OpenRaiser/Intern-Atlas huggingface dataset:
Cached and Confused: Web Cache Deception in the Wild
Venue: arXiv cs.CR 2020
Year: 2020

Web cache deception (WCD) is an attack proposed in 2017, where an attacker tricks a caching proxy into erroneously storing private information transmitted over the Internet and subsequently gains unauthorized access to that cached data. Due to the widespread use of web caches and, in particular, the use of massive networks of caching proxies deployed by content distribution network (CDN) providers as a critical component of the Internet, WCD puts a substantial population of Internet users at risk. We present the first large-scale study that quantifies the prevalence of WCD in 340 high-profile sites among the Alexa Top 5K. Our analysis reveals WCD vulnerabilities that leak private user data as well as secret authentication and authorization tokens that can be leveraged by an attacker to mount damaging web application attacks. Furthermore, we explore WCD in a scientific framework as an instance of the path confusion class of attacks, and demonstrate that variations on the path confusion technique used make it possible to exploit sites that are otherwise not impacted by the original attack. Our findings show that many popular sites remain vulnerable two years after the public disclosure of WCD. Our empirical experiments with popular CDN providers underline the fact that web caches are not plug & play technologies. In order to mitigate WCD, site operators must adopt a holistic view of their web infrastructure and carefully configure cache settings appropriate for their applications.
Dispel: Byzantine SMR with Distributed Pipelining
Venue: arXiv cs.CR 2020
Year: 2020

Byzantine State Machine Replication (SMR) is a long studied topic that received increasing attention recently with the advent of blockchains as companies are trying to scale them to hundreds of nodes. Byzantine SMRs try to increase throughput by either reducing the latency of consensus instances that they run sequentially or by reducing the number of replicas that send messages to others in order to reduce the network usage. Unfortunately, the former approach makes use of resources in burst whereas the latter requires CPU-intensive authentication mechanisms. In this paper, we propose a new Byzantine SMR called Dispel (Distributed Pipeline) that allows any node to distributively start new consensus instances whenever they detect sufficient resources locally. We evaluate the performance of Dispel within a single datacenter and across up to 380 machines over 3 continents by comparing it against four other SMRs. On 128 nodes, Dispel speeds up HotStuff, the Byzantine fault tolerant SMR being integrated within Facebook's blockchain, by more than 12 times. In addition, we also test Dispel under isolated and correlated failures and show that the Dispel distributed design is more robust than HotStuff. Finally, we evaluate Dispel in a cryptocurrency application with Bitcoin transactions and show that this SMR is not the bottleneck.
The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity
Venue: arXiv cs.LG 2020
Year: 2020

Algorithm performance in supervised learning is a combination of memorization, generalization, and luck. By estimating how much information an algorithm can memorize from a dataset, we can set a lower bound on the amount of performance due to other factors such as generalization and luck. With this goal in mind, we introduce the Labeling Distribution Matrix (LDM) as a tool for estimating the capacity of learning algorithms. The method attempts to characterize the diversity of possible outputs by an algorithm for different training datasets, using this to measure algorithm flexibility and responsiveness to data. We test the method on several supervised learning algorithms, and find that while the results are not conclusive, the LDM does allow us to gain potentially valuable insight into the prediction behavior of algorithms. We also introduce the Label Recorder as an additional tool for estimating algorithm capacity, with more promising initial results.
LNBot: A Covert Hybrid Botnet on Bitcoin Lightning Network for Fun and Profit
Venue: arXiv cs.CR 2020
Year: 2020

While various covert botnets were proposed in the past, they still lack complete anonymization for their servers/botmasters or suffer from slow communication between the botmaster and the bots. In this paper, we propose a new generation hybrid botnet that covertly and efficiently communicates over Bitcoin Lightning Network (LN), called LNBot. LN is a payment channel network operating on top of Bitcoin network for faster Bitcoin transactions with negligible fees. Exploiting various anonymity features of LN, we designed a scalable two-layer botnet which completely anonymize the identity of the botmaster. In the first layer, the botmaster sends commands anonymously to the C&C servers through LN transactions. Specifically, LNBot allows botmaster's commands to be sent in the form of surreptitious multihop LN payments, where the commands are encoded with ASCII or Huffman encoding to provide covert communications. In the second layer, C&C servers further relay those commands to the bots they control in their mini-botnets to launch any type of attacks to victim machines. We implemented a proof-of-concept on the actual LN and extensively analyzed the delay and cost performance of LNBot. Our analysis show that LNBot achieves better scalibility compared to the other similar blockchain botnets with negligible costs. Finally, we also provide and discuss a list of potential countermeasures to detect LNBot activities and minimize its impacts.
Channel Estimation Method and Phase Shift Design for Reconfigurable Intelligent Surface Assisted MIMO Networks
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate channel estimation for a large intelligent surface (LIS) aided multiple-input multiple-output (MIMO) communication system. Due to the close proximity of communication devices and LIS terminal, the desired channels can be modeled as line-of-sight channels which are ill-conditioned. To estimate these channel matrices with high quality, we propose a two-stage channel estimation method. In particular, we employ the conventional time-division duplexing based MIMO channel estimation technique in the first stage to estimate the direct MIMO channel between the end terminals. In the second stage, we propose to use a recently developed bilinear adaptive vector approximate message passing (BAdVAMP) algorithm to estimate ill-conditioned LIS channels. The BAdVAMP method has been shown to be accurate and robust for ill-conditioned dictionary learning problems in compressed sensing. We also propose a phase shift design for the LIS using the estimated channels. Specifically, we formulate an optimization problem that maximizes the total channel gain at the user. A closed-form expression to obtain the phase shift of each passive element in the LIS is also derived. Numerical results show that the proposed BAdVAMP based LIS channel estimation performs better than its counterpart scheme bilinear generalized AMP (BiGAMP), especially when channel matrices are ill-conditioned.
Oriented Objects as pairs of Middle Lines
Venue: arXiv cs.CV 2020
Year: 2020

The detection of oriented objects is frequently appeared in the field of natural scene text detection as well as object detection in aerial images. Traditional detectors for oriented objects are common to rotate anchors on the basis of the RCNN frameworks, which will multiple the number of anchors with a variety of angles, coupled with rotating NMS algorithm, the computational complexities of these models are greatly increased. In this paper, we propose a novel model named Oriented Objects Detection Network O^2-DNet to detect oriented objects by predicting a pair of middle lines inside each target. O^2-DNet is an one-stage, anchor-free and NMS-free model. The target line segments of our model are defined as two corresponding middle lines of original rotating bounding box annotations which can be transformed directly instead of additional manual tagging. Experiments show that our O^2-DNet achieves excellent performance on ICDAR 2015 and DOTA datasets. It is noteworthy that the objects in COCO can be regard as a special form of oriented objects with an angle of 90 degrees. O^2-DNet can still achieve competitive results in these general natural object detection datasets.
Non-linear and Selective Fusion of Cross-Modal Images
Venue: arXiv cs.CV 2020
Year: 2020

The human visual perception system has strong robustness in image fusion. This robustness is based on human visual perception system's characteristics of feature selection and non-linear fusion of different features. In order to simulate the human visual perception mechanism in image fusion tasks, we propose a multi-source image fusion framework that combines illuminance factors and attention mechanisms. The framework effectively combines traditional image features and modern deep learning features. First, we perform multi-scale decomposition of multi-source images. Then, the visual saliency map and the deep feature map are combined with the illuminance fusion factor to perform high-low frequency nonlinear fusion. Secondly, the characteristics of high and low frequency fusion are selected through the channel attention network to obtain the final fusion map. By simulating the nonlinear characteristics and selection characteristics of the human visual perception system in image fusion, the fused image is more in line with the human visual perception mechanism. Finally, we validate our fusion framework on public datasets of infrared and visible images, medical images and multi-focus images. The experimental results demonstrate the superiority of our fusion framework over state-of-arts in visual quality, objective fusion metrics and robustness.
H2O-Cloud: A Resource and Quality of Service-Aware Task Scheduling Framework for Warehouse-Scale Data Centers -- A Hierarchical Hybrid DRL (Deep Reinforcement Learning) based Approach
Venue: arXiv cs.DC 2020
Year: 2020

Cloud computing has attracted both end-users and Cloud Service Providers (CSPs) in recent years. Improving resource utilization rate (RUtR), such as CPU and memory usages on servers, while maintaining Quality-of-Service (QoS) is one key challenge faced by CSPs with warehouse-scale data centers. Prior works proposed various algorithms to reduce energy cost or to improve RUtR, which either lack the fine-grained task scheduling capabilities, or fail to take a comprehensive system model into consideration. This article presents H2O-Cloud, a Hierarchical and Hybrid Online task scheduling framework for warehouse-scale CSPs, to improve resource usage effectiveness while maintaining QoS. H2O-Cloud is highly scalable and considers comprehensive information such as various workload scenarios, cloud platform configurations, user request information and dynamic pricing model. The hierarchy and hybridity of the framework, combined with its deep reinforcement learning (DRL) engines, enable H2O-Cloud to efficiently start on-the-go scheduling and learning in an unpredictable environment without pre-training. Our experiments confirm the high efficiency of the proposed H2O-Cloud when compared to baseline approaches, in terms of energy and cost while maintaining QoS. Compared with a state-of-the-art DRL-based algorithm, H2O-Cloud achieves up to 201.17% energy cost efficiency improvement, 47.88% energy efficiency improvement and 551.76% reward rate improvement.
On metric regularity of Reed-Muller codes
Venue: arXiv cs.DM 2020
Year: 2020

In this work we study metric properties of the well-known family of binary Reed-Muller codes. Let $A$ be an arbitrary subset of the Boolean cube, and $\widehat{A}$ be the metric complement of $A$ -- the set of all vectors of the Boolean cube at the maximal possible distance from $A$. If the metric complement of $\widehat{A}$ coincides with $A$, then the set $A$ is called a {\it metrically regular set}. The problem of investigating metrically regular sets appeared when studying {\it bent functions}, which have important applications in cryptography and coding theory and are also one of the earliest examples of a metrically regular set. In this work we describe metric complements and establish the metric regularity of the codes $\mathcal{RM}(0,m)$ and $\mathcal{RM}(k,m)$ for $k \geqslant m-3$. Additionally, the metric regularity of the codes $\mathcal{RM}(1,5)$ and $\mathcal{RM}(2,6)$ is proved. Combined with previous results by Tokareva N. (2012) concerning duality of affine and bent functions, this establishes the metric regularity of most Reed-Muller codes with known covering radius. It is conjectured that all Reed-Muller codes are metrically regular.
Random CapsNet Forest Model for Imbalanced Malware Type Classification Task
Venue: arXiv cs.CR 2020
Year: 2020

Behavior of a malware varies with respect to malware types. Therefore,knowing type of a malware affects strategies of system protection softwares. Many malware type classification models empowered by machine and deep learning achieve superior accuracies to predict malware types.Machine learning based models need to do heavy feature engineering and feature engineering is dominantly effecting performance of models.On the other hand, deep learning based models require less feature engineering than machine learning based models. However, traditional deep learning architectures and components cause very complex and data sensitive models. Capsule network architecture minimizes this complexity and data sensitivity unlike classical convolutional neural network architectures. This paper proposes an ensemble capsule network model based on bootstrap aggregating technique. The proposed method are tested on two malware datasets, whose the-state-of-the-art results are well-known.
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
Venue: arXiv cs.CV 2020
Year: 2020

With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks. However, it remains unclear if the searched architecture can transfer across different types of tasks as manually designed ones did. This paper puts forward this problem, referred to as NAS in the wild, which explores the possibility of finding the optimal architecture in a proxy dataset and then deploying it to mostly unseen scenarios. We instantiate this setting using a currently popular algorithm named differentiable architecture search (DARTS), which often suffers unsatisfying performance while being transferred across different tasks. We argue that the accuracy drop originates from the formulation that uses a super-network for search but a sub-network for re-training. The different properties of these stages have resulted in a significant optimization gap, and consequently, the architectural parameters "over-fit" the super-network. To alleviate the gap, we present a progressive method that gradually increases the network depth during the search stage, which leads to the Progressive DARTS (P-DARTS) algorithm. With a reduced search cost (7 hours on a single GPU), P-DARTS achieves improved performance on both the proxy dataset (CIFAR10) and a few target problems (ImageNet classification, COCO detection and three ReID benchmarks). Our code is available at \url{https://github.com/chenxin061/pdarts}.
Learning an Interpretable Traffic Signal Control Policy
Venue: arXiv cs.LG 2020
Year: 2020

Signalized intersections are managed by controllers that assign right of way (green, yellow, and red lights) to non-conflicting directions. Optimizing the actuation policy of such controllers is expected to alleviate traffic congestion and its adverse impact. Given such a safety-critical domain, the affiliated actuation policy is required to be interpretable in a way that can be understood and regulated by a human. This paper presents and analyzes several on-line optimization techniques for tuning interpretable control functions. Although these techniques are defined in a general way, this paper assumes a specific class of interpretable control functions (polynomial functions) for analysis purposes. We show that such an interpretable policy function can be as effective as a deep neural network for approximating an optimized signal actuation policy. We present empirical evidence that supports the use of value-based reinforcement learning for on-line training of the control function. Specifically, we present and study three variants of the Deep Q-learning algorithm that allow the training of an interpretable policy function. Our Deep Regulatable Hardmax Q-learning variant is shown to be particularly effective in optimizing our interpretable actuation policy, resulting in up to 19.4% reduced vehicles delay compared to commonly deployed actuated signal controllers.
Unsupervised Domain Adversarial Self-Calibration for Electromyographic-based Gesture Recognition
Venue: arXiv cs.HC 2020
Year: 2020

Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.
Geometry-Aware Generation of Adversarial Point Clouds
Venue: arXiv cs.CV 2020
Year: 2020

Machine learning models have been shown to be vulnerable to adversarial examples. While most of the existing methods for adversarial attack and defense work on the 2D image domain, a few recent attempts have been made to extend them to 3D point cloud data. However, adversarial results obtained by these methods typically contain point outliers, which are both noticeable and easy to defend against using the simple techniques of outlier removal. Motivated by the different mechanisms by which humans perceive 2D images and 3D shapes, in this paper we propose the new design of \emph{geometry-aware objectives}, whose solutions favor (the discrete versions of) the desired surface properties of smoothness and fairness. To generate adversarial point clouds, we use a targeted attack misclassification loss that supports continuous pursuit of increasingly malicious signals. Regularizing the targeted attack loss with our proposed geometry-aware objectives results in our proposed method, Geometry-Aware Adversarial Attack ($GeoA^3$). The results of $GeoA^3$ tend to be more harmful, arguably harder to defend against, and of the key adversarial characterization of being imperceptible to humans. While the main focus of this paper is to learn to generate adversarial point clouds, we also present a simple but effective algorithm termed $Geo_{+}A^3$-IterNormPro, with Iterative Normal Projection (IterNorPro) that solves a new objective function $Geo_{+}A^3$, towards surface-level adversarial attacks via generation of adversarial point clouds. We quantitatively evaluate our methods on both synthetic and physical objects in terms of attack success rate and geometric regularity. For a qualitative evaluation, we conduct subjective studies by collecting human preferences from Amazon Mechanical Turk. Comparative results in comprehensive experiments confirm the advantages of our proposed methods.
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Venue: arXiv cs.LG 2020
Year: 2020

Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines. Coarsening is one such approach: it generates a pyramid of graphs whereby the one in the next level is a structural summary of the prior one. With a long history in scientific computing, many coarsening strategies were developed based on mathematically driven heuristics. Recently, resurgent interests exist in deep learning to design hierarchical methods learnable through differentiable parameterization. These approaches are paired with downstream tasks for supervised learning. In practice, however, supervised signals (e.g., labels) are scarce and are often laborious to obtain. In this work, we propose an unsupervised approach, coined OTCoarsening, with the use of optimal transport. Both the coarsening matrix and the transport cost matrix are parameterized, so that an optimal coarsening strategy can be learned and tailored for a given set of graphs. We demonstrate that the proposed approach produces meaningful coarse graphs and yields competitive performance compared with supervised methods for graph classification and regression.
Computing the Number of Equivalent Classes on $\mathcal{R}(s,n)/\mathcal{R}(k,n)$
Venue: arXiv cs.IT 2020
Year: 2020

Affine equivalent classes of Boolean functions have many applications in modern cryptography and circuit design. Previous publications have shown that affine equivalence on the entire space of Boolean functions can be computed up to 10 variables, but not on the quotient Boolean function space modulo functions of different degrees. Computing the number of equivalent classes of cosets of Reed-Muller code $\mathcal{R}(1,n)$ is equivalent to classifying Boolean functions modulo linear functions, which can be computed only when $n\leq 7$. Based on the linear representation of the affine group $\mathcal{AGL}(n,2)$ on $\mathcal{R}(s,n)/\mathcal{R}(k,n)$, we obtain a useful counting formula to compute the number of equivalent classes. Instead of computing the conjugate classes and representatives directly in $\mathcal{AGL}(n,2)$, we reduce the computation complexity by introducing an isomorphic permutation group $P_n$ and performing the computation in $P_n$. With the proposed algorithm, the number of equivalent classes of cosets of $R(1,n)$ can be computed up to 10 variables. Furthermore, the number of equivalent classes on $\mathcal{R}(s,n)/\mathcal{R}(k,n)$ can also be computed when $-1\leq k< s\leq n\leq 10$, which is a major improvement and advancement comparing to previous methods.