arXiv subjects

Hui Guo

Publications and source records attributed to Hui Guo.

71 records · Page 4Linked to original sources

arXiv2020

DualNet: Locate Then Detect Effective Payload with Deep Attention Network

Network intrusion detection (NID) is an essential defense strategy that is used to discover the trace of suspicious user behaviour in large-scale cyberspace, and machine learning (ML), due to its capability of automation and intelligence, has been gradually adopted as a mainstream hunting method in recent years. However, traditional ML based network intrusion detection systems (NIDSs) are not effective to recognize unknown threats and their high detection rate often comes with the cost of high false alarms, which leads to the problem of alarm fatigue. To address the above problems, in this paper, we propose a novel neural network based detection system, DualNet, which is constructed with a general feature extraction stage and a crucial feature learning stage. DualNet can rapidly reuse the spatial-temporal features in accordance with their importance to facilitate the entire learning process and simultaneously mitigate several optimization problems occurred in deep learning (DL). We evaluate the DualNet on two benchmark cyber attack datasets, NSL-KDD and UNSW-NB15. Our experiment shows that DualNet outperforms classical ML based NIDSs and is more effective than existing DL methods for NID in terms of accuracy, detection rate and false alarm rate.

cs.CR↗

arXiv2020

Densely Connected Residual Network for Attack Recognition

High false alarm rate and low detection rate are the major sticking points for unknown threat perception. To address the problems, in the paper, we present a densely connected residual network (Densely-ResNet) for attack recognition. Densely-ResNet is built with several basic residual units, where each of them consists of a series of Conv-GRU subnets by wide connections. Our evaluation shows that Densely-ResNet can accurately discover various unknown threats that appear in edge, fog and cloud layers and simultaneously maintain a much lower false alarm rate than existing algorithms.

cs.CR↗

arXiv2020

Pelican: A Deep Residual Network for Network Intrusion Detection

One challenge for building a secure network communication environment is how to effectively detect and prevent malicious network behaviours. The abnormal network activities threaten users' privacy and potentially damage the function and infrastructure of the whole network. To address this problem, the network intrusion detection system (NIDS) has been used. By continuously monitoring network activities, the system can timely identify attacks and prompt counter-attack actions. NIDS has been evolving over years. The current-generation NIDS incorporates machine learning (ML) as the core technology in order to improve the detection performance on novel attacks. However, the high detection rate achieved by a traditional ML-based detection method is often accompanied by large false-alarms, which greatly affects its overall performance. In this paper, we propose a deep neural network, Pelican, that is built upon specially-designed residual blocks. We evaluated Pelican on two network traffic datasets, NSL-KDD and UNSW-NB15. Our experiments show that Pelican can achieve a high attack detection performance while keeping a much low false alarm rate when compared with a set of up-to-date machine learning based designs.

cs.LG↗

arXiv2020

Generation of the Squeezed State with an Arbitrary Complex Amplitude Distribution

The squeezed state is important in quantum metrology and quantum information. The most effective generation tool known is the optical parametric oscillator (OPO). Currently, only the squeezed states of lower-order spatial modes can be generated by an OPO. However, the squeezed states of higher-order complex spatial modes are more useful for applications such as quantum metrology, quantum imaging and quantum information. A major challenge for future applications is efficient generation. Here, we use cascaded phase-only spatial light modulators to modulate the amplitude and phase of the incident fundamental mode squeezed state. This efficiently generates a series of squeezed higher-order Hermite-Gauss modes and a squeezed arbitrary complex amplitude distributed mode. The method may yield new applications in biophotonics, quantum metrology and quantum information processing.

quant-ph↗

arXiv2020

Mendelian Randomization with Incomplete Exposure Data: a Bayesian Approach

We expand Mendelian Randomization (MR) methodology to deal with randomly missing data on either the exposure or the outcome variable, and furthermore with data from nonindependent individuals (eg components of a family). Our method rests on the Bayesian MR framework proposed by Berzuini et al (2018), which we apply in a study of multiplex Multiple Sclerosis (MS) Sardinian families to characterise the role of certain plasma proteins in MS causation. The method is robust to presence of pleiotropic effects in an unknown number of instruments, and is able to incorporate inter-individual kinship information. Introduction of missing data allows us to overcome the bias introduced by the (reverse) effect of treatment (in MS cases) on level of protein. From a substantive point of view, our study results confirm recent suspicion that an increase in circulating IL12A and STAT4 protein levels does not cause an increase in MS risk, as originally believed, suggesting that these two proteins may not be suitable drug targets for MS.

stat.AP↗

arXiv2019

A Transfer Learning Approach for Network Intrusion Detection

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 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 NSL-KDD 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.

cs.LG↗

arXiv2019

LuNet: A Deep Neural Network for Network Intrusion Detection

Network attack is a significant security issue for modern society. From small mobile devices to large cloud platforms, almost all computing products, used in our daily life, are networked and potentially under the threat of network intrusion. With the fast-growing network users, network intrusions become more and more frequent, volatile and advanced. Being able to capture intrusions in time for such a large scale network is critical and very challenging. To this end, the machine learning (or AI) based network intrusion detection (NID), due to its intelligent capability, has drawn increasing attention in recent years. Compared to the traditional signature-based approaches, the AI-based solutions are more capable of detecting variants of advanced network attacks. However, the high detection rate achieved by the existing designs is usually accompanied by a high rate of false alarms, which may significantly discount the overall effectiveness of the intrusion detection system. In this paper, we consider the existence of spatial and temporal features in the network traffic data and propose a hierarchical CNN+RNN neural network, LuNet. In LuNet, the convolutional neural network (CNN) and the recurrent neural network (RNN) learn input traffic data in sync with a gradually increasing granularity such that both spatial and temporal features of the data can be effectively extracted. Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.

cs.AI↗

arXiv2019

Compressed ghost edge imaging

In this paper, we propose an advanced framework of ghost edge imaging, named compressed ghost edge imaging (CGEI). In the scheme, a set of structured speckle patterns with pixel shifting are illuminated on an unknown object, and the output is collected by a bucket detector without any spatial resolution. By using compressed sensing algorithm, we obtain the horizontal and vertical edge information of the unknown object with the bucket detector detection results and the known structured speckle patterns. The edge is finally constructed by the two-dimentional edge information. The experimental and numerical simulations results show that the proposed scheme has a higher quality and reduces the number of measurements, in comparison with the existed edge detection schemes based on ghost imaging.

eess.IV↗

arXiv2017

On nodal solutions of a nonlocal Choquard equation in a bounded domain

In this paper, we are interested in the least energy nodal solutions to the following nonlocal Choquard equation with a local term \begin{equation*}\left\{\begin{array}{rll} -Δu&=λ|u|^{p-2}u+μϕ(x)|u|^{q-2}u\\ -Δϕ&=|u|^q\\ u&=ϕ=0 \end{array}\right. \begin{gathered}\begin{array}{rll} &\mbox{in}\ Ω,\\ &\mbox{in}\ Ω,\\ &\mbox{on}\ \partialΩ, \end{array}\end{gathered}\end{equation*} where $ λ,μ>0, p\in [2,6), q\in (1,5)$ and $Ω\subset \mathbb{R}^3$ is a bounded domain. This problem may be seen as a nonlocal perturbation of the classical Lane-Emden equation $-Δu=λ|u|^{p-2}u$ in $Ω.$ The problem has a variational functional with a nonlocal term $μ\int_Ωϕ|u|^q$. The appearance of the nonlocal term makes the variational functional very different from the local case $μ=0$, for which the problem has ground state solutions and least energy nodal solutions if $p\in (2,6)$. The problem may also be viewed as a nonlocal Choquard equation with a local pertubation term when $λ\not =0$. For $μ>0$, we show that although ground state solutions always exist, the existence of least energy nodal solution depends on $q$: for $q\in(1,2)$ there does not exist a least energy nodal solution while for $q\in[2,5)$ such a solution exists. Note that $q=2$ is a critical value. In the case of a linear local perturbation, i.e., $p=2,$ if $λ<λ_1,$ the problem has a positive ground state and a least energy nodal solution. However, if $λ\geq λ_1,$ the problem has a ground state which changes sign. Hence it is also a least energy nodal solution.

math.AP↗

arXiv2017

Bound-preserving discontinuous Galerkin method for compressible miscible displacement in porous media

In this paper, we develop bound-preserving discontinuous Galerkin (DG) methods for the coupled system of compressible miscible displacement problems. We consider the problem with two components and the (volumetric) concentration of the $i$th component of the fluid mixture, $c_i$, should be between $0$ and $1$. However, $c_i$ does not satisfy the maximum principle. Therefore, the numerical techniques introduced in (X. Zhang and C.-W. Shu, Journal of Computational Physics, 229 (2010), 3091-3120) cannot be applied directly. The main idea is to apply the positivity-preserving techniques to both $c_1$ and $c_2$, respectively and enforce $c_1+c_2=1$ simultaneously to obtain physically relevant approximations. By doing so, we have to treat the time derivative of the pressure $dp/dt$ as a source in the concentration equation. Moreover, $c_i's$ are not the conservative variables, as a result, the classical bound-preserving limiter in (X. Zhang and C.-W. Shu, Journal of Computational Physics, 229 (2010), 3091-3120) cannot be applied. Therefore, another limiter will be introduced. Numerical experiments will be given to demonstrate the accuracy in $L^\infty$-norm and good performance of the numerical technique.

math.NA↗

arXiv2017

Bayesian Mendelian Randomization

Our Bayesian approach to Mendelian Randomisation uses multiple instruments to assess the putative causal effect of an exposure on an outcome. The approach is robust to violations of the (untestable) Exclusion Restriction condition, and hence it does not require instruments to be independent of the outcome conditional on the exposure and on the confounders of the exposure-outcome relationship. The Bayesian approach offers a rigorous handling of the uncertainty (e.g. about the estimated instrument-exposure associations), freedom from asymptotic approximations of the null distribution and the possibility to elaborate the model in any direction of scientific relevance. We illustrate the last feature with the aid of a study of the metabolic mediators of the disease-inducing effects of obesity, where we elaborate the model to investigate whether the causal effect of interest interacts with a covariate. The proposed model contains a vector of unidentifiable parameters, $β$, whose $j$th element represents the pleiotropic (i.e., not mediated by the exposure) component of the association of instrument $j$ with the outcome. We deal with the incomplete identifiability by assuming that the pleiotropic effect of some instruments is null, or nearly so, formally by imposing on $β$ Carvalho's horseshoe shrinkage prior, in such a way that different components of $β$ are subjected to different degrees of shrinking, adaptively and in accord with the compatibility of each individual instrument with the hypothesis of no pleiotropy. This prior requires a minimal input from the user. We present the results of a simulation study into the performance of the proposed method under different types of pleiotropy and sample sizes. Comparisons with the performance of the weighted median estimator are made. Choice of the prior and inference via Markov chain Monte Carlo are discussed.

math.ST↗

arXiv2016

Thermoresponsive Toughening in LCST-Type Hydrogels: Comparison between Semi-Interpenetrated and Grafted Networks

Hydrophilic and LCST polymer chains, poly(N,N-dimethylacrylamide) (PDMA) and poly(Nisopropylacrylamide)(PNIPA), were combined in semi-interpenetrated architectures toinvestigate their responsive properties in swollen isochoric conditions comparatively tografted network structures. Using equal weight fractions of PDMA and PNIPA, semi-IPNdesigned with opposite topologies have demonstrated a thermoresponsive behavior with verydifferent structure/properties relationships as investigated by calorimetry, swellingexperiments, tensile tests and 2D neutron scattering at rest and under deformation. In the caseof the PDMA network interpenetrated by linear PNIPA chains, the phase transition of PNIPAgives rise to the formation of large microdomains, loosely percolating the PDMA network.Above the transition, the enhancement of the mechanical properties remains low in terms ofelastic modulus and fracture energy. Conversely, the opposite topology, with PDMA chainsinterpenetrating the crosslinked PNIPA network, brings a large improvement of themechanical properties at high temperature with a 10-fold increase of the modulus and veryhigh fracture energy. By comparison with grafted hydrogels of similar composition anddifferent topologies, the impact of the primary structure over the phase-separated morphologyand the resulting mechanical properties was clearly highlighted.

cond-mat.soft↗

arXiv2016

Negative and nonlinear magnetoresistance effect in silicon strip

Both negative magnetoresistance and nonlinear magnetoresisitance were observed in silicon strip nuclear radiation detector in room temperature if we applied high magnetic field intensity in different direction. This result is different with former report. We believe this is the result of coaction of high electric field (Gunn effect) and high magnetic field, or because of the variation of number of carriers and the carriers mobility. The weak localization and Landau energy levels also affect the magnetoresistance. Different crystal orientations have different energy band structures. Complex band structures lead complex carriers mobility plus Landau energy levels. So the magnetoresisitance effect is anisotropy.

physics.ins-det↗

arXiv2015

Sufficient Covariate, Propensity Variable and Doubly Robust Estimation

Statistical causal inference from observational studies often requires adjustment for a possibly multi-dimensional variable, where dimension reduction is crucial. The propensity score, first introduced by Rosenbaum and Rubin, is a popular approach to such reduction. We address causal inference within Dawid's decision-theoretic framework, where it is essential to pay attention to sufficient covariates and their properties. We examine the role of a propensity variable in a normal linear model. We investigate both population-based and sample-based linear regressions, with adjustments for a multivariate covariate and for a propensity variable. In addition, we study the augmented inverse probability weighted estimator, involving a combination of a response model and a propensity model. In a linear regression with homoscedasticity, a propensity variable is proved to provide the same estimated causal effect as multivariate adjustment. An estimated propensity variable may, but need not, yield better precision than the true propensity variable. The augmented inverse probability weighted estimator is doubly robust and can improve precision if the propensity model is correctly specified.

math.ST↗

arXiv2014

VSEAMS: A pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes

Motivation: Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility. Integration of GWAS summary statistics (p values) and functional genomic datasets should help to elucidate mechanisms. Results: We describe the extension of a previously described non-parametric method to test whether GWAS signals are enriched in functionally defined loci to a situation where only GWAS p values are available. The approach is implemented in VSEAMS, a freely available software pipeline. We use VSEAMS to integrate functional gene sets defined via transcription factor knock down experiments with GWAS results for type 1 diabetes and find variant set enrichment in gene sets associated with IKZF3, BATF and ESRRA. IKZF3 lies in a known T1D susceptibility region, whilst BATF and ESRRA overlap other immune disease susceptibility regions, validating our approach and suggesting novel avenues of research for type 1 diabetes. Availability and implementation: VSEAMS is available for download http://github.com/ollyburren/vseams.

q-bio.GN↗

arXiv2013

Methods for scoring the collective effect of SNPs: Minor alleles of common SNPs quantitatively affect traits/diseases and are under both positive and negative selection

Most common SNPs are popularly assumed to be neutral. We here developed novel methods to examine in animal models and humans whether extreme amount of minor alleles (MAs) carried by an individual may represent extreme trait values and common diseases. We analyzed panels of genetic reference populations and identified the MAs in each panel and the MA content (MAC) that each strain carried. We also analyzed 21 published GWAS datasets of human diseases and identified the MAC of each case or control. MAC was nearly linearly linked to quantitative variations in numerous traits in model organisms, including life span, tumor susceptibility, learning and memory, sensitivity to alcohol and anti-psychotic drugs, and two correlated traits poor reproductive fitness and strong immunity. Similarly, in Europeans or European Americans, enrichment of MAs of fast but not slow evolutionary rate was linked to autoimmune and numerous other diseases, including type 2 diabetes, Parkinson's disease, psychiatric disorders, alcohol and cocaine addictions, cancer, and less life span. Therefore, both high and low MAC correlated with extreme values in many traits, indicating stabilizing selection on most MAs. The methods here are broadly applicable and may help solve the missing heritability problem in complex traits and diseases.

q-bio.GN↗

arXiv2006

Application Layer Definition and Analyses of Controller Area Network Bus for Wire Harness Assembly Machine

With the feature of multi-master bus access, nondestructive contention-based arbitration and flexible configuration, Controller Area Network (CAN) bus is applied into the control system of Wire Harness Assembly Machine (WHAM). To accomplish desired goal, the specific features of the CAN bus is analyzed by compared with other field buses and the functional performances in the CAN bus system of WHAM is discussed. Then the application layer planning of CAN bus for dynamic priority is presented. The critical issue for the use of CAN bus system in WHAM is the data transfer rate between different nodes. So processing efficient model is introduced to assist analyzing data transfer procedure. Through the model, it is convenient to verify the real time feature of the CAN bus system in WHAM.

cs.RO↗