arXiv subjects

Yun Shen

Publications and source records attributed to Yun Shen.

At least 73 records · Page 4Linked to original sources

arXiv2021

Realization of broadband truly rainbow trapping in gradient-index heterostructures

Unidirectionally propagating waves (UPW) such as topologically protected edge modes and surface magnetoplasmons (SMPs) has been a research hotspot in the last decades. In the study of UPW, metals are usually treated as perfect electric conductors (PECs) which, in general, are the boundary conditions. However, it was reported that the transverse resonance condition induced by the PEC wall(s) may significantly narrow up the complete one-way propagation (COWP) band. In this paper, we propose two ways to achieve ultra-broadband one-way waveguide in terahertz regime. The first way is utilizing the epsilon negative (ENG) metamaterial (MM) and the other one is replacing the PEC boundary with perfect magnetic conductor (PMC) boundary. In both conditions, the total bandwidth of the COWP bands can be efficiently broadened by more than three times. Moreover, based on the ultra-broadband one-way configurations, gradient-index metamaterial-based one-way waveguides are proposed to achieve broadband truly rainbow trapping (TRT). By utilizing the finite element method, the realization of the broadband TRT without backward reflection is verified in gradient-index structures. Besides, giant electric field enhancement is observed in a PMC-based one-way structure with an ultra-subwavelength ($\approx 10^{-4} λ_0$, $λ_0$ is the wavelength in vaccum) terminal, and the amplitude of the electric field is enormously enhanced by five orders of magnitude. Our findings are beneficial for researches on broadband terahertz communication, energy harvesting and strong-field devices.

physics.optics↗

arXiv2021

Inference Attacks Against Graph Neural Networks

Graph is an important data representation ubiquitously existing in the real world. However, analyzing the graph data is computationally difficult due to its non-Euclidean nature. Graph embedding is a powerful tool to solve the graph analytics problem by transforming the graph data into low-dimensional vectors. These vectors could also be shared with third parties to gain additional insights of what is behind the data. While sharing graph embedding is intriguing, the associated privacy risks are unexplored. In this paper, we systematically investigate the information leakage of the graph embedding by mounting three inference attacks. First, we can successfully infer basic graph properties, such as the number of nodes, the number of edges, and graph density, of the target graph with up to 0.89 accuracy. Second, given a subgraph of interest and the graph embedding, we can determine with high confidence that whether the subgraph is contained in the target graph. For instance, we achieve 0.98 attack AUC on the DD dataset. Third, we propose a novel graph reconstruction attack that can reconstruct a graph that has similar graph structural statistics to the target graph. We further propose an effective defense mechanism based on graph embedding perturbation to mitigate the inference attacks without noticeable performance degradation for graph classification tasks. Our code is available at https://github.com/Zhangzhk0819/GNN-Embedding-Leaks.

cs.CR↗

arXiv2021

Machine-learning-enabled vectorial opto-magnetization orientation

Manipulation of light-induced magnetization has become a fundamentally hot topic with a potentially high impact for atom trapping, confocal and magnetic resonance microscopy, and data storage. The control of the magnetization orientation mainly relies on the direct methods composed of amplitude, phase and polarization modulations of the incident light under the tight focusing condition, leaving the achievement of arbitrary desirable three-dimensional (3D) magnetization orientation complicated, inflexible and inefficient. Here, we propose a facile approach called machine learning inverse design to achieve expected vectorial opto-magnetization orientation. This pathway is time-efficient and accurate to produce the demanded incident beam for arbitrary prescribed 3D magnetization orientation. It is highlighted that the machine learning method is not only applied for magnetization orientations, but also widely used in the control of magnetization structures.

physics.optics↗

arXiv2021

A Large-scale Temporal Measurement of Android Malicious Apps: Persistence, Migration, and Lessons Learned

We study the temporal dynamics of potentially harmful apps (PHAs) on Android by leveraging 8.8M daily on-device detections collected among 11.7M customers of a popular mobile security product between 2019 and 2020. We show that the current security model of Android, which limits security products to run as regular apps and prevents them from automatically removing malicious apps opens a significant window of opportunity for attackers. Such apps warn users about the newly discovered threats, but users do not promptly act on this information, allowing PHAs to persist on their device for an average of 24 days after they are detected. We also find that while app markets remove PHAs after these become known, there is a significant delay between when PHAs are identified and when they are removed: PHAs persist on Google Play for 77 days on average and 34 days on third party marketplaces. Finally, we find evidence of PHAs migrating to other marketplaces after being removed on the original one. This paper provides an unprecedented view of the Android PHA landscape, showing that current defenses against PHAs on Android are not as effective as commonly thought, and identifying multiple research directions that the security community should pursue, from orchestrating more effective PHA takedowns to devising better alerts for mobile security products.

cs.CR↗

arXiv2021

Broadband energy squeezing and tunneling based on unidirectional modes

Energy squeezing attracts many attentions for its potential applications in electromagnetic (EM) energy harvesting and optical communication. However, due to the Fabry-Perot resonance, only the EM waves with discrete frequencies can be squeezed and, as far as we know, in the previous energy-squeezing devices, stringent requirements of the materials or the geometrical shape are needed. We note that the structures filled with epsilon-near-zero (ENZ) mediums as reported in some works can squeeze and tunnel EM waves at frequencies (e.g. plasma frequency). However, the group velocity is usually near zero which means few EM information travel through the structures. In this paper, low-loss energy squeezing and tunneling (EST) based on unidirectional modes were demonstrated in YIG-based one-way waveguides at microwave frequencies. According to our theoretical analysis and the simulations using finite element method, broadband EST was achieved and the EM EST was observed even for extremely bended structures. Besides, similar EM EST was achieved in a realistic three-dimensional remanence-based one-way waveguide as well. The unidirectional modes-based EST paving the way to ultra-subwavelength EM focusing, enhanced nonlinear optics, and designing numerous functional devices in integrated optical circuits such as phase modulator.

physics.app-ph↗

arXiv2021

Marked for Disruption: Tracing the Evolution of Malware Delivery Operations Targeted for Takedown

The malware and botnet phenomenon is among the most significant threats to cybersecurity today. Consequently, law enforcement agencies, security companies, and researchers are constantly seeking to disrupt these malicious operations through so-called takedown counter-operations. Unfortunately, the success of these takedowns is mixed. Furthermore, very little is understood as to how botnets and malware delivery operations respond to takedown attempts. We present a comprehensive study of three malware delivery operations that were targeted for takedown in 2015-16 using global download metadata provided by a major security company. In summary, we found that: (1) Distributed delivery architectures were commonly used, indicating the need for better security hygiene and coordination by the (ab)used service providers. (2) A minority of malware binaries were responsible for the majority of download activity, suggesting that detecting these "super binaries" would yield the most benefit to the security community. (3) The malware operations exhibited displacing and defiant behaviours following their respective takedown attempts. We argue that these "predictable" behaviours could be factored into future takedown strategies. (4) The malware operations also exhibited previously undocumented behaviours, such as Dridex dropping competing brands of malware, or Dorkbot and Upatre heavily relying on upstream dropper malware. These "unpredictable" behaviours indicate the need for researchers to use better threat-monitoring techniques.

cs.CR↗

arXiv2021

Gradient Boosting Survival Tree with Applications in Credit Scoring

Credit scoring plays a vital role in the field of consumer finance. Survival analysis provides an advanced solution to the credit-scoring problem by quantifying the probability of survival time. In order to deal with highly heterogeneous industrial data collected in Chinese market of consumer finance, we propose a nonparametric ensemble tree model called gradient boosting survival tree (GBST) that extends the survival tree models with a gradient boosting algorithm. The survival tree ensemble is learned by minimizing the negative log-likelihood in an additive manner. The proposed model optimizes the survival probability simultaneously for each time period, which can reduce the overall error significantly. Finally, as a test of the applicability, we apply the GBST model to quantify the credit risk with large-scale real market datasets. The results show that the GBST model outperforms the existing survival models measured by the concordance index (C-index), Kolmogorov-Smirnov (KS) index, as well as by the area under the receiver operating characteristic curve (AUC) of each time period.

cs.LG↗

arXiv2021

ANDRUSPEX : Leveraging Graph Representation Learning to Predict Harmful App Installations on Mobile Devices

Android's security model severely limits the capabilities of anti-malware software. Unlike commodity anti-malware solutions on desktop systems, their Android counterparts run as sandboxed applications without root privileges and are limited by Android's permission system. As such, PHAs on Android are usually willingly installed by victims, as they come disguised as useful applications with hidden malicious functionality, and are encountered on mobile app stores as suggestions based on the apps that a user previously installed. Users with similar interests and app installation history are likely to be exposed and to decide to install the same PHA. This observation gives us the opportunity to develop predictive approaches that can warn the user about which PHAs they will encounter and potentially be tempted to install in the near future. These approaches could then be used to complement commodity anti-malware solutions, which are focused on post-fact detection, closing the window of opportunity that existing solutions suffer from. In this paper we develop Andruspex, a system based on graph representation learning, allowing us to learn latent relationships between user devices and PHAs and leverage them for prediction. We test Andruspex on a real world dataset of PHA installations collected by a security company, and show that our approach achieves very high prediction results (up to 0.994 TPR at 0.0001 FPR), while at the same time outperforming alternative baseline methods. We also demonstrate that Andruspex is robust and its runtime performance is acceptable for a real world deployment.

cs.CR↗

arXiv2021

Understanding Worldwide Private Information Collection on Android

Mobile phones enable the collection of a wealth of private information, from unique identifiers (e.g., email addresses), to a user's location, to their text messages. This information can be harvested by apps and sent to third parties, which can use it for a variety of purposes. In this paper we perform the largest study of private information collection (PIC) on Android to date. Leveraging an anonymized dataset collected from the customers of a popular mobile security product, we analyze the flows of sensitive information generated by 2.1M unique apps installed by 17.3M users over a period of 21 months between 2018 and 2019. We find that 87.2% of all devices send private information to at least five different domains, and that actors active in different regions (e.g., Asia compared to Europe) are interested in collecting different types of information. The United States (62% of the total) and China (7% of total flows) are the countries that collect most private information. Our findings raise issues regarding data regulation, and would encourage policymakers to further regulate how private information is used by and shared among the companies and how accountability can be truly guaranteed.

cs.CR↗

arXiv2021

Node-Level Membership Inference Attacks Against Graph Neural Networks

Many real-world data comes in the form of graphs, such as social networks and protein structure. To fully utilize the information contained in graph data, a new family of machine learning (ML) models, namely graph neural networks (GNNs), has been introduced. Previous studies have shown that machine learning models are vulnerable to privacy attacks. However, most of the current efforts concentrate on ML models trained on data from the Euclidean space, like images and texts. On the other hand, privacy risks stemming from GNNs remain largely unstudied. In this paper, we fill the gap by performing the first comprehensive analysis of node-level membership inference attacks against GNNs. We systematically define the threat models and propose three node-level membership inference attacks based on an adversary's background knowledge. Our evaluation on three GNN structures and four benchmark datasets shows that GNNs are vulnerable to node-level membership inference even when the adversary has minimal background knowledge. Besides, we show that graph density and feature similarity have a major impact on the attack's success. We further investigate two defense mechanisms and the empirical results indicate that these defenses can reduce the attack performance but with moderate utility loss.

cs.CR↗

arXiv2021

Slow wave and truly rainbow trapping in one-way terahertz waveguide

Slow or even stop electromagnetic (EM) waves attract researchers' attentions for its potential applications in energy storage, optical buffer and nonlinearity enhancement. However, in most cases of the EM waves trapping, the EM waves are not truly trapped due to the existence of reflection. In this paper, a novel metal-semiconductor-semiconductor-metal (MSSM) structure, and a novel truly rainbow trapping in a tapered MSSM model at terahertz frequencies are demonstrated by theoretical analysis and numerical simulations. More importantly, functional devices such as optical buffer, optical switch and optical filter are achieved in our designed MSSM structure based on truly rainbow trapping theory. Owing to the property of one-way propagation, these new types of optical devices can be high-performance and are expected to be used in integrated optical circuits.

physics.optics↗

arXiv2020

UAVs as a Service: Boosting Edge Intelligence for Air-Ground Integrated Networks

The air-ground integrated network is a key component of future sixth generation (6G) networks to support seamless and near-instant super-connectivity. There is a pressing need to intelligently provision various services in 6G networks, which however is challenging. To meet this need, in this article, we propose a novel architecture called UaaS (UAVs as a Service) for the air-ground integrated network, featuring UAV as a key enabler to boost edge intelligence with the help of machine learning (ML) techniques. We envision that the proposed UaaS architecture could intelligently provision wireless communication service, edge computing service, and edge caching service by a network of UAVs, making full use of UAVs' flexible deployment and diverse ML techniques. We also conduct a case study where UAVs participate in the model training of distributed ML among multiple terrestrial users, whose result shows that the model training is efficient with a negligible energy consumption of UAVs, compared to the flight energy consumption. Finally, we discuss the challenges and open research issues in the UaaS.

cs.NI↗

arXiv2019

Slow light,double directional rainbow trapping and releasing at terahertz frequencies

Slow light and rainbow trapping attract many attentions in last twenty years, and in most of the previous works, the researchers achieve the slow light and rainbow trapping with complicate configurations or techniques, for example, metamaterial techniques. In this paper, we propose a simple waveguide consisted with semiconductor and dielectric layers, and we find that the slow-light peaks appear in the dispersion curves. Besides, the cutoff frequency with $v_g = 0$ ($v_g$ is the group velocity) in the slow-light peaks is depending on the thicknesses of the semiconductor and dielectric. Then, we design a tapered, horizontally symmetric structure. By using software COMSOL and finite difference time domain method as well, we achieve double directional rainbow trapping and more importantly, releasing the rainbow in such structure. This approach is promising for optical isolator, optical buffer, optical switch and other optical functional devices in miniaturization optical integrated circuit.

physics.optics↗

arXiv2019

ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks

Despite the fact that cyberattacks are constantly growing in complexity, the research community still lacks effective tools to easily monitor and understand them. In particular, there is a need for techniques that are able to not only track how prominently certain malicious actions, such as the exploitation of specific vulnerabilities, are exploited in the wild, but also (and more importantly) how these malicious actions factor in as attack steps in more complex cyberattacks. In this paper we present ATTACK2VEC, a system that uses temporal word embeddings to model how attack steps are exploited in the wild, and track how they evolve. We test ATTACK2VEC on a dataset of billions of security events collected from the customers of a commercial Intrusion Prevention System over a period of two years, and show that our approach is effective in monitoring the emergence of new attack strategies in the wild and in flagging which attack steps are often used together by attackers (e.g., vulnerabilities that are frequently exploited together). ATTACK2VEC provides a useful tool for researchers and practitioners to better understand cyberattacks and their evolution, and use this knowledge to improve situational awareness and develop proactive defenses.

cs.CR↗

arXiv2019

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task.

cs.CR↗

arXiv2019

Collaborative and Privacy-Preserving Machine Teaching via Consensus Optimization

In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. We focus on consensus super teaching. It aims at organizing distributed teachers to jointly select a compact while informative training subset from data hosted by the teachers to make a learner learn better. The challenges arise from three perspectives. First, the state-of-the-art pool-based super teaching method applies mixed-integer non-linear programming (MINLP) which does not scale well to very large data sets. Second, it is desirable to restrict data access of the teachers to only their own data during the collaboration stage to mitigate privacy leaks. Finally, the teaching collaboration should be communication-efficient since large communication overheads can cause synchronization delays between teachers. To address these challenges, we formulate collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk. We theoretically demonstrate the necessity of collaboration between teachers for improving the learner's learning. Furthermore, we show that the proposed method enjoys a similar property as the Oracle property of adaptive Lasso. The empirical study illustrates that our teaching method can deliver significantly more accurate teaching results with high speed, while the non-collaborative MINLP-based super teaching becomes prohibitively expensive to compute.

cs.LG↗

arXiv2019

A Fenchel-Moreau-Rockafellar type theorem on the Kantorovich-Wasserstein space with Applications in Partially Observable Markov Decision Processes

By using the fact that the space of all probability measures with finite support can be somehow completed in two different fashions, one generating the Arens-Eells space and another generating the Kantorovich-Wasserstein (Wasserstein-1) space, and by exploiting the duality relationship between the Arens-Eells space with the space of Lipschitz functions, we provide a dual representation of Fenchel-Moreau-Rockafellar type for proper convex functionals on Wasserstein-1. We retrieve dual transportation inequalities as a Corollary and we provide examples where the theorem can be used to easily prove dual expressions like the celebrated Donsker-Varadhan variational formula. Finally our result allows to write convex functions as the supremum over all linear functions that are generated by roots of its conjugate dual, something that we apply to the field of Partially observable Markov decision processes (POMDPs) to approximate the value function of a given POMDP by iterating level sets. This extends the method used in Smallwood 1973 for finite state spaces to the case were the state space is a Polish metric space.

math.PR↗

arXiv2015

On a conjecture for the signless Laplacian spectral radius of cacti with given matching number

A connected graph $G$ is a cactus if any two of its cycles have at most one common vertex. Let $\ell_n^m$ be the set of cacti on $n$ vertices with matching number $m.$ S.C. Li and M.J. Zhang determined the unique graph with the maximum signless Laplacian spectral radius among all cacti in $\ell_n^m$ with $n=2m$. In this paper, we characterize the case $n\geq 2m+1$. This confirms the conjecture of Li and Zhang(S.C. Li, M.J. Zhang, On the signless Laplacian index of cacti with a given number of pendant vetices, Linear Algebra Appl. 436, 2012, 4400--4411). Further, we characterize the unique graph with the maximum signless Laplacian spectral radius among all cacti on $n$ vertices.

math.CO↗