arXiv subjects

Vyas Sekar

Publications and source records attributed to Vyas Sekar.

At least 37 records · Page 2Linked to original sources

arXiv2021

A Roadmap for Enabling a Future-Proof In-Network Computing Data Plane Ecosystem

As the vision of in-network computing becomes more mature, we see two parallel evolutionary trends. First, we see the evolution of richer, more demanding applications that require capabilities beyond programmable switching ASICs. Second, we see the evolution of diverse data plane technologies with many other future capabilities on the horizon. While some point solutions exist to tackle the intersection of these trends, we see several ecosystem-level disconnects today; e.g., the need to refactor applications for new data planes, lack of systematic guidelines to inform the development of future data plane capabilities, and lack of holistic runtime frameworks for network operators. In this paper, we use a simple-yet-instructive emerging application-data plane combination to highlight these disconnects. Drawing on these lessons, we sketch a high-level roadmap and guidelines for the community to tackle these to create a more thriving "future-proof" data plane ecosystem.

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arXiv2021

Why Spectral Normalization Stabilizes GANs: Analysis and Improvements

Spectral normalization (SN) is a widely-used technique for improving the stability and sample quality of Generative Adversarial Networks (GANs). However, there is currently limited understanding of why SN is effective. In this work, we show that SN controls two important failure modes of GAN training: exploding and vanishing gradients. Our proofs illustrate a (perhaps unintentional) connection with the successful LeCun initialization. This connection helps to explain why the most popular implementation of SN for GANs requires no hyper-parameter tuning, whereas stricter implementations of SN have poor empirical performance out-of-the-box. Unlike LeCun initialization which only controls gradient vanishing at the beginning of training, SN preserves this property throughout training. Building on this theoretical understanding, we propose a new spectral normalization technique: Bidirectional Scaled Spectral Normalization (BSSN), which incorporates insights from later improvements to LeCun initialization: Xavier initialization and Kaiming initialization. Theoretically, we show that BSSN gives better gradient control than SN. Empirically, we demonstrate that it outperforms SN in sample quality and training stability on several benchmark datasets.

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arXiv2021

Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions

Generative adversarial networks (GANs) are often billed as "universal distribution learners", but precisely what distributions they can represent and learn is still an open question. Heavy-tailed distributions are prevalent in many different domains such as financial risk-assessment, physics, and epidemiology. We observe that existing GAN architectures do a poor job of matching the asymptotic behavior of heavy-tailed distributions, a problem that we show stems from their construction. Additionally, when faced with the infinite moments and large distances between outlier points that are characteristic of heavy-tailed distributions, common loss functions produce unstable or near-zero gradients. We address these problems with the Pareto GAN. A Pareto GAN leverages extreme value theory and the functional properties of neural networks to learn a distribution that matches the asymptotic behavior of the marginal distributions of the features. We identify issues with standard loss functions and propose the use of alternative metric spaces that enable stable and efficient learning. Finally, we evaluate our proposed approach on a variety of heavy-tailed datasets.

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arXiv2021

Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

Limited data access is a longstanding barrier to data-driven research and development in the networked systems community. In this work, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge. As a specific target, our focus in this paper is on time series datasets with metadata (e.g., packet loss rate measurements with corresponding ISPs). We identify key challenges of existing GAN approaches for such workloads with respect to fidelity (e.g., long-term dependencies, complex multidimensional relationships, mode collapse) and privacy (i.e., existing guarantees are poorly understood and can sacrifice fidelity). To improve fidelity, we design a custom workflow called DoppelGANger (DG) and demonstrate that across diverse real-world datasets (e.g., bandwidth measurements, cluster requests, web sessions) and use cases (e.g., structural characterization, predictive modeling, algorithm comparison), DG achieves up to 43% better fidelity than baseline models. Although we do not resolve the privacy problem in this work, we identify fundamental challenges with both classical notions of privacy and recent advances to improve the privacy properties of GANs, and suggest a potential roadmap for addressing these challenges. By shedding light on the promise and challenges, we hope our work can rekindle the conversation on workflows for data sharing.

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arXiv2020

Sketchy With a Chance of Adoption: Can Sketch-Based Telemetry Be Ready for Prime Time?

Sketching algorithms or sketches have emerged as a promising alternative to the traditional packet sampling-based network telemetry solutions. At a high level, they are attractive because of their high resource efficiency and accuracy guarantees. While there have been significant recent advances in various aspects of sketching for networking tasks, many fundamental challenges remain unsolved that are likely stumbling blocks for adoption. Our contribution in this paper is in identifying and formulating these research challenges across the ecosystem encompassing network operators, platform vendors/developers, and algorithm designers. We hope that these serve as a necessary fillip for the community to enable the broader adoption of sketch-based telemetry.

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arXiv2020

Unleashing In-network Computing on Scientific Workloads

Many recent efforts have shown that in-network computing can benefit various datacenter applications. In this paper, we explore a relatively less-explored domain which we argue can benefit from in-network computing: scientific workloads in high-performance computing. By analyzing canonical examples of HPC applications, we observe unique opportunities and challenges for exploiting in-network computing to accelerate scientific workloads. In particular, we find that the dynamic and demanding nature of scientific workloads is the major obstacle to the adoption of in-network approaches which are mostly open-loop and lack runtime feedback. In this paper, we present NSinC (Network-accelerated ScIeNtific Computing), an architecture for fully unleashing the potential benefits of in-network computing for scientific workloads by providing closed-loop runtime feedback to in-network acceleration services. We outline key challenges in realizing this vision and a preliminary design to enable acceleration for scientific applications.

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arXiv2020

Fighting Fire with Light: A Case for Defending DDoS Attacks Using the Optical Layer

The DDoS attack landscape is growing at an unprecedented pace. Inspired by the recent advances in optical networking, we make a case for optical layer-aware DDoS defense (O-LAD) in this paper. Our approach leverages the optical layer to isolate attack traffic rapidly via dynamic reconfiguration of (backup) wavelengths using ROADMs---bridging the gap between (a) evolution of the DDoS attack landscape and (b) innovations in the optical layer (e.g., reconfigurable optics). We show that the physical separation of traffic profiles allows finer-grained handling of suspicious flows and offers better performance for benign traffic in the face of an attack. We present preliminary results modeling throughput and latency for legitimate flows while scaling the strength of attacks. We also identify a number of open problems for the security, optical, and systems communities: modeling diverse DDoS attacks (e.g., fixed vs. variable rate, detectable vs. undetectable), building a full-fledged defense system with optical advancements (e.g., OpenConfig), and optical layer-aware defenses for a broader class of attacks (e.g., network reconnaissance).

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arXiv2019

Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches

Communication and privacy are two critical concerns in distributed learning. Many existing works treat these concerns separately. In this work, we argue that a natural connection exists between methods for communication reduction and privacy preservation in the context of distributed machine learning. In particular, we prove that Count Sketch, a simple method for data stream summarization, has inherent differential privacy properties. Using these derived privacy guarantees, we propose a novel sketch-based framework (DiffSketch) for distributed learning, where we compress the transmitted messages via sketches to simultaneously achieve communication efficiency and provable privacy benefits. Our evaluation demonstrates that DiffSketch can provide strong differential privacy guarantees (e.g., $\varepsilon$= 1) and reduce communication by 20-50x with only marginal decreases in accuracy. Compared to baselines that treat privacy and communication separately, DiffSketch improves absolute test accuracy by 5%-50% while offering the same privacy guarantees and communication compression.

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arXiv2019

Enhancing the Privacy of Federated Learning with Sketching

In response to growing concerns about user privacy, federated learning has emerged as a promising tool to train statistical models over networks of devices while keeping data localized. Federated learning methods run training tasks directly on user devices and do not share the raw user data with third parties. However, current methods still share model updates, which may contain private information (e.g., one's weight and height), during the training process. Existing efforts that aim to improve the privacy of federated learning make compromises in one or more of the following key areas: performance (particularly communication cost), accuracy, or privacy. To better optimize these trade-offs, we propose that \textit{sketching algorithms} have a unique advantage in that they can provide both privacy and performance benefits while maintaining accuracy. We evaluate the feasibility of sketching-based federated learning with a prototype on three representative learning models. Our initial findings show that it is possible to provide strong privacy guarantees for federated learning without sacrificing performance or accuracy. Our work highlights that there exists a fundamental connection between privacy and communication in distributed settings, and suggests important open problems surrounding the theoretical understanding, methodology, and system design of practical, private federated learning.

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arXiv2019

Vulnerabilities of Electric Vehicle Battery Packs to Cyberattacks

Electric Vehicles (EVs), like all modern vehicles, are entirely controlled by electronic devices embedded within networks that are exposed to the threat of cyberattacks. Cyber vulnerabilities are magnified with EVs due to unique risks associated with EV battery packs. Current batteries have well-known issues with specific energy, cost and fire-related safety risks. In this study, we develop a systematic framework to assess the impact of cyberattacks on EVs. While the current focus of automotive cyberattacks is on short-term physical safety, it is crucial to consider long-term cyberattacks that aim to cause financial losses through accrued impact, especially in the context of EVs. Faulty components of battery management systems such as a compromised voltage regulator could lead to cyberattacks that can overdischarge or overcharge the battery. Overdischarge could lead to failures such as internal shorts in the timescale of minutes through cyberattacks that compromise energy-intensive EV subsystems like auxiliary components. Attacks that overcharge the pack could shorten the lifetime of a new battery pack to less than a year. Further, such attacks also pose physical safety risks via the triggering of thermal (fire) events. Attacks on auxiliary components lead to battery drain, which could be up to 20% of the state-of-charge per hour. Lastly, we develop a heuristic for the stealthiness of a cyberattack to augment traditional threat models. The methodology presented here will help in building the foundational principles of electric vehicle cybersecurity: a nascent but critical topic in the coming years.

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arXiv2019

Practical Verifiable In-network Filtering for DDoS defense

In light of ever-increasing scale and sophistication of modern DDoS attacks, it is time to revisit in-network filtering or the idea of empowering DDoS victims to install in-network traffic filters in the upstream transit networks. Recent proposals show that filtering DDoS traffic at a handful of large transit networks can handle volumetric DDoS attacks effectively. However, the innetwork filtering primitive can also be misused. Transit networks can use the in-network filtering service as an excuse for any arbitrary packet drops made for their own benefit. For example, transit networks may intentionally execute filtering services poorly or unfairly to discriminate their competing neighbor ASes while claiming that they drop packets for the sake of DDoS defense. We argue that it is due to the lack of verifiable filtering - i.e., no one can check if a transit network executes the filter rules correctly as requested by the DDoS victims. To make in-network filtering a more robust defense primitive, in this paper, we propose a verifiable in-network filtering, called VIF, that exploits emerging hardware-based trusted execution environments (TEEs) and offers filtering verifiability to DDoS victims and neighbor ASes. Our proof of concept demonstrates that a VIF filter implementation on commodity servers with TEE support can handle traffic at line rate (e.g., 10 Gb/s) and execute up to 3,000 filter rules. We show that VIF can easily scale to handle larger traffic volume (e.g., 500 Gb/s) and more complex filtering operations (e.g., 150,000 filter rules) by parallelizing the TEE-based filters. As a practical deployment model, we suggest that Internet exchange points (IXPs) are the ideal candidates for the early adopters of our verifiable filters due to their central locations and flexible software-defined architecture.

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arXiv2018

Oh, What a Fragile Web We Weave: Third-party Service Dependencies In Modern Webservices and Implications

The recent October 2016 DDoS attack on Dyn served as a wakeup call to the security community as many popular and independent webservices (e.g., Twitter, Spotify) were impacted. This incident raises a larger question on the fragility of modern webservices due to their dependence on third-party services. In this paper, we characterize the dependencies of popular webservices on third party services and how these can lead to DoS, RoQ attacks, and reduction in security posture. In particular, we focus on three critical infrastructure services: DNS, CDNs, and certificate authorities (CAs). We analyze both direct relationships (e.g., Twitter uses Dyn) and indirect dependencies (e.g., Netflix uses Symantec as OCSP and Symantec, in turn, uses Verisign for DNS). Our key findings are: (1) 73.14% of the top 100,000 popular services are vulnerable to reduction in availabil- ity due to potential attacks on third-party DNS, CDN, CA services that they exclusively rely on; (2) the use of third-party services is concentrated, so that if the top-10 providers of CDN, DNS and OCSP services go down, they can potentially impact 25%-46% of the top 100K most popular web services; (3) transitive depen- dencies significantly increase the set of webservices that exclusively depend on popular CDN and DNS service providers, in some cases by ten times (4) targeting even less popular webservices can potentially cause signifi- cant collateral damage, affecting upto 20% of the top- 100K webservices due to their shared dependencies. Based on our findings, we present a number of key implications and guidelines to guard against such Internet- scale incidents in the future.

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arXiv2016

Shedding Light on the Adoption of Let's Encrypt

Let's Encrypt is a new entrant in the Certificate Authority ecosystem that offers free and automated certificate signing. It is visionary in its commitment to Certificate Transparency. In this paper, we shed light on the adoption patterns of Let's Encrypt "in the wild" and inform the future design and deployment of this exciting development in the security landscape. We analyze acquisition patterns of certificates as well as their usage and deployment trends in the real world. To this end, we analyze data from Certificate Transparency Logs containing records of more then 18 million certificates. We also leverage other sources like Censys, Alexa's historic records, Geolocation databases, and VirusTotal. We also perform active HTTPS measurements on the domains owning Let's Encrypt certificates. Our analysis of certificate acquisition shows that (1) the impact of Let's Encrypt is particularly visible in Western Europe; (2) Let's Encrypt has the potential to democratize HTTPS adoption in countries that are recent entrants to Internet adoption; (3) there is anecdotal evidence of popular domains quitting their previously untrustworthy or expensive CAs in order to transition to Let's Encrypt; and (4) there is a "heavy tailed" behavior where a small number of domains acquire a large number of certificates. With respect to usage, we find that: (1) only 54% of domains actually use the Let's Encrypt certificates they have procured; (2) there are many non-trivial incidents of server misconfigurations; and (3) there is early evidence of use of Let's Encrypt certificates for typosquatting and for malware-laden sites.

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arXiv2016

On the Efficiency and Fairness of Multiplayer HTTP-based Adaptive Video Streaming

User-perceived quality-of-experience (QoE) is critical in internet video delivery systems. Extensive prior work has studied the design of client-side bitrate adaptation algorithms to maximize single-player QoE. However, multiplayer QoE fairness becomes critical as the growth of video traffic makes it more likely that multiple players share a bottleneck in the network. Despite several recent proposals, there is still a series of open questions. In this paper, we bring the problem space to light from a control theory perspective by formalizing the multiplayer QoE fairness problem and addressing two key questions in the broader problem space. First, we derive the sufficient conditions of convergence to steady state QoE fairness under TCP-based bandwidth sharing scheme. Based on the insight from this analysis that in-network active bandwidth allocation is needed, we propose a non-linear MPC-based, router-assisted bandwidth allocation algorithm that regards each player as closed-loop systems. We use trace-driven simulation to show the improvement over existing approaches. We identify several research directions enabled by the control theoretic modeling and envision that control theory can play an important role on guiding real system design in adaptive video streaming.

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arXiv2016

NetMemex: Providing Full-Fidelity Traffic Archival

NetMemex explores efficient network traffic archival without any loss of information. Unlike NetFlow-like aggregation, NetMemex allows retrieving the entire packet data including full payload, which makes it useful in forensic analysis, networked and distributed system research, and network administration. Different from packet trace dumps, NetMemex performs sophisticated data compression for small storage space use and optimizes the data layout for fast query processing. NetMemex takes advantage of high-speed random access of flash drives and inexpensive storage space of hard disk drives. These efforts lead to a cost-effective yet high-performance full traffic archival system. We demonstrate that NetMemex can record full-fidelity traffic at near-Gbps rates using a single commodity machine, handling common queries at up to 90.1 K queries/second, at a low storage cost comparable to conventional hard disk-only traffic archival solutions.

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arXiv2015

A New Approach to DDoS Defense using SDN and NFV

Networks today rely on expensive and proprietary hard- ware appliances, which are deployed at fixed locations, for DDoS defense. This introduces key limitations with respect to flexibility (e.g., complex routing to get traffic to these "chokepoints") and elasticity in handling changing attack patterns. We observe an opportunity to ad- dress these limitations using new networking paradigms such as software-defined networking (SDN) and network functions virtualization (NFV). Based on this observation, we design and implement of Bohatei, an elastic and flexible DDoS defense system. In designing Bohatei, we address key challenges of scalability, responsive- ness, and adversary-resilience. We have implemented defenses for several well-known DDoS attacks in Bohatei. Our evaluations show that Bohatei is scalable (handling 500 Gbps attacks), responsive (mitigating attacks within one minute), and resilient to dynamic adversaries.

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arXiv2015

Analyzing TCP Throughput Stability and Predictability with Implications for Adaptive Video Streaming

Recent work suggests that TCP throughput stability and predictability within a video viewing session can inform the design of better video bitrate adaptation algorithms. Despite a rich tradition of Internet measurement, however, our understanding of throughput stability and predictability is quite limited. To bridge this gap, we present a measurement study of throughput stability using a large-scale dataset from a video service provider. Drawing on this analysis, we propose a simple-but-effective prediction mechanism based on a hidden Markov model and demonstrate that it outperforms other approaches. We also show the practical implications in improving the user experience of adaptive video streaming.

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arXiv2015

Scalable Testing of Context-Dependent Policies over Stateful Data Planes with Armstrong

Network operators today spend significant manual effort in ensuring and checking that the network meets their intended policies. While recent work in network verification has made giant strides to reduce this effort, they focus on simple reachability properties and cannot handle context-dependent policies (e.g., how many connections has a host spawned) that operators realize using stateful network functions (NFs). Together, these introduce new expressiveness and scalability challenges that fall outside the scope of existing network verification mechanisms. To address these challenges, we present Armstrong, a system that enables operators to test if network with stateful data plane elements correctly implements a given context-dependent policy. Our design makes three key contributions to address expressiveness and scalability: (1) An abstract I/O unit for modeling network I/O that encodes policy-relevant context information; (2) A practical representation of complex NFs via an ensemble of finite state machines abstraction; and (3) A scalable application of symbolic execution to tackle state space explosion. We demonstrate that Armstrong is several orders of magnitude faster than existing mechanisms.

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