Recent years have witnessed a slew of coding techniques custom designed for networked storage systems. Network coding inspired regenerating codes are the most prolifically studied among these new age storage centric codes. A lot of effort has been invested in understanding the fundamental achievable trade-offs of storage and bandwidth usage to maintain redundancy in presence of different models of failures, showcasing the efficacy of regenerating codes with respect to traditional erasure coding techniques. For practical usability in open and adversarial environments, as is typical in peer-to-peer systems, we need however not only resilience against erasures, but also from (adversarial) errors. In this paper, we study the resilience of generalized regenerating codes (supporting multi-repairs, using collaboration among newcomers) in the presence of two classes of Byzantine nodes, relatively benign selfish (non-cooperating) nodes, as well as under more active, malicious polluting nodes. We give upper bounds on the resilience capacity of regenerating codes, and show that the advantages of collaborative repair can turn to be detrimental in the presence of Byzantine nodes. We further exhibit that system mechanisms can be combined with regenerating codes to mitigate the effect of rogue nodes.
Pollution attacks are mitigated in peer-to-peer content dissemination systems @cite_18 @cite_8 @cite_17 using a combination of proactive strategies such as digital signature provided by the content source or by reactive strategies such as by randomized probing of the content source, leveraging on the causal relationship in the sequence of content to be delivered, as well as by deploying reputation mechanisms. In such settings, the prevention of pollution attacks is furthermore facilitated by a continuous involvement of the content source, which is assumed to be online.
@cite_18 @cite_17 @cite_8
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The popularity of peer-to-peer (P2P) networks makes them an attractive target to the creators of viruses and other malicious code. Recently a number of viruses designed specifically to spread via P2P networks have emerged. Pollution has also become increasingly prevalent as copyright holders inject multiple decoy versions in order to impede item distribution. In this paper we derive deterministic epidemiological models for the propagation of a P2P virus through a P2P network and the dissemination of pollution. We report on discrete simulations that provide some verification that the models remain sufficiently accurate despite variations in individual peer conduct to provide insight into the behaviour of the system. The paper examines the steady-state behaviour and illustrates how the models may be used to estimate in a computationally efficient manner how effective object reputation schemes will be in mitigating the impact of viruses and preventing the spread of pollution. RSS (really simple syndication) based feeds have become the defacto mechanism of web based publish subscribe. Peer-to-Peer delivery of such feeds can not only alleviate the load at the content server, but also reduce the dissemination latency. However, due to openness of P2P system, malicious peers can join the network as easily as normal peers do. Such malicious peers may pretend to relay but actually not, and thus deny service, or even disseminate counterfeit updates, rendering a Peer-to-Peer mechanism not only useless, but even harmful (e.g. by false updates). We propose overlay independent randomized strategies to mitigate these ill-effects of malicious peers at a marginal overhead, thus enjoying the benefits of Peer-to-Peer dissemination, along with the assurance of content integrity in RSS like web-based publish-subscribe applications without altering currently deployed server infrastructure. We conduct analysis of performance of our proposal by modeling behavior of the system and validating the same with simulation. Results show that our proposal is effective, robust and scalable. Health surveillance systems based on online user-generated content often rely on the identification of textual markers that are related to a target disease. Given the high volume of available data, these systems benefit from an automatic feature selection process. This is accomplished either by applying statistical learning techniques, which do not consider the semantic relationship between the selected features and the inference task, or by developing labour-intensive text classifiers. In this paper, we use neural word embeddings, trained on social media content from Twitter, to determine, in an unsupervised manner, how strongly textual features are semantically linked to an underlying health concept. We then refine conventional feature selection methods by a priori operating on textual variables that are sufficiently close to a target concept. Our experiments focus on the supervised learning problem of estimating influenza-like illness rates from Google search queries. A "flu infection" concept is formulated and used to reduce spurious and potentially confounding features that were selected by previously applied approaches. In this way, we also address forms of scepticism regarding the appropriateness of the feature space, alleviating potential cases of overfitting. Ultimately, the proposed hybrid feature selection method creates a more reliable model that, according to our empirical analysis, improves the inference performance (Mean Absolute Error) of linear and nonlinear regressors by 12 and 28.7 , respectively. A number of online video sharing systems, out of which YouTube is the most popular, provide features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. For instance, spammers may post an unrelated video as response to a popular one, aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, content promoters may try to gain visibility to a specific video by posting a large number of (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we address the issue of detecting video spammers and promoters. Towards that end, we first manually build a test collection of real YouTube users, classifying them as spammers, promoters, and legitimate users. Using our test collection, we provide a characterization of content, individual, and social attributes that help distinguish each user class. We then investigate the feasibility of using supervised classification algorithms to automatically detect spammers and promoters, and assess their effectiveness in our test collection. While our classification approach succeeds at separating spammers and promoters from legitimate users, the high cost of manually labeling vast amounts of examples compromises its full potential in realistic scenarios. For this reason, we further propose an active learning approach that automatically chooses a set of examples to label, which is likely to provide the highest amount of information, drastically reducing the amount of required training data while maintaining comparable classification effectiveness.