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IEEE Final Year Project Topic for CSE

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A Secure Higher-Order Lanczos-Based Orthogonal Tensor SVD for Big Data Reduction

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IEEE Project Abstract

Singular value decomposition (SVD) has been applied in cyber security since it can reduce data. However, SVD is hard to reduce higher-order big data because it is designed for only matrix data initially. Reducing higher-order big data is desired for cyber security applications, and is a very challenging issue. In this paper, we propose a novel orthogonal tensor SVD method using big data techniques for higher-order big data reduction, which can be extensively used in big data applications of cyber security. More specifically, we firstly present a higher-order Lanczos-based orthogonal tensor SVD algorithm to reduce higher-order data. Then, for utilizing the incomparable benefits of cloud, we develop a secure orthogonal tensor SVD method to outsource the computation task of the orthogonal tensor SVD algorithm to cloud. The secure orthogonal tensor SVD method can protect data security from untrusted cloud by applying garbled circuits to the orthogonal tensor SVD algorithm. Finally, we analyze the security and efficiency of our proposed orthogonal tensor SVD on synthetic dataset and real network intrusion detection dataset, and the results demonstrate that our proposed method is promising for big data reduction.Singular value decomposition (SVD) has been applied in cyber security since it can reduce data. However, SVD is hard to reduce higher-order big data because it is designed for only matrix data initially. Reducing higher-order big data is desired for cyber security applications, and is a very challenging issue. In this paper, we propose a novel orthogonal tensor SVD method using big data techniques for higher-order big data reduction, which can be extensively used in big data applications of cyber security. More specifically, we firstly present a higher-order Lanczos-based orthogonal tensor SVD algorithm to reduce higher-order data. Then, for utilizing the incomparable benefits of cloud, we develop a secure orthogonal tensor SVD method to outsource the computation task of the orthogonal tensor SVD algorithm to cloud. The secure orthogonal tensor SVD method can protect data security from untrusted cloud by applying garbled circuits to the orthogonal tensor SVD algorithm. Finally, we analyze the security and efficiency of our proposed orthogonal tensor SVD on synthetic dataset and real network intrusion detection dataset, and the results demonstrate that our proposed method is promising for big data reduction.

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