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Asynchronous local construction of bounded-degree network topologies using only neighborhood information

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arXiv 2018年

作者： Koyuncu, Erdem Jafarkhani, Hamid Department of Electrical and Computer Engineering University of Illinois Chicago United States Center for Pervasive Communications and Computing University of California Irvine United States

We consider ad-hoc networks consisting of n wireless nodes that are located on the plane. Any two given nodes are called neighbors if they are located within a certain distance (communication range) from one another. A given node can be directly connected to any one of its neighbors and picks its connections according to a unique topology control algorithm that is available at every node. Given that each node knows only the indices (unique identification numbers) of its one- and two-hop neighbors, we identify an algorithm that preserves connectivity and can operate without the need of any synchronization among nodes. Moreover, the algorithm results in a sparse graph with at most 5n edges and a maximum node degree of 10. Existing algorithms with the same promises further require neighbor distance and/or direction information at each node. We also evaluate the performance of our algorithm for random networks. In this case, our algorithm provides an asymptotically connected network with n(1 + o(1)) edges with a degree less than or equal to 6 for 1 − o(1) fraction of the nodes. We also introduce another asynchronous connectivity-preserving algorithm that can provide an upper bound as well as a lower bound on node degrees. Copyright © 2018, The Authors. All rights reserved.

关键词： Ad hoc networks

Excitable london: Street map analysis with oregonator model

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arXiv 2018年

作者： Adamatzky, Andrew Phillips, Neil Weerasekera, Roshan Tsompanas, Michail-Antisthenis Sirakoulis, Georgios Ch. Department of Computer Science and Creative Technologies Unconventional Computing Lab Uwe Bristol United Kingdom Department of Engineering Design and Mathematics Uwe Bristol United Kingdom Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece

We explore geometry of London's streets using computational mode of an excitable chemical system, Belousov-Zhabotinsky (BZ) medium. We virtually fill in the streets with a BZ medium and study propagation of excitation waves for a range of excitability parameters, gradual transition from excitable to subexcitable to non-excitable. We demonstrate a pruning strategy adopted by the medium with decreasing excitability when wider and ballistically appropriate streets are selected. We explain mechanics of streets selection and pruning. The results of the paper will be used in future studies of studying dynamics of cities with living excitable substrates. Copyright © 2018, The Authors. All rights reserved.

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Community Detection Method Based on User Influence Probability and Similarity

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Community Detection Method Based on User Influence Probabili...

International Conference on Advanced Cloud and Big Data (CBD)

作者： Sheng-chen Yang Lei-lei Shi Lu Liu School of Computer Science and Telecommunication Engineering Jiangsu University Zhen Jiang China Department of Computing Electrical and Mathematics University of Derby Derby England

With the development of the social network, the rapid spread of micro-blog information and its convenience have enabled a large number of users to share their daily activities. On the basis of interest, users on Twitter formed a community by exchanging views and establishing friendships with others. Most existing community division methods use classification or clustering method to separate communities by single user interest weighting, and the effect is not ideal. In this paper, we propose a community detection method based on user impact probability and interest model called IPS(base on Influence Probability and Similarity). This paper predicts the probability of influence between two users. Finally, we assign users to communities with higher interest and impact probability and higher public connection topology. In addition, we test the ability of our algorithm by experiments on the actual micro-blog dataset. The experimental results show that the algorithm is effective for micro-blog.

关键词： Social network services Clustering algorithms Prediction algorithms Classification algorithms Probability Partitioning algorithms Heuristic algorithms

Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks

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Malware Detection in Cloud Infrastructures Using Convolution...

IEEE International Conference on Cloud computing, CLOUD

作者： Mahmoud Abdelsalam Ram Krishnan Yufei Huang Ravi Sandhu Department of Computer Science Institute for Cyber Security and Center for Security and Privacy Enhanced Cloud Computing Department of Electrical and Computer Engineering University of Texas at San Antonio San Antonio Texas USA

A major challenge in Infrastructure as a Service (IaaS) clouds is its exposure to malware. Malware can spread rapidly within a datacenter and can cause major disruption to a cloud service provider and its clients. This paper introduces and discusses an effective malware detection approach in cloud infrastructure using Convolutional Neural Network (CNN), a deep learning approach. We initially employ a standard 2d CNN by training on metadata available for each of the processes in a virtual machine (VM) obtained by means of the hypervisor. We enhance the CNN classifier accuracy by using a novel 3d CNN (where an input is a collection of samples over a time interval), which greatly helps reduce mislabelled samples during data collection and training. Our experiments are performed on data collected by running various malware (mostly Trojans and Rootkits) on VMs. The malware used in our experiments are randomly selected. This reduces the selection bias of known-to-be highly active malware for easy detection. We demonstrate that our 2d CNN model reaches an accuracy of ≃ 79%, and our 3d CNN model significantly improves the accuracy to ≃ 90%.

关键词： Malware Cloud computing Measurement Feature extraction Static analysis Training Machine learning

Optimal Throughput Fairness Tradeoffs for Downlink Non-Orthogonal Multiple Access over Fading Channels

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Optimal Throughput Fairness Tradeoffs for Downlink Non-Ortho...

作者： Xing, Hong Liu, Yuanawei Nallanathan, Arumugam Ding, Zhiguo Poor, H. Vincent School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electrical Engineering Princeton University PrincetonNJ08544 United States School of Computing and Communications Lancaster University LancasterLA1 4YW United Kingdom

Recently, non-orthogonal multiple access (NOMA) has attracted considerable interest as one of the 5G-enabling techniques. However, the users with better channel conditions in downlink communications intrinsically benefit more from NOMA than the users with worse channel conditions thanks to successive decoding, judicious designs are required to guarantee user fairness. In this paper, a two-user downlink NOMA system over fading channels is considered. For delay-tolerant transmission, the average sum rate is maximized subject to both average and peak-power constraints as well as a minimum average user rate constraint. The optimal resource allocation is obtained using the Lagrangian dual decomposition under full channel state information at the transmitter (CSIT), while an effective power allocation policy under partial CSIT is also developed based on analytical results. In parallel, for delay-limited transmission, the sum of delay-limited throughput (DLT) is maximized subject to a maximum allowable user outage constraint under full CSIT, and the analysis for the sum of DLT is also performed under partial CSIT. Furthermore, an optimal orthogonal multiple access (OMA) scheme is also studied as a benchmark to prove the superiority of NOMA over OMA under full CSIT. Finally, the theoretical analysis is verified by simulations via different tradeoffs for the average sum rate (sum-DLT) versus the minimum (maximum) average user rate (outage) requirement. © 2002-2012 IEEE.

关键词： Fading channels

A compact structure for realizing Lorentzian, Fano and EIT resonance lineshapes in a microring resonator

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arXiv 2019年

作者： Gu, Linpeng Fang, Hanlin Li, Juntao Fang, Liang Chua, Soo Jin Zhao, Jianlin Gan, Xuetao MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions Shaanxi Key Laboratory of Optical Information Technology School of Science Northwestern Polytechnical University Xi'an710072 China State Key Laboratory of Optoelectronic Materials and Technologies School of Physics Sun Yat-Sen University Guangzhou510275 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore117583 Singapore

Microring resonators, as a fundamental building block of photonic integrated circuits, have been well developed into numerous functional devices, whose performances are strongly determined by microring's resonance lineshapes. We propose a compact structure to reliably realize Lorentzian, Fano, and electromagnetically induced transparency (EIT) resonance lineshapes in a microring. By simply inserting two air-holes in the side-coupled waveguide of a microring, a Fabry-Perot (FP) resonance is involved to couple with microring's resonant modes, showing Lorentzian, Fano, and EIT lineshapes over one free spectral range of the FP resonance. The quality factors, extinction ratios, and slope rates in different lineshapes are discussed. At microring's specific resonant wavelength, the lineshape could be tuned among these three types by controlling the FP cavity's length. Experiment results verify the theoretical analysis well and represent Fano lineshapes with extinction ratios of about 20 dB and slope rates over 280 dB/nm. The reliably and flexibly tunable lineshapes in the compact structure have potentials to improve microring-based devices and expand their application scopes. Copyright © 2019, The Authors. All rights reserved.

关键词： Light extinction

Interleaving channel estimation and limited feedback for point-to-point systems with a large number of transmit antennas

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arXiv 2018年

作者： Koyuncu, Erdem Zou, Xun Jafarkhani, Hamid Department of Electrical and Computer Engineering University of Illinois Chicago United States Center for Pervasive Communications & Computing University of California Irvine United States

We introduce and investigate the opportunities of multi-antenna communication schemes whose training and feedback stages are interleaved and mutually interacting. Specifically, unlike the traditional schemes where the transmitter first trains all of its antennas at once and then receives a single feedback message, we consider a scenario where the transmitter instead trains its antennas one by one and receives feedback information immediately after training each one of its antennas. The feedback message may ask the transmitter to train another antenna;or, it may terminate the feedback/training phase and provide the quantized codeword (e.g., a beamforming vector) to be utilized for data transmission. As a specific application, we consider a multiple-input single-output system with t transmit antennas, a short-term power constraint P, and target data rate ρ. We show that for any t, the same outage probability as a system with perfect transmitter and receiver channel state information can be achieved with a feedback rate of R1 bits per channel state and via training R2 transmit antennas on average, where R1 and R2 are independent of t, and depend only on ρ and P. In addition, we design variable-rate quantizers for channel coefficients to further minimize the feedback rate of our scheme. Copyright © 2018, The Authors. All rights reserved.

关键词： Beamforming

Effective Quantization Approaches for Recurrent Neural Networks

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Effective Quantization Approaches for Recurrent Neural Netwo...

International Joint Conference on Neural Networks (IJCNN)

作者： Md Zahangir Alom Adam T Moody Naoya Maruyama Brian C Van Essen Tarek M. Taha Department of Electrical and Computer Engineering University of Dayton OH USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory CA USA

Deep learning, Recurrent Neural Networks (RNN) in particular have shown superior accuracy in a large variety of tasks including machine translation, language understanding, and movie frames generation. However, these deep learning approaches are very expensive in terms of computation. In most cases, Graphic Processing Units (GPUs) are in used for large scale implementations. Meanwhile, energy efficient RNN approaches are proposed for deploying solutions on special purpose hardware including Field Programming Gate Arrays (FPGAs) and mobile platforms. In this paper, we propose an effective quantization approach for Recurrent Neural Networks (RNN) techniques including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Convolutional Long Short Term Memory (ConvLSTM). We have implemented different quantization methods including Binary Connect {-1, 1}, Ternary Connect {-1, 0, 1}, and Quaternary Connect {-1, -0.5, 0.5, 1}. These proposed approaches are evaluated on different datasets for sentiment analysis on IMDB and video frame predictions on the moving MNIST dataset. The experimental results are compared against the full precision versions of the LSTM, GRU, and ConvLSTM. They show promising results for both sentiment analysis and video frame prediction.

关键词： Recurrent neural networks Logic gates Quantization (signal) Computational modeling Machine learning Hardware

A game theoretic approach for dynamic information flow tracking to detect multi-stage advanced persistent threats

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arXiv 2018年

作者： Moothedath, Shana Sahabandu, Dinuka Allen, Joey Clark, Andrew Bushnell, Linda Lee, Wenke Poovendran, Radha Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States College of Computing Georgia Institute of Technology AtlantaGA30332 United States

Advanced Persistent Threats (APTs) infiltrate cyber systems and compromise specifically targeted data and/or resources through a sequence of stealthy attacks consisting of multiple stages. Dynamic information flow tracking has been proposed to detect APTs. In this paper, we develop a dynamic information flow tracking game for resource-efficient detection of APTs via multi-stage dynamic games. The game evolves on an information flow graph, whose nodes are processes and objects (e.g. file, network end points) in the system and the edges capture the interaction between different processes and objects. Each stage of the game has pre-specified targets which are characterized by a set of nodes of the graph and the goal of the APT is to evade detection and reach a target node of that stage. The goal of the defender is to maximize the detection probability while minimizing performance overhead on the system. The resource costs of the players are different and information structure is asymmetric resulting in a nonzero-sum imperfect information game. We first calculate the best responses of the players and characterize the set of Nash equilibria for single stage attacks. Subsequently, we provide a polynomial-time algorithm to compute a correlated equilibrium for the multi-stage attack case. Finally, we experiment our model and algorithms on real-world nation state attack data obtained from Refinable Attack Investigation system. Copyright © 2018, The Authors. All rights reserved.

关键词： Game theory