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Intrusion detection is still a crucial issue for network security. For visualization and classification on intrusion detection, the high dimensionality should be confronted. As a nonlinear learning method, Isomap is an effective dimension reduction tool among manifold learning algorithms. However, Euclidean distance is used in Isomap which is more suitable for continuous features. Another limitation...
Manifold learning is an emerging and promising approach in nonlinear dimension reduction. Representative methods include locally linear embedding (LLE) and Isomap. However, both methods fail to guarantee connectedness of the constructed neighborhood graphs. We propose k variable method called kv-LLE and kv-Isomap to build connected neighborhood graphs so as to enhance the robustness. The applicability...
Intrusion detection is still a crucial issue for network security. Support vector machine (SVM) has been successfully applied in intrusion detection systems. However, for further improvement in performance, data dimension reduction should have drawn special attention. This paper proposes a scheme using popular non-linear dimension reduction tool Isomap and one-class support vector machine to detect...
A reversible watermarking algorithm with high data-hiding capacity has been developed for electrocardiogram (ECG) signal based on wavelet transforms. In electrocardiogram signal, the energy is concentrated in QRS complex waves. So the selection of wavelet coefficients for hiding should avoid making QRS complex waves distort obviously. The algorithm hides bits in the expansion of selected coefficients...
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