Accurately predicting future service traffic would be of great help for load balancing and resource allocation, which plays a key role in guaranteeing the quality of service (QoS) in cloud computing. With the rapid development of data center, the large-scale network traffic prediction requires more suitable methods to deal with the complex properties (e.g., high-dimension, long-range dependence, non-linearity, and so on). However, due to the limitations of traditional methods (e.g., strong theoretical assumptions and simple implementation), few research works could predict the large-scale network traffic efficiently and accurately. More importantly, most of the studies took only the temporal features but without the services' communications into consideration, which may weaken the QoS of applications in the data center. To this end, we applied the gated recurrent unit (GRU) model and the interactive temporal recurrent convolution network (ITRCN) to single-service traffic prediction and interactive network traffic prediction, respectively. Especially, ITRCN takes the communications between services as a whole and directly predicts the interactive traffic in large-scale network. Within the ITRCN model, the convolution neural network (CNN) part learns network traffic as images to capture the network-wide services' correlations, and the GRU part learns the temporal features to help the interactive network traffic prediction. We conducted comprehensive experiments based on the Yahoo! data sets, and the results show that the proposed novel method outperforms the conventional GRU and CNN method by an improvement of 14.3% and 13.0% in root mean square error, respectively.

In today’s dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. In this article, we give a quick overview of the cybersecurity knowledge graph’s core concepts, schema, and building methodologies. We also give a relevant dataset review and open-source frameworks on the information extraction and knowledge creation job to aid future studies on cybersecurity knowledge graphs. We perform a comparative assessment of the many works that expound on the recent advances in the application scenarios of cybersecurity knowledge graph in the majority of this paper. In addition, a new comprehensive classification system is developed to define the linked works from 9 core categories and 18 subcategories. Finally, based on the analyses of existing research issues, we have a detailed overview of various possible research directions.

Training gene expression data with supervised learning approaches can provide an alarm sign for early treatment of lung cancer to decrease death rates. However, the samples of gene features involve lots of noises in a realistic environment. In this study, we present a random forest with self-paced learning bootstrap for improvement of lung cancer classification and prognosis based on gene expression data. To be specific, we propose an ensemble learning with random forest approach to improving the model classification performance by selecting multi-classifiers. Then, we investigate the sampling strategy by gradually embedding from high- to low-quality samples by self-paced learning. The experimental results based on five public lung cancer datasets show that our proposed method could select significant genes exactly, which improves classification performance compared to that of existing approaches. We believe that our proposed method has the potential to assist doctors in gene selections and lung cancer prognosis.

Spatial data clustering has played an important role in the knowledge discovery in spatial databases. However, due to the increasing volume and diversity of data, conventional spatial clustering methods are inefficient even on moderately large datasets, and usually fail to discover clusters with diverse shapes and densities. To address these challenges, we propose a two-phase clustering method named KMDD (clustering by combining K-means with Density and Distance-based method) to fast find clusters with diverse shapes and densities in spatial databases. In the first phase, KMDD uses a partition-based algorithm (K-means) to cluster the dataset into several relatively small spherical or ball-shaped subclusters. After that, each subcluster is given a local density; to merge subclusters, KMDD utilizes the idea that genuine cluster cores are characterized by a higher density than their neighbor subclusters and by a relatively large distance from subclusters with higher densities. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithm has a near-linear time complexity with respect to the dataset size and dimension, and has the capability to find clusters with diverse shapes and densities.