Systematic Literature Review Metode Data Science dalam Prediksi Kinerja dan Keamanan Jaringan Cloud
Keywords:
Systematic Literature Review, Data Science, Cloud Computing, Keamanan Siber, Prediksi Kinerja JaringanAbstract
Transformasi menuju cloud computing meningkatkan kompleksitas pengelolaan kinerja jaringan dan risiko ancaman keamanan siber, sehingga diperlukan pendekatan prediktif yang akurat dan adaptif. Penelitian ini menyajikan Systematic Literature Review (SLR) mengenai penerapan metode data science dalam prediksi kinerja jaringan dan deteksi ancaman keamanan siber pada lingkungan cloud. Tinjauan dilakukan mengikuti pedoman PRISMA terhadap publikasi periode 2015–2025 yang diindeks pada Scopus, IEEE Xplore, ACM Digital Library, dan ScienceDirect. Hasil kajian menunjukkan bahwa metode machine learning seperti Support Vector Machine dan Random Forest, serta deep learning seperti Convolutional Neural Network dan Long Short-Term Memory, mendominasi penelitian terkait. Teknik anomaly detection dan hybrid learning terbukti efektif dalam mengidentifikasi pola serangan kompleks pada infrastruktur cloud berskala besar. Namun, tantangan utama masih mencakup ketidakseimbangan data, keterbatasan generalisasi model, dan minimnya dataset terbuka. Studi ini memberikan pemetaan tren metodologis dan celah penelitian sebagai dasar pengembangan model prediktif yang lebih robust dan skalabel pada infrastruktur cloud.
References
[1] M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, Art. no. n71, 2021.
[2] M. A. Ferrag, L. Maglaras, H. Janicke, and R. Smith, “Security and privacy for cloud-based IoT: Challenges and future directions,” Future Generation Computer Systems, vol. 128, pp. 361–382, 2022.
[3] F. Khan, S. U. Rehman, and M. A. Khan, “Machine learning techniques for cybersecurity in cloud computing: A comprehensive review,” Computers & Security, vol. 124, Art. no. 102987, 2023.
[4] S. Alasmary, A. Khormali, and A. Anwar, “Deep learning-based intrusion detection systems: A systematic review,” IEEE Access, vol. 9, pp. 110203–110223, 2021.
[5] A. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A deep learning approach to network intrusion detection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 1, pp. 1–12, 2021.
[6] Y. Tang, Y. Liu, J. Zhang, and Q. Wang, “Deep learning-based traffic prediction for cloud data centers,” Journal of Network and Computer Applications, vol. 176, Art. no. 102919, 2021.
[7] R. Kumar, A. Goyal, and S. Sharma, “Network performance prediction in cloud computing using LSTM networks,” Future Internet, vol. 14, no. 2, Art. no. 45, 2022.
[8] A. Singh and S. Silakari, “An ensemble deep learning framework for DDoS attack detection in cloud environment,” Computers & Security, vol. 121, Art. no. 102832, 2022.
[9] J. Li, Z. Wang, and H. Chen, “Hybrid machine learning models for cloud network anomaly detection,” IEEE Access, vol. 10, pp. 78211–78225, 2022.
[10] M. A. Rahman, M. S. Hossain, and G. Muhammad, “Cloud security threat detection using explainable artificial intelligence,” IEEE Transactions on Network and Service Management, vol. 19, no. 4, pp. 4213–4226, 2022.
[11] H. Zhang, Y. Chen, and L. Zhou, “Real-time network performance prediction in cloud environments using deep reinforcement learning,” IEEE Systems Journal, vol. 17, no. 2, pp. 2456–2467, 2023.
[12] N. Moustafa and J. Slay, “CICIDS2023: A modern intrusion detection dataset for cloud and IoT environments,” IEEE Dataport, 2023.
[13] S. Verma, P. K. Sharma, and J. H. Park, “A scalable deep learning framework for cyber threat intelligence in cloud computing,” Journal of Cloud Computing, vol. 12, no. 1, Art. no. 67, 2023.
[14] A. Alzahrani and R. Kumar, “Explainable AI for intrusion detection systems in cloud environments,” Future Generation Computer Systems, vol. 146, pp. 423–435, 2024.
[15] Y. Liu, X. Li, and K. Li, “Integrated prediction of network performance and security threats in multi-cloud environments using deep learning,” IEEE Access, vol. 12, pp. 55421–55435, 2024.
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Copyright (c) 2025 Aliyah Aliyah, M. Adhit Dwi Yuda, Iwan Iwan (Author)
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