Geng Li, Yichen Qian, Y. Richard Yang

Abstract

Max-min fair is widely used in network traffic engineering to allocate available resources among different traffic transfers. Recently, as data replication technique developed, increasing systems enforce multi-source transmission to maximize network utilization. However, existing TE approaches fail to deal with multi-source transfers because the optimization becomes a joint problem of bandwidth allocation as well as flow assignment among different sources. In this paper, we present a novel allocation approach for multi-source transfers to achieve global max-min fairness. The joint bandwidth allocation and flow assignment optimization problem poses a major challenge due to nonlinearity and multiple objectives. We cope with this by deriving a novel transformation with simple equivalent canonical linear programming to achieve global optimality efficiently. We conduct data-driven simulations, showing that our approach is more max-min fair than other single-source and multi-source allocation approaches, meanwhile it outperforms others with substantial gains in terms of network throughput and transfer completion time.

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Luca Melis, Apostolos Pyrgelis, Emiliano De Cristofaro

Abstract

Collaborative predictive blacklisting (CPB) allows to forecast future attack sources based on logs and alerts contributed by multiple organizations. Unfortunately, however, research on CPB has only focused on increasing the number of predicted attacks but has not considered the impact on false positives and false negatives. Moreover, sharing alerts is often hindered by confidentiality, trust, and liability issues, which motivates the need for privacy-preserving approaches to the problem. In this paper, we present a measurement study of state-of-the-art CPB techniques, aiming to shed light on the actual impact of collaboration. To this end, we reproduce and measure two systems: a non privacy-friendly one that uses a trusted coordinating party with access to all alerts [12] and a peer-to-peer one using privacy-preserving data sharing [8]. We show that, while collaboration boosts the number of predicted attacks, it also yields high false positives, ultimately leading to poor accuracy. This motivates us to present a hybrid approach, using a semi-trusted central entity, aiming to increase utility from collaboration while, at the same time, limiting information disclosure and false positives. This leads to a better trade-off of true and false positive rates, while at the same time addressing privacy concerns.

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Taeho Lee, Christos Pappas, Adrian Perrig

Abstract

Internet users today have few solutions to cover a large space of diverse privacy requirements. We introduce the concept of privacy domains, which provide flexibility in expressing users’ privacy requirements. Then, we propose three privacy services that construct meaningful privacy domains and can be offered by ISPs. Furthermore, we illustrate that these services introduce little overhead for communication sessions and that they come with a low deployment barrier for ISPs.

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Mingda Li, Cristian Lumezanu, Bo Zong, Haifeng Chen

Abstract

We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet. In contrast, DIP computes low-dimensional representations of nodes that preserve structural properties and non-linear relationships across multiple, heterogeneous sources of structural information, such as IP, routing, and distance information. Using a large real-world data set, we show that DIP learns representations that preserve the real-world clustering of the associated nodes and predicts distance between them more than 30% better than a meanbased approach. Furthermore, DIP accurately imputes hop count distance to unknown hosts (i.e., not used in training) given only their IP addresses and routable prefixes. Our framework is extensible to new data sources and applicable to a wide range of problems in network monitoring and security

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Arthur Selle Jacobs, Ricardo José Pfitscher , Ronaldo Alves Ferreira, Lisandro Zambenedetti Granville

Abstract

Recent advances in artificial intelligence (AI) offer an opportunity for the adoption of self-driving networks. However, network operators or home-network users still do not have the right tools to exploit these new advancements in AI, since they have to rely on low-level languages to specify network policies. Intent-based networking (IBN) allows operators to specify high-level policies that dictate how the network should behave without worrying how they are translated into configuration commands in the network devices. However, the existing research proposals for IBN fail to exploit the knowledge and feedback from the network operator to validate or improve the translation of intents. In this paper, we introduce a novel intent-refinement process that uses machine learning and feedback from the operator to translate the operator’s utterances into network configurations. Our refinement process uses a sequence-to-sequence learning model to extract intents from natural language and the feedback from the operator to improve learning. The key insight of our process is an intermediate representation that resembles natural language that is suitable to collect feedback from the operator but is structured enough to facilitate precise translations. Our prototype interacts with a network operator using natural language and translates the operator input to the intermediate representation before translating to SDN rules. Our experimental results show that our process achieves a correlation coefficient squared (i.e., R-squared of 0.99 for a dataset with 5000 entries and the operator feedback significantly improves the accuracy of our model.

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Arvind Narayanan, Saurabh Verma, Eman Ramadan, Pariya Babaie, Zhi-Li Zhang

Abstract

In this paper, we present DeepCache a novel framework for content caching, which can significantly boost cache performance. Our framework is based on powerful deep recurrent neural network models. It comprises of two main components: i) Object Characteristics Predictor, which builds upon deep LSTM Encoder-Decoder model to predict the future characteristics of an object (such as object popularity) — to the best of our knowledge, we are the first to propose LSTM Encoder-Decoder model for content caching; ii) a caching policy component, which accounts for predicted information of objects to make smart caching decisions. In our thorough experiments, we show that applying DeepCache Framework to existing cache policies, such as LRU and k-LRU, significantly boosts the number of cache hits.

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Abhishta Abhishta, Roland van Rijswijk-Deij
and Lambert J. M. Nieuwenhuis

Abstract

Distributed Denial-of-Service (DDoS) attacks continue to pose a serious threat to the availability of Internet services. The Domain Name System (DNS) is part of the core of the Internet and a crucial factor in the successful delivery of Internet services. Because of the importance of DNS, specialist service providers have sprung up in the market, that provide managed DNS services. One of their key selling points is that they protect DNS for a domain against DDoS attacks. But what if such a service becomes the target of a DDoS attack, and that attack succeeds?

In this paper we analyse two such events, an attack on NS1 in May 2016, and an attack on Dyn in October 2016. We do this by analysing the change in the behaviour of the service’s customers. For our analysis we leverage data from the OpenINTEL active DNS measurement system, which covers large parts of the global DNS over time. Our results show an almost immediate and statistically significant change in the behaviour of domains that use NS1 or Dyn as a DNS service provider. We observe a decline in the number of domains that exclusively use NS1 or Dyn as a managed DNS service provider, and see a shift toward risk spreading by using multiple providers. While a large managed DNS provider may be better equipped to protect against attacks, these two case studies show they are not impervious to them. This calls into question the wisdom of using a single provider for managed DNS. Our results show that spreading risk by using multiple providers is an effective countermeasure, albeit probably at a higher cost.

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