Analisis Keamanan Data pada Sistem Informasi Berbasis Cloud Computing
DOI:
https://doi.org/10.54314/jssr.v%25vi%25i.4570Abstract
Abstract:Â Data security is crucial in the digital era, where cloud computing has become the primary solution for organizations to efficiently store and process data. However, the increasing use of cloud computing also poses significant risks related to data security and privacy. The purpose of this study is to identify challenges and solutions in maintaining data security in cloud computing systems. The method used is a systematic literature review and comparative analysis of various trusted academic sources. The results reveal key threats such as misconfigurations, unauthorized access, and data leaks, which are exacerbated by weak identity management. The study also evaluates mitigation mechanisms such as end-to-end encryption, multi-factor authentication, and a shared responsibility model between cloud service providers and users. These findings can guide the design of effective and adaptive security strategies and policies to support a secure and sustainable digital transformation.
Â
Keyword:Â data security;Â information systems;Â cloud computing;Â encryption;Â authentication.
Downloads
References
DAFTAR PUSTAKA
Abou-Nassar, E. M., Iliyasu, A. M., El-Kafrawy, P. M., Song, O.-Y., Bashir, A. K., & El-Latif, A. A. A. (2020). DITrust Chain: Towards Blockchain-Based Trust Models for Sustainable Healthcare IoT Systems. IEEE Access, 8, 111223–111238. https://doi.org/10.1109/ACCESS.2020.2999468
Ahmed, I., Jeon, G., & Piccialli, F. (2022). From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where. IEEE Transactions on Industrial Informatics, 18(8), 5031–5042. https://doi.org/10.1109/TII.2022.3146552
Alam, T. (2021). Cloud-Based IoT Applications and Their Roles in Smart Cities. Smart Cities, 4(3), 1196–1219. https://doi.org/10.3390/smartcities4030064
Albahri, A. S., Alwan, J. K., Taha, Z. K., Ismail, S. F., Hamid, R. A., Zaidan, A. A., Albahri, O. S., Zaidan, B. B., Alamoodi, A. H., & Alsalem, M. A. (2021). IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art. Journal of Network and Computer Applications, 173, 102873. https://doi.org/10.1016/j.jnca.2020.102873
Amini, S., Saber, M., Rabiei-Dastjerdi, H., & Homayouni, S. (2022). Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series. Remote Sensing, 14(11), 2654. https://doi.org/10.3390/rs14112654
Arthurs, P., Gillam, L., Krause, P., Wang, N., Halder, K., & Mouzakitis, A. (2022). A Taxonomy and Survey of Edge Cloud Computing for Intelligent Transportation Systems and Connected Vehicles. IEEE Transactions on Intelligent Transportation Systems, 23(7), 6206–6221. https://doi.org/10.1109/TITS.2021.3084396
Bera, B., Chattaraj, D., & Das, A. K. (2020). Designing secure blockchain-based access control scheme in IoT-enabled Internet of Drones deployment. Computer Communications, 153, 229–249. https://doi.org/10.1016/j.comcom.2020.02.011
Bhamare, D., Zolanvari, M., Erbad, A., Jain, R., Khan, K., & Meskin, N. (2020). Cybersecurity for industrial control systems: A survey. Computers & Security, 89, 101677. https://doi.org/10.1016/j.cose.2019.101677
Bi, J., Zhang, X., Yuan, H., Zhang, J., & Zhou, M. (2022). A Hybrid Prediction Method for Realistic Network Traffic With Temporal Convolutional Network and LSTM. IEEE Transactions on Automation Science and Engineering, 19(3), 1869–1879. https://doi.org/10.1109/TASE.2021.3077537
Çalık, A. (2021). A novel Pythagorean fuzzy AHP and fuzzy TOPSIS methodology for green supplier selection in the Industry 4.0 era. Soft Computing, 25(3), 2253–2265. https://doi.org/10.1007/s00500-020-05294-9
Chen, C., Liu, B., Wan, S., Qiao, P., & Pei, Q. (2021). An Edge Traffic Flow Detection Scheme Based on Deep Learning in an Intelligent Transportation System. IEEE Transactions on Intelligent Transportation Systems, 22(3), 1840–1852. https://doi.org/10.1109/TITS.2020.3025687
Cui, Y., Kara, S., & Chan, K. C. (2020). Manufacturing big data ecosystem: A systematic literature review. Robotics and Computer-Integrated Manufacturing, 62, 101861. https://doi.org/10.1016/j.rcim.2019.101861
Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agriculture, 12(10), 1745. https://doi.org/10.3390/agriculture12101745
Ding, Y., Jin, M., Li, S., & Feng, D. (2021). Smart logistics based on the internet of things technology: an overview. International Journal of Logistics Research and Applications, 24(4), 323–345. https://doi.org/10.1080/13675567.2020.1757053
Egala, B. S., Pradhan, A. K., Badarla, V., & Mohanty, S. P. (2021). Fortified-Chain: A Blockchain-Based Framework for Security and Privacy-Assured Internet of Medical Things With Effective Access Control. IEEE Internet of Things Journal, 8(14), 11717–11731. https://doi.org/10.1109/JIOT.2021.3058946
Ferrag, M. A., Friha, O., Hamouda, D., Maglaras, L., & Janicke, H. (2022). Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning. IEEE Access, 10, 40281–40306. https://doi.org/10.1109/ACCESS.2022.3165809
Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A., & Hasanlou, M. (2020). Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 276–288. https://doi.org/10.1016/j.isprsjprs.2020.07.013
Gomes, V., Queiroz, G., & Ferreira, K. (2020). An Overview of Platforms for Big Earth Observation Data Management and Analysis. Remote Sensing, 12(8), 1253. https://doi.org/10.3390/rs12081253
Gorgulla, C., Boeszoermenyi, A., Wang, Z.-F., Fischer, P. D., Coote, P. W., Padmanabha Das, K. M., Malets, Y. S., Radchenko, D. S., Moroz, Y. S., Scott, D. A., Fackeldey, K., Hoffmann, M., Iavniuk, I., Wagner, G., & Arthanari, H. (2020). An open-source drug discovery platform enables ultra-large virtual screens. Nature, 580(7805), 663–668. https://doi.org/10.1038/s41586-020-2117-z
Heidari, A., Navimipour, N. J., & Unal, M. (2022). Applications of ML/DL in the management of smart cities and societies based on new trends in information technologies: A systematic literature review. Sustainable Cities and Society, 85, 104089. https://doi.org/10.1016/j.scs.2022.104089
Jalili, V., Afgan, E., Gu, Q., Clements, D., Blankenberg, D., Goecks, J., Taylor, J., & Nekrutenko, A. (2020). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update. Nucleic Acids Research, 48(W1), W395–W402. https://doi.org/10.1093/nar/gkaa434
Kalvari, I., Nawrocki, E. P., Ontiveros-Palacios, N., Argasinska, J., Lamkiewicz, K., Marz, M., Griffiths-Jones, S., Toffano-Nioche, C., Gautheret, D., Weinberg, Z., Rivas, E., Eddy, S. R., Finn, R. D., Bateman, A., & Petrov, A. I. (2021). Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Research, 49(D1), D192–D200. https://doi.org/10.1093/nar/gkaa1047
Karar, M. E., Alsunaydi, F., Albusaymi, S., & Alotaibi, S. (2021). A new mobile application of agricultural pests recognition using deep learning in cloud computing system. Alexandria Engineering Journal, 60(5), 4423–4432. https://doi.org/10.1016/j.aej.2021.03.009
Kasongo, S. M. (2023). A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework. Computer Communications, 199, 113–125. https://doi.org/10.1016/j.comcom.2022.12.010
Kong, L., Tan, J., Huang, J., Chen, G., Wang, S., Jin, X., Zeng, P., Khan, M., & Das, S. K. (2023). Edge-computing-driven Internet of Things: A Survey. ACM Computing Surveys, 55(8), 1–41. https://doi.org/10.1145/3555308
Li, Y., Dai, J., & Cui, L. (2020). The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model. International Journal of Production Economics, 229, 107777. https://doi.org/10.1016/j.ijpe.2020.107777
Mamta, Gupta, B. B., Li, K.-C., Leung, V. C. M., Psannis, K. E., & Yamaguchi, S. (2021). Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System. IEEE/CAA Journal of Automatica Sinica, 8(12), 1877–1890. https://doi.org/10.1109/JAS.2021.1004003
Mansour, R. F., Amraoui, A. El, Nouaouri, I., Diaz, V. G., Gupta, D., & Kumar, S. (2021). Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems. IEEE Access, 9, 45137–45146. https://doi.org/10.1109/ACCESS.2021.3066365
Or-Meir, O., Nissim, N., Elovici, Y., & Rokach, L. (2020). Dynamic Malware Analysis in the Modern Era—A State of the Art Survey. ACM Computing Surveys, 52(5), 1–48. https://doi.org/10.1145/3329786
Pereira, L. S., Paredes, P., & Jovanovic, N. (2020). Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach. Agricultural Water Management, 241, 106357. https://doi.org/10.1016/j.agwat.2020.106357
Phan, T. N., Kuch, V., & Lehnert, L. W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition. Remote Sensing, 12(15), 2411. https://doi.org/10.3390/rs12152411
Ren, L., Dong, J., Wang, X., Meng, Z., Zhao, L., & Deen, M. J. (2021). A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life. IEEE Transactions on Industrial Informatics, 17(5), 3478–3487. https://doi.org/10.1109/TII.2020.3008223
Rodrigues, T. K., Suto, K., Nishiyama, H., Liu, J., & Kato, N. (2020). Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective. IEEE Communications Surveys & Tutorials, 22(1), 38–67. https://doi.org/10.1109/COMST.2019.2943405
Sharma, A., Singh, P. K., & Kumar, Y. (2020). An integrated fire detection system using IoT and image processing technique for smart cities. Sustainable Cities and Society, 61, 102332. https://doi.org/10.1016/j.scs.2020.102332
Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers & Operations Research, 119, 104926. https://doi.org/10.1016/j.cor.2020.104926
Shoeibi, A., Khodatars, M., Ghassemi, N., Jafari, M., Moridian, P., Alizadehsani, R., Panahiazar, M., Khozeimeh, F., Zare, A., Hosseini-Nejad, H., Khosravi, A., Atiya, A. F., Aminshahidi, D., Hussain, S., Rouhani, M., Nahavandi, S., & Acharya, U. R. (2021). Epileptic Seizures Detection Using Deep Learning Techniques: A Review. International Journal of Environmental Research and Public Health, 18(11), 5780. https://doi.org/10.3390/ijerph18115780
Soni, G., Kumar, S., Mahto, R. V., Mangla, S. K., Mittal, M. L., & Lim, W. M. (2022). A decision-making framework for Industry 4.0 technology implementation: The case of FinTech and sustainable supply chain finance for SMEs. Technological Forecasting and Social Change, 180, 121686. https://doi.org/10.1016/j.techfore.2022.121686
Stergiou, C. L., Psannis, K. E., & Gupta, B. B. (2021). IoT-Based Big Data Secure Management in the Fog Over a 6G Wireless Network. IEEE Internet of Things Journal, 8(7), 5164–5171. https://doi.org/10.1109/JIOT.2020.3033131
Tawalbeh, L., Muheidat, F., Tawalbeh, M., & Quwaider, M. (2020). IoT Privacy and Security: Challenges and Solutions. Applied Sciences, 10(12), 4102. https://doi.org/10.3390/app10124102
Tian, Z., Luo, C., Qiu, J., Du, X., & Guizani, M. (2020). A Distributed Deep Learning System for Web Attack Detection on Edge Devices. IEEE Transactions on Industrial Informatics, 16(3), 1963–1971. https://doi.org/10.1109/TII.2019.2938778
Tong, Z., Chen, H., Deng, X., Li, K., & Li, K. (2020). A scheduling scheme in the cloud computing environment using deep Q-learning. Information Sciences, 512, 1170–1191. https://doi.org/10.1016/j.ins.2019.10.035
von Chamier, L., Laine, R. F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P. K., Karinou, E., Holden, S., Solak, A. C., Krull, A., Buchholz, T.-O., Jones, M. L., Royer, L. A., Leterrier, C., Shechtman, Y., Jug, F., … Henriques, R. (2021). Democratising deep learning for microscopy with ZeroCostDL4Mic. Nature Communications, 12(1), 2276. https://doi.org/10.1038/s41467-021-22518-0
Wang, C., Qin, J., Qu, C., Ran, X., Liu, C., & Chen, B. (2021). A smart municipal waste management system based on deep-learning and Internet of Things. Waste Management, 135, 20–29. https://doi.org/10.1016/j.wasman.2021.08.028
Wang, C., Zhang, S., Chen, Y., Qian, Z., Wu, J., & Xiao, M. (2020). Joint Configuration Adaptation and Bandwidth Allocation for Edge-based Real-time Video Analytics. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, 257–266. https://doi.org/10.1109/INFOCOM41043.2020.9155524
Wang, J., Wu, L., Choo, K.-K. R., & He, D. (2020). Blockchain-Based Anonymous Authentication With Key Management for Smart Grid Edge Computing Infrastructure. IEEE Transactions on Industrial Informatics, 16(3), 1984–1992. https://doi.org/10.1109/TII.2019.2936278
Wang, S., Guo, Y., Zhang, N., Yang, P., Zhou, A., & Shen, X. (2021). Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach. IEEE Transactions on Mobile Computing, 20(3), 939–951. https://doi.org/10.1109/TMC.2019.2957804
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020). Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. IEEE Communications Surveys & Tutorials, 22(2), 869–904. https://doi.org/10.1109/COMST.2020.2970550
Wazid, M., Das, A. K., Bhat K, V., & Vasilakos, A. V. (2020). LAM-CIoT: Lightweight authentication mechanism in cloud-based IoT environment. Journal of Network and Computer Applications, 150, 102496. https://doi.org/10.1016/j.jnca.2019.102496
Xia, S., Yao, Z., Li, Y., & Mao, S. (2021). Online Distributed Offloading and Computing Resource Management With Energy Harvesting for Heterogeneous MEC-Enabled IoT. IEEE Transactions on Wireless Communications, 20(10), 6743–6757. https://doi.org/10.1109/TWC.2021.3076201
Xie, H., & Qin, Z. (2021). A Lite Distributed Semantic Communication System for Internet of Things. IEEE Journal on Selected Areas in Communications, 39(1), 142–153. https://doi.org/10.1109/JSAC.2020.3036968
You, N., & Dong, J. (2020). Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 161, 109–123. https://doi.org/10.1016/j.isprsjprs.2020.01.001
Zhang, Z., Wang, Z., Shi, T., Bi, C., Rao, F., Cai, Y., Liu, Q., Wu, H., & Zhou, P. (2020). Memory materials and devices: From concept to application. InfoMat, 2(2), 261–290. https://doi.org/10.1002/inf2.12077
Zhao, Y., Zhao, J., Yang, M., Wang, T., Wang, N., Lyu, L., Niyato, D., & Lam, K.-Y. (2021). Local Differential Privacy-Based Federated Learning for Internet of Things. IEEE Internet of Things Journal, 8(11), 8836–8853. https://doi.org/10.1109/JIOT.2020.3037194
Zhou, Y., Shen, M., Cui, X., Shao, Y., Li, L., & Zhang, Y. (2021). Triboelectric nanogenerator based self-powered sensor for artificial intelligence. Nano Energy, 84, 105887. https://doi.org/10.1016/j.nanoen.2021.105887




