehensive literature review and empirical analysis, this scholarly endeavor highlights the efficacy of machine learning in anomaly detection, threat identification, and predictive analytics within security frameworks. Through practical demonstrations, such as z-score-based anomaly detection in network traffic data and NLP-based email security systems, the study illustrates the practical applications of machine learning techniques. Additionally, it delves into the mathematical underpinnings of predictive analytics and the architecture of neural networks for malware detection. However, while showcasing the transformative potential of machine learning, the study also confronts significant challenges. Ethical, legal, and privacy considerations emerge prominently, emphasizing the need for regulations addressing algorithmic biases, ethical dilemmas, and data protection. Moreover, the study emphasizes the practical challenges of scalability, interpretability, continual adaptation to evolving threats, and the harmonious interaction between human expertise and machine intelligence. By offering practical recommendations and future research directions, this scholarly exploration aims to empower researchers, practitioners, and policymakers in navigating the complex intersection of machine learning and information security, thereby fostering innovation and comprehension in this evolving domain.

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References

Abdullayeva, F. (2023). Cyber resilience and cyber security issues of intelligent cloud computing systems. Results in Control and Optimization, 12(June), 100268. https://doi.org/10.1016/j.rico.2023.100268

Admass, W. S., Munaye, Y. Y., & Diro, A. A. (2024). Cyber security: State of the art, challenges and future directions. Cyber Security and Applications, 2(October 2023), 100031. https://doi.org/10.1016/j.csa.2023.100031

Ahmed, K., Khurshid, S. K., & Hina, S. (2024). CyberEntRel: Joint extraction of cyber entities and relations using deep learning. Computers and Security, 136(November 2023), 103579. https://doi.org/10.1016/j.cose.2023.103579

Bhayo, J., Shah, S. A., Hameed, S., Ahmed, A., Nasir, J., & Draheim, D. (2023). Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks. Engineering Applications of Artificial Intelligence, 123(April), 106432. https://doi.org/10.1016/j.engappai.2023.106432

Botta, A., Rotbei, S., Zinno, S., & Ventre, G. (2023). Cyber security of robots: A comprehensive survey. Intelligent Systems with Applications, 18(March), 200237. https://doi.org/10.1016/j.iswa.2023.200237

Cartwright, A., Cartwright, E., & Edun, E. S. (2023). Cascading information on best practice: Cyber security risk management in UK micro and small businesses and the role of IT companies. Computers and Security, 131. https://doi.org/10.1016/j.cose.2023.103288

Hossain, M. A., & Islam, M. S. (2023). Ensuring network security with a robust intrusion detection system using ensemble-based machine learning. Array, 19(May), 100306. https://doi.org/10.1016/j.array.2023.100306

Kim, Y., Lee, T., Hyun, Y., Coatanea, E., Mika, S., Mo, J., & Yoo, Y. J. (2023). Self-supervised representation learning anomaly detection methodology based on boosting algorithms enhanced by data augmentation using StyleGAN for manufacturing imbalanced data. Computers in Industry, 153(February), 104024. https://doi.org/10.1016/j.compind.2023.104024

Kinder, T., Stenvall, J., Koskimies, E., Webb, H., & Janenova, S. (2023). Local public services and the ethical deployment of artificial intelligence. Government Information Quarterly, 40(4), 101865. https://doi.org/10.1016/j.giq.2023.101865

Nazir, A., He, J., Zhu, N., Wajahat, A., Ma, X., Ullah, F., Qureshi, S., & Pathan, M. S. (2023). Advancing IoT security: A systematic review of machine learning approaches for the detection of IoT botnets. Journal of King Saud University - Computer and Information Sciences, 35(10), 101820. https://doi.org/10.1016/j.jksuci.2023.101820

Olukoya, O. (2022). Assessing frameworks for eliciting privacy & security requirements from laws and regulations. Computers and Security, 117, 102697. https://doi.org/10.1016/j.cose.2022.102697

Paya, A., Arroni, S., García-Díaz, V., & Gómez, A. (2024). Apollon: A robust defense system against Adversarial Machine Learning attacks in Intrusion Detection Systems. Computers and Security, 136(October 2023), 103546. https://doi.org/10.1016/j.cose.2023.103546

Ruiz-Villafranca, S., Roldán-Gómez, J., Carrillo-Mondéjar, J., Gómez, J. M. C., & Villalón, J. M. (2023). A MEC-IIoT intelligent threat detector based on machine learning boosted tree algorithms. Computer Networks, 233(June), 109868. https://doi.org/10.1016/j.comnet.2023.109868

Schäfer, F., Gebauer, H., Gröger, C., Gassmann, O., & Wortmann, F. (2023). Data-driven business and data privacy: Challenges and measures for product-based companies. Business Horizons, 66(4), 493–504. https://doi.org/10.1016/j.bushor.2022.10.002

Shin, H. S., Choi, S. Bin, & Kim, J. W. (2023). Harnessing highly efficient triboelectric sensors and machine learning for self-powered intelligent security applications. Materials Today Advances, 20(July), 100426. https://doi.org/10.1016/j.mtadv.2023.100426

Srinivasan, S., & P, D. (2023). Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learning. Measurement: Sensors, 25(October 2022), 100624. https://doi.org/10.1016/j.measen.2022.100624

Sun, Z., An, G., Yang, Y., & Liu, Y. (2024). Franklin Open Optimized machine learning enabled intrusion detection 2 system for internet of medical things. Franklin Open, 6(November 2023), 100056. https://doi.org/10.1016/j.fraope.2023.100056

Wazid, M., Das, A. K., Chamola, V., & Park, Y. (2022). Uniting cyber security and machine learning: Advantages, challenges and future research. ICT Express, 8(3), 313–321. https://doi.org/10.1016/j.icte.2022.04.007

Wu, W., Huang, T., & Gong, K. (2020). Ethical Principles and Governance Technology Development of AI in China. Engineering, 6(3), 302–309. https://doi.org/10.1016/j.eng.2019.12.015

Zhang, J., Yuan, Y., Zhang, J., Yang, Y., & Xie, W. (2023). Journal of King Saud University - Computer and Information Sciences Anomaly detection method based on penalty least squares algorithm and time window entropy for Cyber – Physical Systems. 35(November).