Many of software vulnerability detection methods suffer from problems of dependent on expert experience, rough detection granularity, and incomplete syntax and semantics information on source codes. This paper proposes the VDDA, Vulnerability Detection based on Deep learning and Attention mechanism, an effective software vulnerability detection model based on deep learning and the attention mechanism. In the VDDA, deep learning technology is used to construct the underlying classifier to avoid the feature engineering of traditional machine learning techniques. The Joren slice tool combined with the code attribute graph (CPG) optimization is used to simplify the source code before it is fed to the Bidirectional Long Short-Term Memory (BLSTM) deep model. Meanwhile, the attention mechanism is employed to improve the efficiency and accuracy of vulnerability detection. Experiment results have demonstrated that the proposed VDDA model is more effective than the existing vulnerability detection methods.

As a classical clustering algorithm, the K-means is widely applied and studied in the information retrieval. However, the performance of this algorithm is sensitive to the selection of initial clustering clusters. Improper selection of these centers will trap the algorithm into the problem of local optimum. Meanwhile, this algorithm cannot process the non-convex datasets effectively. In order to ease these problems, this paper proposes the DHKmeans, an improved K-means based on the dynamic initial clustering center determination and the hierarchical clustering. In the DH-Kmeans, a comprehensive measure which is based on the density parameter and the cluster distance is firstly designed to dynamically determine the initial clustering centers. By this, the problem of local optimum is avoided. Then, the Agglomerative Hierarchical Clustering (AHC) is integrated into the iteration procedures of the DH-Kmeans. In the iteration procedures, the similar small clusters are merged gradually to endow the DH-Kmeans with the ability of precisely processing more types of datasets. Experimental results on testing 25 artificial datasets and six real datasets have demonstrated that the DH-Kmeans is fast, stable, and accurate in data clustering.

To effectively boost computer usage, machine learning models are used in several phishing detection systems to classify enormous phishing datasets. Based on phishing patterns, researchers prefer to extract a considerable number of features to improve phishing detection performance. However, redundant and useless features in the feature set degrade the performance of the underlying classification models. In addition, several existing phishing detection models mainly focus on detection accuracy and overlook recall rates. However, in phishing detection, it is more harmful to falsely detect a phishing website as a legitimate website than it is to detect a legitimate website as a phishing website. This study proposes a novel phishing detection model, multi-objective evolution/random forest (MOE/RF), which is based on the revised multi-objective evolution optimization algorithm (MOE) and random forest (RF). The MOE/RF model uses accuracy as the detection target and minimizes the probability of false detection of phishing sites. In addition, two new strategies, the symmetric uncertainty-based population initialization and the population state-based adaptive environmental selection, are proposed to improve the performance of the MOE. Experimental results on testing five different phishing datasets demonstrated that the MOE/RF performs superior to several existing methods.

Cloud computing can provide a more cost-effective way to deploy scientific workflows than traditional distributed computing environments such as cluster and grid. Due to the large size of scientific datasets, data placement plays an important role in scientific cloud workflow systems for improving system performance and reducing data transfer cost. Traditional task-level data placement strategy only considers shared datasets within individual workflows to reduce data transfer cost. However, it is obvious that task-level strategy is not necessarily good enough for the situation of multiple workflows at the workflow level. In this paper, a novel workflow-level data placement model is constructed, which regards multiple workflows as a whole. Then, a two-stage data placement strategy is proposed which first pre-allocates initial datasets to proper datacenters during workflow build-time stage, and then dynamically distributes newly generated datasets to appropriate datacenters during runtime stage. Both stages use an efficient discrete particle swarm optimization algorithm to place flexible-location datasets. Comprehensive experiments demonstrate that our workflow-level data placement strategy can be more cost-effective than its task-level counterpart for data-sharing scientific cloud workflows.

As an important tool, clustering validity index (CVI) is usually used to evaluate the validity of the clustering results and determine the optimal clustering number (Kopt). However, the existing CVIs have some shortcomings, such as too complex calculation, low computational efficiency and narrow range of applications. Aiming at these problems, an improved K-means algorithm that uses density parameters for initial centers selection is firstly proposed to make the results of traditional clustering algorithms more stable. Then, a new variance based clustering validity index (VCVI) from the perspective of spatial distribution of data sets is introduced to extend the application of the partitional clustering algorithms and better evaluate the effect of clustering results. Finally, a new algorithm integrated with the improved K-means algorithm and the new proposed VCVI is designed to effectively determine the Koptand the optimal clustering partition. The experimental results have shown that the new proposed algorithm with VCVI is effective in forming the Kopt and the optimal clustering partition for the tested data sets.

Nowadays, the demand for wireless spectrum resources is growing rapidly with the continuous development of wireless communication technology. However, the vast majority of available spectrums have been allocated, making the new demanders have difficulty to acquire their appropriate spectrums. In order to reallocate the scanty spectrum resources for new demanders, many spectrum auction schemes that are based on truthfulness are proposed. However, the insufficient protective measures been deployed in these schemes made the users private information be easily exposed during the process of the spectrum auction. By combining homomorphic encryption and garbled circuits technology, this paper proposes a secure and efficient collaborative spectrum auction scheme for redistributing the scarcity spectrum resources. Furthermore, the security of the protocol used in the scheme has been proved. The emulation experimental results have demonstrated that the proposed scheme can effective enhance the privacy information of the spectrum users while consume relatively lower computing and communicating overhead.