Abstract
To execute seamless computing along with information sharing, Cloud Computing (CC) plays an important role in measurable resource sharing. But, owing to its open and distributed framework, privacy is a major risk as it is open to attackers. To accomplish their malicious goals, cloud services are abused by intruders. In this paper, using Linear Sigmoid Singleton Deep Neural Network (LS2DNN)with Pearson correlation and Brownian motion induced K-Anonymity (PBKA)centered lightweight privacy preservation technique in the cloud server, an efficient federated learning-based Intrusion Detection System (IDS) is proposed. From the publically available sources, NSL-KDD datasets are downloaded; also, Feature Extraction (FE), null feature removal, and feature mapping are executed. Subsequently, for feature selection, Chebyshev Chaotic Mapping adapted Jaya Optimization Algorithm (C2MJOA) is wielded. Then, for classifying attacked and normal data, the LS2DNNclassifier is utilized.The incoming data from the data owners are privacy preserved via federated learning-centered Pearson correlation and Brownian motion-induced K-Anonymity (PBKA) while testing; moreover, the data is verified for intrusion. Finally, when compared with the prevailing models, the proposed model attained better results.
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Gupta, R., Alam, T. An efficient federated learning based intrusion detection system using LS2DNN with PBKA based lightweight privacy preservation in cloud server. Multimed Tools Appl 83, 44685–44697 (2024). https://doi.org/10.1007/s11042-023-17401-7
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DOI: https://doi.org/10.1007/s11042-023-17401-7