Keamanan Jaringan dan Pengaruhnya terhadap Statistika: Pendekatan Analitik dan Praktis

Authors

  • Jhosua Ersa Arta Pratama Universitas Palangkaraya
  • Jhon Farel Manurung Universitas Palangkaraya
  • Rizki Muhamad Universitas Palangkaraya
  • Jadiaman Parhusip Universitas Palangkaraya

DOI:

https://doi.org/10.35870/ljit.v3i1.3454

Keywords:

Keamanan Jaringan; Statistika; Analisis Data; Big Data

Abstract

Network security is a critical aspect of information technology aimed at protecting data and systems from cyber threats. Statistical approaches play a key role in detecting anomalies, measuring efficiency, and predicting security risks. This paper explores the intersection between network security and statistics, emphasizing the use of data analysis and statistical methods to enhance system security. Furthermore, it discusses the challenges posed by big data processing and highlights the importance of machine learning in supporting adaptive security systems. The findings suggest that integrating traditional statistical methods with modern machine learning techniques can improve real-time threat detection and risk management in network security.

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Author Biographies

  • Jhosua Ersa Arta Pratama , Universitas Palangkaraya

    Teknik Informatika, Universitas Palangkaraya, Kota Palangkaraya, Indonesia

  • Jhon Farel Manurung , Universitas Palangkaraya

    Teknik Informatika, Universitas Palangkaraya, Kota Palangkaraya, Indonesia

  • Rizki Muhamad , Universitas Palangkaraya

    Teknik Informatika, Universitas Palangkaraya, Kota Palangkaraya, Indonesia

  • Jadiaman Parhusip, Universitas Palangkaraya

    Teknik Informatika, Universitas Palangkaraya, Kota Palangkaraya, Indonesia

References

A. Shadab, M. G. Jamil, and A. Mehmood, "Network anomaly detection techniques: A comprehensive survey," Computers, Materials & Continua, vol. 66, no. 2, pp. 1379-1405, 2021.

X. Zhang, L. Yang, Y. Chen, and Z. Liu, "A deep learning-based anomaly detection framework for network traffic," IEEE Access, vol. 9, pp. 52480-52490, 2021.

M. A. Arlitt and C. L. Williamson, "Anomaly detection for network traffic using Principal Component Analysis," Proceedings of the 1999 IEEE Symposium on Security and Privacy, 1999.

W. Wang, J. Xie, and X. Li, "Random Forest-based network intrusion detection system," Journal of Computer Networks and Communications, vol. 2020, pp. 1-8, 2020.

R. J. Hyndman and G. Athanasopoulos, "Forecasting: principles and practice," OTexts,

Published

2024-12-12

How to Cite

Pratama , J. E. A., Farel Manurung , J., Muhamad , R., & Parhusip, J. (2024). Keamanan Jaringan dan Pengaruhnya terhadap Statistika: Pendekatan Analitik dan Praktis. LANCAH: Jurnal Inovasi Dan Tren, 3(1), 7~12. https://doi.org/10.35870/ljit.v3i1.3454

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