Deep learning, which is originated from an artificial neural network (ANN), is one of the major technologies of today’s smart cybersecurity systems or policies to function in an intelligent manner. ...Popular
deep learning
techniques, such as multi-layer perceptron, convolutional neural network, recurrent neural network or long short-term memory, self-organizing map, auto-encoder, restricted Boltzmann machine, deep belief networks, generative adversarial network, deep transfer learning, as well as deep reinforcement learning, or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a
comprehensive overview
from the perspective of these neural networks and deep learning techniques according to today’s diverse needs. We also discuss the
applicability
of these techniques in various
cybersecurity tasks
such as intrusion detection, identification of malware or botnets, phishing, predicting cyberattacks, e.g. denial of service, fraud detection or cyberanomalies, etc. Finally, we highlight several
research issues and future directions
within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.
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Real-time network resource allocation based on virtualization technology is an important method to solve the solidification problem of Fiber-wireless (FiWi) access networks. To increase the resource ...utilization and meet the specific QoS requirements of smart gird communication services, a Load-balancing and QoS based dynamic resource allocation method (LbQ-DR) is proposed with three sub-mechanisms. A time-window based substrate network resource update mechanism is designed to describe the realtime resource consumption of substrate networks, which can balance the accuracy of resource status and the complexity of the allocation algorithm. A QoS-based Virtual network request (VNR) sorting mechanism is presented to precisely calculate the priority of VNRs and reasonably sort the incoming services. A load-balancing based resource allocation mechanism is designed to avoid unbalanced resource consumption. Especially, the channel interference is considered in the cost of embedding and a collision domain mechanism is introduced to decrease interference. Simulation results demonstrate that the proposed method can provide heterogeneous smart grid businesses with differentiated service, improve the utilization and economic benefits of the network and make the network more balanced.
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Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations ...among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are often complex and diverse. On the other hand, the representation learning has achieved great successes in many areas. Thereby, a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space, such that the graph properties can be preserved. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. In this survey, despite numerous types of graph neural networks, we conduct a comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models. First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. Then, we categorize different graph convolutional networks according to the areas of their applications. Finally, we present several open challenges in this area and discuss potential directions for future research.
•Proposes all terminal network reliability considering degradation behavior.•Considers probability of failure of both nodes and links.•The Bayesian method is used to estimate reliability of links and ...nodes as functions of time.•The integration of Monte Carlo and Deep Neural Networks for faster reliability estimation.
Most research on network reliability has considered links to have a binary state, i.e., functioning or failed, whereas nodes are considered flawless. In a more realistic scenario, both links and nodes might fail or may exhibit degradation behavior before failing. This study develops a framework to estimate the all-terminal reliability of a network that considers the degradation and probability of failure of all nodes and links in a network. Unlike previous works on network reliability that considered constant reliability for links, this paper considers the reliability of links, nodes, and the network as functions of time. In the proposed framework, the Bayesian methods (BM) are employed to estimate the reliability of links and nodes as functions of time considering degradation data. Due to the complexity of the all-terminal reliability problem, and to get fast estimations of the reliability of a network, an integration of Monte Carlo (MC) and Deep Neural Networks (DNNs) is proposed. The proposed MC algorithm can estimate the network reliability for given nodes and links reliability values. To speed up the calculation, a DNN model is integrated into the framework, thus enabling accurate and fast estimation of network reliability for given link and node reliability values. The DNN accuracy, based on the RMSE (0.01460), outperforms previous traditional artificial neural network (ANN) approaches. Moreover, the DNN model takes 0.3 ms to compute the reliability for any given links and reliability values. The proposed framework can provide not only reliability point estimates but also credible intervals. Finally, we take advantage of Bayesian methods to integrate new data into the framework as they become available. The framework uses the new data to refine and further update the degradation model parameters and the prediction of the reliability of links, nodes, and the network. The proposed methodology has been demonstrated with the real-world network topology Ion (125 nodes, 150 links) with actual degradation data.
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495.
Dot Com Mantra Arora, Payal
2010, 20160513, 2016-05-16, 2010-09-01
eBook
Billions of dollars are being spent nationally and globally on providing computing access to digitally disadvantaged groups and cultures with an expectation that computers and the Internet can lead ...to higher socio-economic mobility. This ethnographic study of social computing in the Central Himalayas, India, investigates alternative social practices with new technologies and media amongst a population that is for the most part undocumented. In doing so, this book offers fresh and critical perspectives in areas of contemporary debate: informal learning with computers, cyberleisure, gender access and empowerment, digital intermediaries, and glocalization of information and media.
For the past few years, the automation of transportation becomes a hot research topic for smart cities. Intelligent Transportation System (ITS) aims to manage and optimize the traffic congestion, ...road accidents, parking allocation using Autonomous Vehicles (AV) system, where the AVs are internally connected for message passing and critical decision making in time-sensitive applications. The data security in such applications can be offered using Blockchain (BC) technology. But, as per the existing literature, there exists no system which can call AVs automatically based on the situation, i.e., call an ambulance in case of an accident, call logistic service in case of home transfer, and call the traffic department in case of traffic jam. Motivated from the aforementioned reasons, in this article, we propose a BC-based secure and intelligent sensing and tracking architecture for AV system using beyond 5G communication network. Recently, AVs are facing issues with sensing and tracking technology as well as the data thefts. AV system contains sensitive information and transfers it through a communication channel to Connected AVs (CAVs), where the corrupted information or delay of a fraction of a second can lead to a critical situation. So, here we present possib the attacks and safety countermeasures using BC technology to protect the AV system. The proposed architecture ensures secure sensing and tracking of an object through BC by deploying AI algorithms at the edge servers. Also, the beyond 5G network enables communications with low latency and high reliability to meet the desires of the aforementioned time-sensitive applications. The proposed system is evaluated by considering the parameters as mobility and data transfer time against the traditional LTE-A and 5G communication networks. The proposed system outperforms traditional systems and can be suitable for diverse applications where latency, reliability, and security are the prime concerns.
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Small cells are deployed in 5G networks to complement the macro cells for improving coverage and capacity. Small cells and edge computing are natural partners which can improve users’ experience. ...Small cell nodes (SCNs) equipped with edge servers can support emerging computing services such as virtual reality which impose low-latency and precise contextual requirements. With the proliferation of wireless devices, there is an increasing demand for offloading tasks to SCNs. Given limited computation and communication resources, the fundamental problem for a small cell network is how to select computing tasks to maximize effective rewards in an uncertain and stochastic environment. To this end, we propose an online learning framework, LFSC, which has the performance guarantee to guide task offloading in a small cell network. LFSC balances between reward and constraint violations, and it consists of three subroutines: i) a randomized algorithm which calculates selection probability of each task based on task weights; ii) a greedy assignment algorithm which cooperatively allocates tasks among different SCNs based on the selection probability; iii) an update algorithm which exploits the multi-armed bandit (MAB) technique to update task weights according to the feedback. Our theoretical analysis shows that both the regret and violations metrics of LFSC have the sub-linear property. Extensive simulation studies based on real world data confirm that LFSC achieves a close-to-optimal reward with low violations, and outperforms many state-of-the-art algorithms.
In a wavelength-division-multiplexing (WDM) optical network, the failure of network elements (e.g., fiber links and cross connects) may cause the failure of several optical channels, thereby leading ...to large data losses. This study examines different approaches to protect a mesh-based WDM optical network from such failures. These approaches are based on two survivability paradigms: 1) path protection/restoration and 2) link protection/restoration. The study examines the wavelength capacity requirements, and routing and wavelength assignment of primary and backup paths for path and link protection and proposes distributed protocols for path and link restoration. The study also examines the protection-switching time and the restoration time for each of these schemes, and the susceptibility of these schemes to multiple link failures. The numerical results obtained for a representative network topology with random traffic demands demonstrate that there is a tradeoff between the capacity utilization and the susceptibility to multiple link failures. We find that, on one hand, path protection provides significant capacity savings over link protection, and shared protection provides significant savings over dedicated protection; while on the other hand, path protection is more susceptible to multiple link failures than link protection, and shared protection is more susceptible to multiple link failures than dedicated protection. We formulate a model of protection-switching times for the different protection schemes based on a fully distributed control network. We propose distributed control protocols for path and link restoration. Numerical results obtained by simulating these protocols indicate that, for a representative network topology, path restoration has a better restoration efficiency than link restoration, and link restoration has a faster restoration time compared with path restoration.
FAST Yang, Xiangrui; Sun, Zhigang; Li, Junnan ...
2019 IEEE/ACM 27th International Symposium on Quality of Service (IWQoS),
06/2019
Conference Proceeding
The evolution of new technologies in network community is getting ever faster. Yet it remains the case that prototyping those novel mechanisms on a real-world system (i.e. CPU-FPGA platforms) is both ...time and labor consuming, which has a serious impact on the research timeliness. In order to bring researchers out of trivial process in prototype development, this paper proposed FAST, a software hardware co-design framework for fast network prototyping. With the programming abstraction of FAST, researchers are able to prototype (using C, verilog or both) a wide spectrum of network boxes rapidly based on all kinds of CPU-FPGA platforms. FAST framework takes care of managing DMA, PCIe and Linux Kernel while providing a unified API for researchers so they can focus only on the packet processing functions. We demonstrate FAST framework's easy to use features with a number of prototypes and show we can get over 10x gains in performance or 1000x better accuracy in clock synchronization compared with their software versions.
The focus of this paper is to develop a distributed control algorithm that will regulate the power output of multiple photovoltaic generators (PVs) in a distribution network. To this end, the ...cooperative control methodology from network control theory is used to make a group of PV generators converge and operate at certain (or the same) ratio of available power, which is determined by the status of the distribution network and the PV generators. The proposed control only requires asynchronous information intermittently from neighboring PV generators, making a communication network among the PV units both simple and necessary. The minimum requirement on communication topologies is also prescribed for the proposed control. It is shown that the proposed analysis and design methodology has the advantages that the corresponding communication networks are local, their topology can be time varying, and their bandwidth may be limited. These features enable PV generators to have both self-organizing and adaptive coordination properties even under adverse conditions. The proposed method is simulated using the IEEE standard 34-bus distribution network.
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