Security issues have resulted in severe damage to the cloud computing environment, adversely affecting the healthy and sustainable development of cloud computing. Intrusion detection is one of the technologies for pro...
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Security issues have resulted in severe damage to the cloud computing environment, adversely affecting the healthy and sustainable development of cloud computing. Intrusion detection is one of the technologies for protecting the cloud computing environment from malicious attacks. However, network traffic in the cloud computing environment is characterized by large scale, high dimensionality, and high redundancy, these characteristics pose serious challenges to the development of cloud intrusion detection systems. Deep learning technology has shown considerable potential for intrusion detection. Therefore, this study aims to use deep learning to extract essential feature representations automatically and realize high detection performance efficiently. An effective stacked contractiveautoencoder (SCAE) method is presented for unsupervised feature extraction. By using the SCAE method, better and robust low-dimensional features can be automatically learned from raw network traffic. A novel cloud intrusion detection system is designed on the basis of the SCAE and support vector machine (SVM) classification algorithm. The SCAE+SVM approach combines both deep and shallow learning techniques, and it fully exploits their advantages to significantly reduce the analytical overhead. Experiments show that the proposed SCAE+SVM method achieves higher detection performance compared to three other state-of-the-art methods on two well-known intrusion detection evaluation datasets, namely KDD Cup 99 and NSL-KDD.
contractive auto-encoder (CAE) is a type of auto-encoders and a deep learning algorithm that is based on multilayer training approach. It is considered as one of the most powerful, efficient and robust classification ...
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contractive auto-encoder (CAE) is a type of auto-encoders and a deep learning algorithm that is based on multilayer training approach. It is considered as one of the most powerful, efficient and robust classification techniques, more specifically feature reduction. The problem independence, easy implementation and intelligence of solving sophisticated problems make it distinct from other deep learning approaches. However, CAE fails in data dimensionality reduction that cause difficulty to capture the useful information within the features space. In order to resolve the issues of CAE, restricted Boltzmann machine (RBM) layers have been integrated with CAE to enhance the dimensionality reduction and a randomized factor for hidden layer parameters. The proposed model has been evaluated on four benchmark variant datasets of MNIST. The results have been compared with four well-known multiclass class classification approaches including standard CAE, RBM, AlexNet and artificial neural network. A considerable amount of improvement has been observed in the performance of proposed model as compared to other classification techniques. The proposed CAE-RBM showed an improvement of 2-4% on MNIST(basic), 9-12% for MNIST(rot), 7-12% for MNIST(bg-rand) and 7-10% for MNIST(bg-img) dataset in term of final accuracy.
In the field of mechanical fault diagnosis, methods based on traditional machine learning algorithms are commonly used. However, these methods require a lot of manual design of fault features, which needs domain knowl...
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ISBN:
(纸本)9781538684580
In the field of mechanical fault diagnosis, methods based on traditional machine learning algorithms are commonly used. However, these methods require a lot of manual design of fault features, which needs domain knowledge that makes these methods difficult to generalize. This paper presents a deep contractiveauto-encoding network (DCAEN) for machinery fault diagnosis, which is constructed by contractive auto-encoder (CAE), i.e., an unsupervised learning way for feature extraction. In addition, we add a sparsity constraint to the loss function of CAE for removing the redundant information. With the help of deep learning, this model can automatically extract features from raw data without artificially constructing features. In order to validate the superiority of the model, we conduct experiments and compare the model to existing methods with a rolling bearing dataset.
The existing auto-encoder algorithm has been used to do deep learning. A variety of improved auto-encoder algorithms still have their disadvantages. In order to improve the learning accuracy of the auto-encoder algori...
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ISBN:
(纸本)9781509061617
The existing auto-encoder algorithm has been used to do deep learning. A variety of improved auto-encoder algorithms still have their disadvantages. In order to improve the learning accuracy of the auto-encoder algorithm, a hybrid learning model with a classifier is proposed. This model constructs a new depth auto-encoder model (SDCAE) by mixing a denoising auto-encoder (DAE) and a contractive auto-encoder (CAE). The weights are initialized by the construction method of the stacking auto-encoders, which is optimized by the gradient descent method. Therefore, the model has robustness to the reconstruction input of DAE and to the hidden layer representation of the CAE at the same time in the pre-training process. The learning model uses Softmax regression as a classification layer. The experimental results show that the classifier based on SDCAE has higher classification accuracy compared to existing auto-encoder on the given data sets.
Multi-layer extreme learning machine (ML-ELM) is a stacked extreme learning machine based auto-encoding (ELM-AE). It provides an effective solution for deep feature extraction with higher training efficiency. To enhan...
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ISBN:
(数字)9783319466811
ISBN:
(纸本)9783319466811;9783319466804
Multi-layer extreme learning machine (ML-ELM) is a stacked extreme learning machine based auto-encoding (ELM-AE). It provides an effective solution for deep feature extraction with higher training efficiency. To enhance the local-input invariance of feature extraction, we propose a contractive multi-layer extreme learning machine (C-MLELM) by adding a penalty term in the optimization function to minimize derivative of output to input at each hidden layer. In this way, the extracted feature is supposed to keep consecutiveness attribution of an image. The experiments have been done on MNIST handwriting dataset and face expression dataset CAFEE. The results show that it outperforms several state-of-art classification algorithms with less error and higher training efficiency.
De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to the input by corrupting the original data first and then reconstructing the input by minimizing the error function. And contractiveauto-enc...
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ISBN:
(纸本)9783319093338;9783319093321
De-noising auto-encoder (DAE) is an improved auto-encoder which is robust to the input by corrupting the original data first and then reconstructing the input by minimizing the error function. And contractive auto-encoder (CAE) is another kind of improved auto-encoder learning robust feature by introducing Frobenius norm of the Jacobean matrix of the learned feature with respect to the input. In this paper, we combine DAE and CAE, and propose contractive de-noising auto-encoder (CDAE), which is robust to both the original input and the learned feature. We stack CDAE to extract more abstract features and apply SVM for classification. The experiment on benchmark dataset MNIST shows that CDAE performed better than CAE and DAE
Most of smile recognition methods are based on constrained databases. Thus there are a lot of limitations when applying those algorithms into the real-world smile recognition. For the purpose of improving the accuracy...
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ISBN:
(纸本)9781467376792
Most of smile recognition methods are based on constrained databases. Thus there are a lot of limitations when applying those algorithms into the real-world smile recognition. For the purpose of improving the accuracy in real-world smile recognition, we conducted our experiments on two databases (GENKI-4K database and our own built database). Depending on deep learning theory, we constructed a new deep model by stacking contractive auto-encoder (CAE) on contractive Denoising auto-encoder (CDAE) to extract useful features. Firstly, we pre-trained a CDAE to extract the feature of the first layer, then the extracted feature were used as input of the next basic model CAE, by pre-training the CAE model, we got more abstract feature, then the feature were used to classification. Experiments showed that our approach was useful for smile recognition. On the other hand, we also explored the influence of different number of training samples.
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