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Domain generation algorithms (DGA) provide methods that use specific parameters as random seeds to generate a large number of random domain names for preventing malicious domain name detection, which greatly increases the difficulty of detecting and defending botnets and malware. State-of-the-art models for detecting algorithmically generated domain names are generally based on the principle of analyzing the statistical characteristics of the domain name and building a classifier to locate the algorithmically generated ones. However, most current models have problems of requiring the manual construction of feature sets for classification, as they are sensitive to the imbalance of the sample distribution in the domain name dataset and are difficult to adapt to frequent changes of the domain-name algorithm. To address this issue, we propose a hybrid model that combines a convolutional neural network (CNN) and a bidirectional long-term memory network (BLSTM). First, to solve the problem of the number of domain names generated by DGAs being relatively small and the sample distribution being unbalanced, which consequently decreases detection accuracy, the borderline synthetic minority over-sampling technique is employed to optimize the sample balance of the domain name dataset. Second, a hybrid deep neural network that combines CNN and BLSTM is introduced to extract the semantic and context-dependency features from the domain names. The experimental results from different domain-name datasets demonstrate that the proposed model achieves significant improvement over state-of-the-art models with regard to precision and robustness.

It remains a challenge to classify different categories of fruits because of the similarities of shape, color, and texture among them. We presented a novel approach in order to classify fruits accurately and efficiently based on computer vision techniques. We obtained the coefficients using fractional Fourier transform. The entropies extracted from the coefficients were fed into the classifier as the features. A multilayer perceptron optimized by an improved hybrid genetic algorithm was used as the classifier. The experiment results on 1653 fruit images demonstrated that the proposed method achieved an overall accuracy of 89.59%, which was superior to the state-of-the art approaches. Our method is effective in identifying fruit cagegories.

Keyword: rigid image registration, mutual information, bacteria chemotaxis, bacteria multiple colony chemotaxis Abstract A novel approach for image registration based on mutual information is presented in this paper. Firstly, normalized mutual information is introduced as the fitness function to supersede the traditional mutual information. Meanwhile, the extrema in fitness function resulted from interpolation is analyzed and the solution is presented as adding a weighted parameter into the variables set. Secondly, search model is improved. The 2 nd generation wavelet is utilized to decompose the original image into multi-resolution sub-images to search optimal solution in different precision layer, which harmonizes the contradiction between search precision and convergence rate. Finally, we introduce a novel optimization means—bacterial multiple colony chemotaxis. Experiments demonstrate the legitimacy and efficiency of our betterment.