Institution
Helsinki Institute for Information Technology
Facility•Espoo, Finland•
About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.
Papers published on a yearly basis
Papers
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05 Nov 2009TL;DR: A self-stabilizing system must survive arbitrary failures, beyond Byzantine failures, including for instance a total wipe out of volatile memory at all nodes, provided that no further faults happen.
Abstract: Fault tolerance is one of the main concepts in distributed computing. It has been tackled from different angles, e.g. by building replicated systems that can survive crash failures of individual components, or even systems that can tolerate a minority of arbitrarily malicious ("Byzantine") participants.
Self-stabilization, a fault tolerance concept coined by the late Edsger W. Dijkstra in 1973 [1,2], is of a different stamp. A self-stabilizing system must survive arbitrary failures, beyond Byzantine failures, including for instance a total wipe out of volatile memory at all nodes. In other words, the system must self-heal and converge to a correct state even if starting in an arbitrary state, provided that no further faults happen.
51 citations
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01 Dec 2012TL;DR: This paper considers two kinds of attacks on a DHT, one already known attack and one new kind of an attack, and shows how they can be targeted against Mainline DHT and proposes simple countermeasures against them.
Abstract: Distributed hash tables (DHT) are a key building block for modern P2P content-distribution system, for example in implementing the distributed tracker of BitTorrent Mainline DHT. DHTs, due to their fully distributed nature, are known to be vulnerable to certain kinds of attacks and different kinds of defenses have been proposed against these attacks. In this paper, we consider two kinds of attacks on a DHT, one already known attack and one new kind of an attack, and show how they can be targeted against Mainline DHT. We complement them by an extensive measurement study using honeypots which shows that both attacks have been going on for a long time in the network and are still happening. We present numbers showing that the number of sybils in the Mainline DHT network is increasing and is currently around 300,000. We analyze the potential threats from these attacks and propose simple countermeasures against them.
51 citations
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TL;DR: This work describes how it constructed a federated database infrastructure for genotype and phenotype information collected in seven European countries and Australia and connected this database setting via a network called TwinNET to guarantee effortless data exchange and pooled analyses.
Abstract: Integration of complex data and data management represent major challenges in large-scale biobank-based post-genome era research projects like GenomEUtwin (an international collaboration between eight Twin Registries) with extensive amounts of genotype and phenotype data combined from different data sources located in different countries. The challenge lies not only in data harmonization and constant update of clinical details in various locations, but also in the heterogeneity of data storage and confidentiality of sensitive health-related and genetic data. Solid infrastructure must be built to provide secure, but easily accessible and standardized, data exchange also facilitating statistical analyses of the stored data. Data collection sites desire to have full control of the accumulation of data, and at the same time the integration should facilitate effortless slicing and dicing of the data for different types of data pooling and study designs. Here we describe how we constructed a federated database infrastructure for genotype and phenotype information collected in seven European countries and Australia and connected this database setting via a network called TwinNET to guarantee effortless data exchange and pooled analyses. This federated database system offers a powerful facility for combining different types of information from multiple data sources. The system is transparent to end users and application developers, since it makes the set of federated data sources look like a single system. The user need not be aware of the format or site where the data are stored, the language or programming interface of the data source, how the data are physically stored, whether they are partitioned and/or replicated or what networking protocols are used. The user sees a single standardized interface with the desired data elements for pooled analyses.
51 citations
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04 Dec 2017TL;DR: It is shown with case studies that these kernels are necessary when modelling even rather simple time series, image or geospatial data with non-stationary characteristics, and derive efficient inference using model whitening and marginalized posterior.
Abstract: We propose non-stationary spectral kernels for Gaussian process regression by modelling the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-stationary and non-monotonic kernels that can learn input-dependent and potentially long-range, non-monotonic covariances between inputs. We derive efficient inference using model whitening and marginalized posterior, and show with case studies that these kernels are necessary when modelling even rather simple time series, image or geospatial data with non-stationary characteristics.
50 citations
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07 Apr 2014
TL;DR: In this article, the authors present the first independent study of malware infection rates and associated risk factors using data collected directly from over 55,000 Android devices and find that the malware infection rate in Android devices estimated using two malware datasets (0.28% and 0.26%), though small, are significantly higher than the previous independent estimate.
Abstract: There is little information from independent sources in the public domain about mobile malware infection rates. The only previous independent estimate (0.0009%) [11], was based on indirect measurements obtained from domain-name resolution traces. In this paper, we present the first independent study of malware infection rates and associated risk factors using data collected directly from over 55,000 Android devices. We find that the malware infection rates in Android devices estimated using two malware datasets (0.28% and 0.26%), though small, are significantly higher than the previous independent estimate. Based on the hypothesis that some application stores have a greater density of malicious applications and that advertising within applications and cross-promotional deals may act as infection vectors, we investigate whether the set of applications used on a device can serve as an indicator for infection of that device. Our analysis indicates that, while not an accurate indicator of infection by itself, the application set does serve as an inexpensive method for identifying the pool of devices on which more expensive monitoring and analysis mechanisms should be deployed. Using our two malware datasets we show that this indicator performs up to about five times better at identifying infected devices than the baseline of random checks. Such indicators can be used, for example, in the search for new or previously undetected malware. It is therefore a technique that can complement standard malware scanning. Our analysis also demonstrates a marginally significant difference in battery use between infected and clean devices.
50 citations
Authors
Showing all 632 results
Name | H-index | Papers | Citations |
---|---|---|---|
Dimitri P. Bertsekas | 94 | 332 | 85939 |
Olli Kallioniemi | 90 | 353 | 42021 |
Heikki Mannila | 72 | 295 | 26500 |
Jukka Corander | 66 | 411 | 17220 |
Jaakko Kangasjärvi | 62 | 146 | 17096 |
Aapo Hyvärinen | 61 | 301 | 44146 |
Samuel Kaski | 58 | 522 | 14180 |
Nadarajah Asokan | 58 | 327 | 11947 |
Aristides Gionis | 58 | 292 | 19300 |
Hannu Toivonen | 56 | 192 | 19316 |
Nicola Zamboni | 53 | 128 | 11397 |
Jorma Rissanen | 52 | 151 | 22720 |
Tero Aittokallio | 52 | 271 | 8689 |
Juha Veijola | 52 | 261 | 19588 |
Juho Hamari | 51 | 176 | 16631 |