This paper presents a fast SAR imagery algorithm for Ground Moving Target Imaging (GMTIm) based on the slope detection strategy combined with Time-Frequency Representation (TFR), which is known as Lv's Distribution (LVD). This fast imagery algorithm focuses on the solution of the ambiguity problems and relevant heavy computing load in SAR imagery. Firstly, according to the relationship between the slope of the range walk trajectory and the cross-track velocity of moving target, a new high-efficiency slope detection strategy based on gradient and level-line angle is presented in the image domain. Then, the Doppler centroid shift induced by cross-track velocity can also be obtained. Secondly, owing to the cross-track velocity estimated before, the Range Walk Migration Correction (RWMC) can be performed to concentrate the echo response of the moving target into a single range cell. Finally, due to the superior performance in representing multi-component Linear Frequency Modulation (LFM) signal, LVD is adopted here to represent the Doppler chirp rate of multiple moving targets in a Doppler Centroid Frequency and Chirp Rate domain (CFCR). The performance of the proposed algorithm is evaluated in terms of superiority and effectiveness using simulations, and the comparison between the proposed algorithm and the other conventional algorithms is also presented.

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Growing with the technology, there are many new attack techniques presented in the cloud environment. Different from the general server, once the cloud environment suffered from malicious attacks, people or companies will get caught in extreme dangers. Therefore, it is important for network security in cloud. Since there are a lot of packets in network traffic including malicious packets, huge amounts of alerts will be generated by the intrusion detection system. Analyzing these alert data is time-consuming and it is difficult to obtain the attack steps and strategies immediately by directly performing these analyses.We proposed an adaptive feature-weighted alert correlation system that employs a Bayesian Network to choose the features with high relevance and then adjusts the feature weights according to the statistics of Bayesian Network in a period of time. We estimate the correlation probability of two alerts with the relevant features by using the Feature Wight Matrix, and the correlation probability is recorded in Alert Correlation Matrix. Using the information in Alert Correlation Matrix, we can extract high level attack strategies and construct attack graphs. In our system, facing a great deal of network traffic, the administrator can accurately recognize intruders' intentions and learn about the attack probabilities and network security situations.

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Boundary localization is one of the key issues in iris recognition system. For non-ideal iris images, some frequentlyoccurred cases such as dominant texture patterns, eyelashes or eyelids occlusions, low contrast between iris and sclera, and pupil deviation, will lead to inaccurate boundary localization. Specifically, if the intensive transition from iris to sclera is too smooth, outer boundary localization will be very difficult. To stress the problem, in this paper we propose the boundary localization method in which nonlinear gray-level transformation is innovated in outer boundary localization process. The experimental results depict that our algorithm have improved the localization accuracy for non-ideal iris compared to the classical algorithms