arXiv subjects

Peng Liu

Publications and source records attributed to Peng Liu.

At least 361 records · Page 20Linked to original sources

arXiv2021

Analyzing the Overhead of Filesystem Protection Using Linux Security Modules

Over the years, the complexity of the Linux Security Module (LSM) is keeping increasing, and the count of the authorization hooks is nearly doubled. It is important to provide up-to-date measurement results of LSM for system practitioners so that they can make prudent trade-offs between security and performance. This work evaluates the overhead of LSM for file accesses on Linux v5.3.0. We build a performance evaluation framework for LSM. It has two parts, an extension of LMBench2.5 to evaluate the overhead of file operations for different security modules, and a security module with tunable latency for policy enforcement to study the impact of the latency of policy enforcement on the end-to-end latency of file operations. In our evaluation, we find opening a file would see about 87% (Linux v5.3) performance drop when the kernel is integrated with SELinux hooks (policy enforcement disabled) than without, while the figure was 27% (Linux v2.4.2). We found that performance of the above downgrade is affected by two parts, policy enforcement and hook placement. To further investigate the impact of policy enforcement and hook placement respectively, we build a Policy Testing Module, which reuses hook placements of LSM, while alternating latency of policy enforcement. With this module, we are able to quantitatively estimate the impact of the latency of policy enforcement on the end-to-end latency of file operations by using a multiple linear regression model and count policy authorization frequencies for each syscall. We then discuss and justify the evaluation results with static analysis on our enhanced syscalls' call graphs.

cs.CR↗

arXiv2021

GPT Conjecture: Understanding the Trade-offs between Granularity, Performance and Timeliness in Control-Flow Integrity

Performance/security trade-off is widely noticed in CFI research, however, we observe that not every CFI scheme is subject to the trade-off. Motivated by the key observation, we ask three questions. Although the three questions probably cannot be directly answered, they are inspiring. We find that a deeper understanding of the nature of the trade-off will help answer the three questions. Accordingly, we proposed the GPT conjecture to pinpoint the trade-off in designing CFI schemes, which says that at most two out of three properties (fine granularity, acceptable performance, and preventive protection) could be achieved.

cs.CR↗

arXiv2021

Low-cost and high-performance data augmentation for deep-learning-based skin lesion classification

Although deep convolutional neural networks (DCNNs) have achieved significant accuracy in skin lesion classification comparable or even superior to those of dermatologists, practical implementation of these models for skin cancer screening in low resource settings is hindered by their limitations in computational cost and training dataset. To overcome these limitations, we propose a low-cost and high-performance data augmentation strategy that includes two consecutive stages of augmentation search and network search. At the augmentation search stage, the augmentation strategy is optimized in the search space of Low-Cost-Augment (LCA) under the criteria of balanced accuracy (BACC) with 5-fold cross validation. At the network search stage, the DCNNs are fine-tuned with the full training set in order to select the model with the highest BACC. The efficiency of the proposed data augmentation strategy is verified on the HAM10000 dataset using EfficientNets as a baseline. With the proposed strategy, we are able to reduce the search space to 60 and achieve a high BACC of 0.853 by using a single DCNN model without external database, suitable to be implemented in mobile devices for DCNN-based skin lesion detection in low resource settings.

cs.CV↗

arXiv2020

Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder

One of the most challenging problems in the field of intrusion detection is anomaly detection for discrete event logs. While most earlier work focused on applying unsupervised learning upon engineered features, most recent work has started to resolve this challenge by applying deep learning methodology to abstraction of discrete event entries. Inspired by natural language processing, LSTM-based anomaly detection models were proposed. They try to predict upcoming events, and raise an anomaly alert when a prediction fails to meet a certain criterion. However, such a predict-next-event methodology has a fundamental limitation: event predictions may not be able to fully exploit the distinctive characteristics of sequences. This limitation leads to high false positives (FPs) and high false negatives (FNs). It is also critical to examine the structure of sequences and the bi-directional causality among individual events. To this end, we propose a new methodology: Recomposing event sequences as anomaly detection. We propose DabLog, a Deep Autoencoder-Based anomaly detection method for discrete event Logs. The fundamental difference is that, rather than predicting upcoming events, our approach determines whether a sequence is normal or abnormal by analyzing (encoding) and reconstructing (decoding) the given sequence. Our evaluation results show that our new methodology can significantly reduce the numbers of FPs and FNs, hence achieving a higher $F_1$ score.

cs.LG↗

arXiv2020

Generating Comprehensive Data with Protocol Fuzzing for Applying Deep Learning to Detect Network Attacks

Network attacks have become a major security concern for organizations worldwide and have also drawn attention in the academics. Recently, researchers have applied neural networks to detect network attacks with network logs. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new approach, protocol fuzzing, to automatically generate high-quality network data, on which deep learning models can be trained. Our findings show that fuzzing generates data samples that cover real-world data and deep learning models trained with fuzzed data can successfully detect real network attacks.

cs.CR↗

arXiv2020

Looking for the possible gluon condensation signature in sub-TeV gamma-ray spectra: from active galactic nuclei to gamma ray bursts

The gluon condensation in the proton as a dynamical model is used to treat a series of unsolved puzzles in sub-TeV gamma ray spectra, they include the broken power-law of blazar's radiation, the hardening confusion of 1ES 1426+428, Mkn 501, and the recently recorded sub-TeV gamma spectra of GRB 180720B and GRB 190114C. We find that the above anomalous phenomena in gamma ray energy spectra can be understood with the simple broken power law based on a QCD gluon condensation effect.

astro-ph.HE↗

arXiv2020

Simulation of the orbit and spin period evolution of the double pulsars PSR J0737-3039 from their birth to coalescence induced by the gravitational wave radiation

The complete orbital and sin period evolutions of the double neutron star (NS) system PSR J0737-3039 are simulated from the birth to coalescence, which include the two observed radio pulsars classified as primary NS PSR J0737-3039A and companion NS PSR J0737-3039B. By employing the characteristic age of PSR J0737-3039B to constrain the true age of the double pulsar system, the initial orbital period and initial binary separation are obtained as 2.89 hrs and $1.44 \times 10^{6}$ km, respectively, and the coalescence age or the lifetime from the birth to merger of PSR J0737-3039 is obtained to be $1.38 \times 10^{8}$ yr. At the last minute of coalescence, corresponding to that the gravitational wave frequency changes from 20 Hz to 1180 Hz, we present the binary separation of PSR J0737-3039 to be from 442 km to 30 km, while the spin periods of PSR J0737-3039A and PSR J0737-3039B are 27.10 ms and 4.63 s, respectively. From the standard radio pulsar emission model, before the system merged, the primary NS could still be observed by the radio telescope, while the companion NS has crossed the death line in the pulsar magnetic-field versus period ($B-P$) diagram which is usually considered to cease the life as a pulsar. It is for the first time that the whole life evolutionary simulation of the orbit and spin periods for double NS system is presented, which provides the useful information for observing the primary NS at the coalescence stage.

astro-ph.HE↗

arXiv2020

Echoes from phantom wormholes

We study the time evolution of the test scalar and electromagnetic fields perturbations in configurations of phantom wormholes surrounded by dark energy with state parameter $ω< -1$. We observe obvious signals of echoes reflecting wormholes properties and disclose the physical reasons behind such phenomena. In particular, we find that the dark energy equation of state has a clear imprint in echoes in wave perturbations. When $ω$ approaches the phantom divide $ω=-1$ from below, the delay time of echoes becomes longer. The echo of gravitational wave is likely to be detected in the near future, the signature of the dark energy equation of state in the echo spectrum can serve as a local measurement of the dark energy.

gr-qc↗

arXiv2020

The study of calibration for the hybrid pixel detector with single photon counting in HEPS-BPIX

The calibration process for the hybrid array pixel detector designed for High Energy Photon Source in China, we called HEPS-BPIX, is presented in this paper. Based on the threshold scanning, the relationship between energy and threshold is quantified for the threshold calibration. For the threshold trimming, the precise algorithm basing on LDAC characteristic and fast algorithm basing on LDAC scanning are proposed in this paper to study the performance of the threshold DACs which will be applied to the pixel. The threshold dispersion has been reduced from 46.28 mV without algorithm to 6.78 mV with the precise algorithm, whereas it is 7.61 mV with fast algorithm. For the temperature from 5 to 60 , the threshold dispersion of precise algorithm varies in the range of about 5.69 mV, whereas it is about 33.21 mV with the fast algorithm which can be re-corrected to 1.49 mV. The measurement results show that the fast algorithm could get the applicable threshold dispersion for a silicon pixel module and take a shorter time, while the precise algorithm could get better threshold dispersion, but time consuming. The temperature dependence of the silicon pixel module noise is also studied to assess the detector working status. The minimum detection energy can be reduced about 0.83 keV at a 20 lower temperature.

physics.ins-det↗

arXiv2020

Yang-Yang functions, Monodromy and knot polynomials

We derive a structure of $\mathbb{Z}[t,t^{-1}]$-module bundle from a family of Yang-Yang functions. For the fundamental representation of the complex simple Lie algebra of classical type, we give explicit wall-crossing formula and prove that the monodromy representation of the $\mathbb{Z}[t,t^{-1}]$-module bundle is equivalent to the braid group representation induced by the universal R-matrices of $U_{h}(g)$. We show that two transformations induced on the fiber by the symmetry breaking deformation and respectively the rotation of two complex parameters commute with each other.

math-ph↗

arXiv2020

Mixed State Entanglement and Thermal Phase Transitions

We study the relationship between mixed state entanglement and thermal phase transitions. As a typical example, we compute the holographic entanglement entropy (HEE), holographic mutual information (MI) and the holographic entanglement of purification (EoP) over the superconductivity phase transition. We find that HEE, MI and EoP can all diagnose the superconducting phase transition. They are continuous at the critical point, but their first derivative with respect to temperature is discontinuous. MI decreases with increasing temperature and exhibits a convex behavior, while HEE increases with increasing temperature and exhibits a concave behavior. However, EoP can exhibit either the same or the opposite behavior as MI, depending on the size of the specific configuration. These results show that EoP captures more abundant information than HEE and MI. We also provide a new algorithm to compute the EoP for general configurations.

hep-th↗

arXiv2020

Limousine Service Management: Capacity Planning with Predictive Analytics and Optimization

The limousine service in luxury hotels is an integral component of the whole customer journey in the hospitality industry. One of the largest hotels in Singapore manages a fleet of both in-house and outsourced vehicles around the clock, serving 9000 trips per month on average. The need for vehicles may scale up rapidly, especially during special events and festive periods in the country. The excess demand is met by having additional outsourced vehicles on standby, incurring millions of dollars of additional expenses per year for the hotel. Determining the required number of limousines by hour of the day is a challenging service capacity planning problem. In this paper, a recent transformational journey to manage this problem in the hotel is introduced, driving up to S\$3.2 million of savings per year with improved service level. The approach builds on widely available open-source statistical and spreadsheet optimization tools, along with robotic process automation, to optimize the schedule of its fleet of limousines and drivers, and to support decision-making for planners/controllers to drive sustained business value.

stat.AP↗

arXiv2020

Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews

Online customer reviews have become important for managers and executives in the hospitality and catering industry who wish to obtain a comprehensive understanding of their customers' demands and expectations. We propose a Regularized Text Logistic (RTL) regression model to perform text analytics and sentiment classification on unstructured text data, which automatically identifies a set of statistically significant and operationally insightful word features, and achieves satisfactory predictive classification accuracy. We apply the RTL model to two online review datasets, Restaurant and Hotel, from TripAdvisor. Our results demonstrate satisfactory classification performance compared with alternative classifiers with a highest true positive rate of 94.9%. Moreover, RTL identifies a small set of word features, corresponding to 3% for Restaurant and 20% for Hotel, which boosts working efficiency by allowing managers to drill down into a much smaller set of important customer reviews. We also develop the consistency, sparsity and oracle property of the estimator.

stat.ML↗

arXiv2020

ServeNet: A Deep Neural Network for Web Services Classification

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.

cs.LG↗

arXiv2020

Randomized Last-Level Caches Are Still Vulnerable to Cache Side-Channel Attacks! But We Can Fix It

Cache randomization has recently been revived as a promising defense against conflict-based cache side-channel attacks. As two of the latest implementations, CEASER-S and ScatterCache both claim to thwart conflict-based cache side-channel attacks using randomized skewed caches. Unfortunately, our experiments show that an attacker can easily find a usable eviction set within the chosen remap period of CEASER-S and increasing the number of partitions without dynamic remapping, such as ScatterCache, cannot eliminate the threat. By quantitatively analyzing the access patterns left by various attacks in the LLC, we have newly discovered several problems with the hypotheses and implementations of randomized caches, which are also overlooked by the research on conflict-based cache side-channel attack. However, cache randomization is not a false hope and it is an effective defense that should be widely adopted in future processors. The newly discovered problems are corresponding to flaws associated with the existing implementation of cache randomization and are fixable. Several new defense techniques are proposed in this paper. our experiments show that all the newly discovered vulnerabilities of existing randomized caches are fixed within the current performance budget. We also argue that randomized set-associative caches can be sufficiently strengthened and possess a better chance to be actually adopted in commercial processors than their skewed counterparts as they introduce less overhaul to the existing cache structure.

cs.CR↗

arXiv2020

Informational properties for Einstein-Maxwell-Dilaton Gravity

We study the information quantities, including the holographic entanglement entropy (HEE), mutual information (MI) and entanglement of purification (EoP), over Gubser-Rocha model. The remarkable property of this model is the zero entropy density at ground state, in term of which we expect to extract novel, even singular informational properties in zero temperature limit. Surprisedly, we do not observe any singular behavior of entanglement-related physical quantities under the zero temperature limit. Nevertheless, we find a peculiar property from Gubser-Rocha model that in low temperature region, the HEE decreases with the increase of temperature, which is contrary to that in most holographic models. We argue that this novel phenomenon is brought by the singular property of the zero temperature limit, of which the analytical verification is present. In addition, we also compare the features of the information quantities in Gubser-Rocha model with those in Reissner-Nordstrom Anti-de Sitter (RN-AdS) black hole model. It is shown that the HEE and MI of Gubser-Rocha model are always larger than those of RN-AdS model, while the EoP behaves in an opposite way. Our results indicate that MI and EoP could have different abilities in describing mixed state entanglement.

hep-th↗

arXiv2020

Outcome-Guided Disease Subtyping for High-Dimensional Omics Data

High-throughput microarray and sequencing technology have been used to identify disease subtypes that could not be observed otherwise by using clinical variables alone. The classical unsupervised clustering strategy concerns primarily the identification of subpopulations that have similar patterns in gene features. However, as the features corresponding to irrelevant confounders (e.g. gender or age) may dominate the clustering process, the resulting clusters may or may not capture clinically meaningful disease subtypes. This gives rise to a fundamental problem: can we find a subtyping procedure guided by a pre-specified disease outcome? Existing methods, such as supervised clustering, apply a two-stage approach and depend on an arbitrary number of selected features associated with outcome. In this paper, we propose a unified latent generative model to perform outcome-guided disease subtyping constructed from omics data, which improves the resulting subtypes concerning the disease of interest. Feature selection is embedded in a regularization regression. A modified EM algorithm is applied for numerical computation and parameter estimation. The proposed method performs feature selection, latent subtype characterization and outcome prediction simultaneously. To account for possible outliers or violation of mixture Gaussian assumption, we incorporate robust estimation using adaptive Huber or median-truncated loss function. Extensive simulations and an application to complex lung diseases with transcriptomic and clinical data demonstrate the ability of the proposed method to identify clinically relevant disease subtypes and signature genes suitable to explore toward precision medicine.

stat.ME↗

arXiv2020

Speaker Independent and Multilingual/Mixlingual Speech-Driven Talking Head Generation Using Phonetic Posteriorgrams

Generating 3D speech-driven talking head has received more and more attention in recent years. Recent approaches mainly have following limitations: 1) most speaker-independent methods need handcrafted features that are time-consuming to design or unreliable; 2) there is no convincing method to support multilingual or mixlingual speech as input. In this work, we propose a novel approach using phonetic posteriorgrams (PPG). In this way, our method doesn't need hand-crafted features and is more robust to noise compared to recent approaches. Furthermore, our method can support multilingual speech as input by building a universal phoneme space. As far as we know, our model is the first to support multilingual/mixlingual speech as input with convincing results. Objective and subjective experiments have shown that our model can generate high quality animations given speech from unseen languages or speakers and be robust to noise.

eess.AS↗