Spiking Neural Networks (SNNs) are known as a branch of neuromorphic computing and are currently used in neuroscience applications to understand and model the biological brain.SNNs could also potentially be used in many other application domains such as classification, pattern recognition, and autonomous control.This work presents a highly-scalable hardware platform called POETS, and uses it to implement SNN on a very large number of parallel and reconfigurable FPGA-based processors.The current system consists of 48 FPGAs, providing 3072 processing cores and 49152 threads.We use this hardware to implement up to four million neurons with one thousand synapses.Comparison to other similar platforms shows that the current POETS system is twenty times faster than the Brian simulator, and at least two times faster than SpiNNaker.
—In recent years, the rapid development of internet technology brings many severe network security problems linked to malicious intrusions. Intrusion Detection System is considered to be one of the significant techniques to safeguard the network from both external and internal attacks. However, with the fast expansion of the IoT network, cyberattacks are also changing quickly, and many unknown types are showing up in the contemporary network environment. Consequently, the efficiency of traditional signature-based and anomaly-based Intrusion Detection System is insufficient. We propose a novel Intrusion Detection System, which uses an evolutionary technique based feature selection approach and a Random Forest-based classifier. The evolution-based feature selector uses an innovative Fitness Function to select the important features and reduces dimensions of the data, which raise the Ture Positive Rate and reduce the False Positive Rate at the same time. With exceptional high accuracy in multi-classification tasks and outstanding capabilities of handling noise in massive data scenarios, the Random Forest technique is widely used in anomaly detection. This research proposes a framework that can select more steady features and improve the classification results as compared with other technologies. The proposed framework is tested and experimented on UNSW-NB15 datasets and NSL-KDD datasets. Various statistical results and detailed comparison to other methods are presented within this article.
Abstract —An intelligent machine and manufacturing system has a significant role in the near future, especially when the circumstance of manufacturing industries are seriously competitive. New technologies are continuously being developed to serve future manufacturing. CNC turning machine is widely utilized in various advanced manufacturing industries. Straightness is a critical parameter in CNC turning process, which affects the workpiece assembly directly. However, control of straightness of the workpieces during in-process turning is difficult to be measured. Moreover, CNC turning machine cannot be adjusted real-time without stopping the operation. Hence, the aim of this research is to develop the straightness prediction model in the CNC turning process under various cutting conditions for carbon steel and aluminum workpieces in order to improve in-process monitoring and control of straightness. The cutting forces ratio has been adopted to estimate straightness. The Daubechies wavelet transform is utilized to decompose the dynamic cutting forces to remove the noise signals for better prediction. The straightness is calculated by employing the two-layer feed forward neural network, which is trained with the Levenberg-Marquardt back-propagation algorithm. As a result, the in-process straightness could be predicted well with greater accuracy and reliability using the proposed straightness
—Generating network traffic flows remains a critical aspect of developing cyber and network security systems. In this survey, we first consider the history of network traffic generation methods and identify the weaknesses of these. We then proceed to introduce more recent approaches based on machine learning (ML) models. In particular, we focus on Generative Adversarial Network (GAN) models, which have developed from their initial form to encompass many variants in today’s ML landscape. The use of GANs for generating traffic flows that have appeared in the literature are then presented. For each instance, we present the architecture, training methods, generated results, identified limitations and prospects for further research. We thus demonstrate that GANs are key to future developments in network traffic generation and secure cyber and network systems.
Abstract —This paper presents a novel framework for image classification which comprises a convolutional neural network (CNN) feature map extractor combined with a Gaussian process (GP) classifier. Learning within the CNN-GP involves forward propagating the predicted class labels, then followed by backpropagation of the maximum likelihood function of the GP with a regularization term added. The regularization term takes the form of one of the three loss functions: the Kullback-Leibler divergence, Wasserstein distance, and maximum correntropy. The training and testing are performed in mini batches of images. The forward step (before the regularization) involves replacing the original images in the mini batch with their close neighboring images and then providing these to the CNN-GP to get the new predictive labels. The network performance is evaluated on MNIST, Fashion-MNIST, CIFAR10, and CIFAR100 datasets. Precision-recall and receiver operating characteristics curves are used to evaluate the performance of the GP classifier. The proposed CNN-GP performance is validated with different levels of noise, motion blur, and adversarial attacks. Results are explained using uncertainty analysis and further tests on quantifying the impact on uncertainty with attack strength are carried out. The results show that the testing accuracy improves for networks that backpropagate the maximum likelihood with regularized losses when compared with methods that do not. Moreover, a comparison with a state-of-art CNN Monte Carlo dropout method is presented. The outperformance of the CNN-GP framework with respect to reliability and computational efficiency is
