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Most smartphones run on Android OS, which facilitates the installation of third-party applications. Unfortunately, malware also exists for the Android. Malware can perform various harmful activities. In this paper, we have collected the behaviors of 100 Android applications. These collected applications consist of 50 benign applications and 50 pieces of malware. The invoked system calls were logged to serve as the behaviors of these applications. Then, the data were input to the dendritic cell algorithm (DCA). The DCA was inspired by a danger model of the human immune system and is able to detect anomalies. We used the features of the DCA to perform anomaly detection and classified the collected applications as either benign or malicious. Our experiment results showed that the DCA could achieve a higher accuracy than either the decision tree, the naive Bayes, or the support vector machine.

A hybrid algorithm for calculating the flow around a flat-nosed projectile moving through a fluid is established. At cells of fixed volume away from the projectile, Harten's total variation diminishing (TVD), second-order accurate, shock capturing scheme is utilized. Due to projectile motion, cells adjacent to the projectile can be treated as compressing or expanding in volume. For such cells, a local finite volume approach has been employed to derive a cell update algorithm. Proof of concept for the expanding cell scheme is established through calculating the one-dimensional flow behind a moving piston, whose theoretical solution is wellknown. The resulting hybrid scheme is applied to the problem of blast wave simulation in axisymmetric geometries. Flow around a vertical muzzle brake is calculated for the case of an embedded moving projectile.

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A clustering algorithm based on particle swarm optimization (PSO) and fuzzy theorem was introduced for data analysis. Clustering algorithms require users to set some parameters, such as the number of clusters k. However, it is unreasonable to expect users to specify a meaningful value of k if they lack prior knowledge of the data. This paper proposed an algorithm to determine the appropriate number of clusters and produced an associated set of cluster centers automatically. The proposed algorithm was compared with stand-alone PSO clustering and fuzzy c-means on three data sets. The results of the experiment showed that the proposed method was able to determine the number of clusters accurately, and to deliver favorable performance in the clustering of data.