Hui Guo

TL;DR: A novel algorithm named PRS that combines proactive with reactive scheduling methods is proposed to schedule real-time tasks and three system scaling strategies according to dynamic workloads are developed to improve the resource utilization and reduce energy consumption.

TL;DR: A real-time workflow fault-tolerant model that extends the traditional PB model by incorporating the cloud characteristics is established and a dynamic fault-Tolerant scheduling algorithm, FASTER, is proposed for realtime workflows in the virtualized cloud.

Abstract: Convolution Neural Network (ConvNet) offers a high potential to generalize input data. It has been widely used in many application areas, such as visual imagery, where comprehensive learning datasets are available and a ConvNet model can be well trained and perform the required function effectively. ConvNet can also be applied to network intrusion detection. However, the currently available datasets related to the network intrusion are often inadequate, which makes the ConvNet learning deficient, hence the trained model is not competent in detecting unknown intrusions. In this paper, we propose a ConvNet model using transfer learning for the network intrusion detection. The model consists of two concatenated ConvNets and is built on a two-stage learning process: learning a base dataset and transferring the learned knowledge to the learning of the target dataset. Our experiments on the NSLKDD dataset show that the proposed model can improve the detection accuracy not only on the test dataset containing mostly known attacks (KDDTest $+)$ but also on the test dataset featuring many novel attacks (KDDTest-21) – about 2.68% improvement on KDDTest+ and 22.02% on KDDTest-21 can be achieved, as compared to the traditional ConvNet model.

TL;DR: LuNet as discussed by the authors proposes a hierarchical CNN+RNN neural network for network intrusion detection, where the convolutional neural network (CNN) and the RNN learn input traffic data in sync with a gradually increasing granularity such that both spatial and temporal features can be effectively extracted.

TL;DR: Experimental results conducted on a real VMs cluster show that the proposed SP-Partitioner algorithms can not only achieve higher balancing performance on data with varying degree of data skew, but also decrease the average processing time of one batch of these data.