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Publikationer (10 of 89) Visa alla publikationer
Garcia, J. (2018). A Fragment Hashing Approach for Scalable and Cloud-Aware Network File Detection. In: Proceedings of NTMS 2018 Conference and Workshop: . Paper presented at 2018 9th IFIP International Conference on New Technologies, Mobility & Security, 26-28 February 2018, Paris, France (pp. 1-5). New York: IEEE
Öppna denna publikation i ny flik eller fönster >>A Fragment Hashing Approach for Scalable and Cloud-Aware Network File Detection
2018 (Engelska)Ingår i: Proceedings of NTMS 2018 Conference and Workshop, New York: IEEE, 2018, s. 1-5Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Monitoring networks for the presence of some particular set of files can, for example, be important in order to avoid exfiltration of sensitive data, or combat the spread of Child Sexual Abuse (CSA) material. This work presents a scalable system for large-scale file detection in high-speed networks. A multi-level approach using packet sampling with rolling and block hashing is introduced. We show that such approach together with a well tuned implementation can perform detection of a large number of files on the network at 10 Gbps using standard hardware. The use of packet sampling enables easy distribution of the monitoring processing functionality, and allows for flexible scaling in a cloud environment. Performance experiments on the most run-time critical hashing parts shows a single-thread performance consistent with 10Gbps line rate monitoring. The file detectability is examined for three data sets over a range of packet sampling rates. A conservative sampling rate of 0.1 is demonstrated to perform well for all tested data sets. It is also shown that knowledge of the file size distribution can be exploited to allow lower sampling rates to be configured for two of the data sets, which in turn results in lower resource usage.

Ort, förlag, år, upplaga, sidor
New York: IEEE, 2018
Nyckelord
Monitoring, Databases, Metadata, Hardware, Throughput, Forensics, System analysis and design
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-67375 (URN)10.1109/NTMS.2018.8328746 (DOI)000448864200076 ()978-1-5386-3662-6 (ISBN)978-1-5386-3663-3 (ISBN)
Konferens
2018 9th IFIP International Conference on New Technologies, Mobility & Security, 26-28 February 2018, Paris, France
Tillgänglig från: 2018-05-24 Skapad: 2018-05-24 Senast uppdaterad: 2019-06-17Bibliografiskt granskad
Garcia, J. & Brunström, A. (2018). Clustering-based separation of media transfers in DPI-classified cellular video and VoIP traffic. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC): . Paper presented at 2018 IEEE Wireless Communications and Networking Conference (WCNC), 15-18 April 2018, Barcelona, Spain.. IEEE
Öppna denna publikation i ny flik eller fönster >>Clustering-based separation of media transfers in DPI-classified cellular video and VoIP traffic
2018 (Engelska)Ingår i: 2018 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, 2018Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Identifying VoIP and video traffic is often useful in the context of managing a cellular network, and to perform such traffic classification deep packet inspection (DPI) approaches are often used. Commercial DPI classifiers do not necessarily differentiate between, for example, YouTube traffic that arises from browsing inside the YouTube app, and traffic arising from the actual viewing of a YouTube video. Here we apply unsupervised clustering methods on such cellular DPI-labeled VoIP and video traffic to identify the characteristic behavior of the two sub-groups of media-transfer and non media-transfer flows. The analysis is based on a measurement campaign performed inside the core network of a commercial cellular operator, collecting data for more than two billion packets in 40+ million flows. A specially instrumented commercial DPI appliance allows the simultaneous collection of per packet information in addition to the DPI classification output. We show that the majority of flows falls into clusters that are easily identifiable as belonging to one of the traffic sub-groups, and that a surprising majority of DPIlabeled VoIP and video traffic is non-media related.

Ort, förlag, år, upplaga, sidor
IEEE, 2018
Serie
IEEE Wireless Communications and Networking Conference. Proceedings, ISSN 1525-3511, E-ISSN 1558-2612
Nyckelord
Media, YouTube, Clustering algorithms, Cryptography, Downlink, Engines, Uplink
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-67798 (URN)10.1109/WCNC.2018.8377027 (DOI)000435542400081 ()978-1-5386-1734-2 (ISBN)978-1-5386-1735-9 (ISBN)
Konferens
2018 IEEE Wireless Communications and Networking Conference (WCNC), 15-18 April 2018, Barcelona, Spain.
Projekt
HITS
Tillgänglig från: 2018-06-19 Skapad: 2018-06-19 Senast uppdaterad: 2019-04-05Bibliografiskt granskad
Garcia, J. (2018). Duplications and Misattributions of File Fragment Hashes in Image and Compressed Files. In: Proceedings of NTMS 2018 Conference and Workshop: . Paper presented at 2018 9th IFIP International Conference on New Technologies, Mobility and Security, February 26-28, Paris, France (pp. 1-5). New York: IEEE
Öppna denna publikation i ny flik eller fönster >>Duplications and Misattributions of File Fragment Hashes in Image and Compressed Files
2018 (Engelska)Ingår i: Proceedings of NTMS 2018 Conference and Workshop, New York: IEEE, 2018, s. 1-5Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Hashing is used in a wide variety of security contexts. Hashes of parts of files, fragment hashes, can be used to detect remains of deleted files in cluster slack, to detect illicit files being sent over a network, to perform approximate file matching, or to quickly scan large storage devices using sector sampling. In this work we examine the fragment hash uniqueness and hash duplication characteristics of five different data sets with a focus on JPEG images and compressed file archives. We consider both block and rolling hashes and evaluate sizes of the hashed fragments ranging from 16 to 4096 bytes. During an initial hash generation phase hash metadata is created for each data set, which in total becomes several several billion hashes. During the scan phase each other data set is scanned and hashes checked for potential matches in the hash metadata. Three aspects of fragment hashes are examined: 1) the rate of duplicate hashes within each data set, 2) the rate of hash misattribution where a fragment hash from the scanned data set matches a fragment in the hash metadata although the actual file is not present in the scan set, 3) to what extent it is possible to detect fragments from files in a hashed set when those files have been compressed and embedded in a zip archive. The results obtained are useful as input to dimensioning and evaluation procedures for several application areas of fragment hashing.

Ort, förlag, år, upplaga, sidor
New York: IEEE, 2018
Nyckelord
Metadata, Transform coding, Forensics, Image coding, Security, Entropy, Focusing
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-67376 (URN)10.1109/NTMS.2018.8328690 (DOI)000448864200021 ()978-1-5386-3662-6 (ISBN)978-1-5386-3663-3 (ISBN)
Konferens
2018 9th IFIP International Conference on New Technologies, Mobility and Security, February 26-28, Paris, France
Tillgänglig från: 2018-05-24 Skapad: 2018-05-24 Senast uppdaterad: 2019-06-17Bibliografiskt granskad
Garcia, J. & Korhonen, T. (2018). Efficient Distribution-Derived Features for High-Speed Encrypted Flow Classification. In: NetAI'18 Proceedings of the 2018 Workshop on Network Meets AI & ML: . Paper presented at 2018 Workshop on Network Meets AI & ML. August 24 - 24, 2018. Budapest, Hungary. (pp. 21-27). New York: Association for Computing Machinery (ACM)
Öppna denna publikation i ny flik eller fönster >>Efficient Distribution-Derived Features for High-Speed Encrypted Flow Classification
2018 (Engelska)Ingår i: NetAI'18 Proceedings of the 2018 Workshop on Network Meets AI & ML, New York: Association for Computing Machinery (ACM), 2018, s. 21-27Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Flow classification is an important tool to enable efficient network resource usage, support traffic engineering, and aid QoS mechanisms. As traffic is increasingly becoming encrypted by default, flow classification is turning towards the use of machine learning methods employing features that are also available for encrypted traffic. In this work we evaluate flow features that capture the distributional properties of in-flow per-packet metrics such as packet size and inter-arrival time. The characteristics of such distributions are often captured with general statistical measures such as standard deviation, variance, etc. We instead propose a Kolmogorov-Smirnov discretization (KSD) algorithm to perform histogram bin construction based on the distributional properties observed in the data. This allows for a richer, histogram based, representation which also requires less resources for feature computation than higher order statistical moments. A comprehensive evaluation using synthetic data from Gaussian and Beta mixtures show that the KSD approach provides Jensen-Shannon distance results surpassing those of uniform binning and probabilistic binning. An empirical evaluation using live traffic traces from a cellular network further shows that when coupled with a random forest classifier the KSD-constructed features improve classification performance compared to general statistical features based on higher order moments, or alternative bin placement approaches.

Ort, förlag, år, upplaga, sidor
New York: Association for Computing Machinery (ACM), 2018
Nyckelord
Traffic classification, Discretization, Machine learning
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-68707 (URN)10.1145/3229543.3229548 (DOI)978-1-4503-5911-5 (ISBN)
Konferens
2018 Workshop on Network Meets AI & ML. August 24 - 24, 2018. Budapest, Hungary.
Projekt
HITS
Tillgänglig från: 2018-08-14 Skapad: 2018-08-14 Senast uppdaterad: 2019-11-08Bibliografiskt granskad
Garcia, J. & Korhonen, T. (2018). On Runtime and Classification Performance of the Discretize-Optimize (DISCO) Classification Approach. Performance Evaluation Review, 46(3), 167-170
Öppna denna publikation i ny flik eller fönster >>On Runtime and Classification Performance of the Discretize-Optimize (DISCO) Classification Approach
2018 (Engelska)Ingår i: Performance Evaluation Review, ISSN 0163-5999, E-ISSN 1557-9484, Vol. 46, nr 3, s. 167-170Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Using machine learning in high-speed networks for tasks such as flow classification typically requires either very resource efficient classification approaches, large amounts of computational resources, or specialized hardware. Here we provide a sketch of the discretize-optimize (DISCO) approach which can construct an extremely efficient classifier for low dimensional problems by combining feature selection, efficient discretization, novel bin placement, and lookup. As feature selection and discretization parameters are crucial, appropriate combinatorial optimization is an important aspect of the approach. A performance evaluation is performed for a YouTube classification task using a cellular traffic data set. The initial evaluation results show that the DISCO approach can move the Pareto boundary in the classification performance versus runtime trade-off by up to an order of magnitude compared to runtime optimized random forest and decision tree classifiers.

Ort, förlag, år, upplaga, sidor
New york, USA: Association for Computing Machinery (ACM), 2018
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-71213 (URN)10.1145/3308897.3308965 (DOI)
Projekt
HITS, 4707
Forskningsfinansiär
KK-stiftelsen
Tillgänglig från: 2019-02-20 Skapad: 2019-02-20 Senast uppdaterad: 2019-11-08Bibliografiskt granskad
Afzal, Z., Garcia, J., Lindskog, S. & Brunström, A. (2018). Slice Distance: An Insert-Only Levenshtein Distance with a Focus on Security Applications. In: Proceedings of NTMS 2018 Conference and Workshop: . Paper presented at 9th IFIP International Conference on New Technologies, Mobility and Security, 26-28 February 2018, Paris, France (pp. 1-5). New York: IEEE
Öppna denna publikation i ny flik eller fönster >>Slice Distance: An Insert-Only Levenshtein Distance with a Focus on Security Applications
2018 (Engelska)Ingår i: Proceedings of NTMS 2018 Conference and Workshop, New York: IEEE, 2018, s. 1-5Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Levenshtein distance is well known for its use in comparing two strings for similarity. However, the set of considered edit operations used when comparing can be reduced in a number of situations. In such cases, the application of the generic Levenshtein distance can result in degraded detection and computational performance. Other metrics in the literature enable limiting the considered edit operations to a smaller subset. However, the possibility where a difference can only result from deleted bytes is not yet explored. To this end, we propose an insert-only variation of the Levenshtein distance to enable comparison of two strings for the case in which differences occur only because of missing bytes. The proposed distance metric is named slice distance and is formally presented and its computational complexity is discussed. We also provide a discussion of the potential security applications of the slice distance.

Ort, förlag, år, upplaga, sidor
New York: IEEE, 2018
Nyckelord
Measurement, Pattern matching, Time complexity, Transforms, Security, DNA
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-67012 (URN)10.1109/NTMS.2018.8328718 (DOI)000448864200049 ()978-1-5386-3662-6 (ISBN)978-1-5386-3663-3 (ISBN)
Konferens
9th IFIP International Conference on New Technologies, Mobility and Security, 26-28 February 2018, Paris, France
Projekt
HITS, 4707
Forskningsfinansiär
KK-stiftelsen, 4707
Tillgänglig från: 2018-04-17 Skapad: 2018-04-17 Senast uppdaterad: 2019-11-11Bibliografiskt granskad
Garcia, J., Korhonen, T., Andersson, R. & Västlund, F. (2018). Towards Video Flow Classification at a Million Encrypted Flows Per Second. In: Leonard Barolli, Makoto Takizawa, Tomoya Enokido, Marek R. Ogiela, Lidia Ogiela & Nadeem Javaid (Ed.), Proceedings of 32nd International Conference on Advanced Information Networking and Applications (AINA): . Paper presented at 32nd International Conference on Advanced Information Networking and Applications (AINA). Krakow, Poland, 16-18 May 2018.. Krakow: IEEE
Öppna denna publikation i ny flik eller fönster >>Towards Video Flow Classification at a Million Encrypted Flows Per Second
2018 (Engelska)Ingår i: Proceedings of 32nd International Conference on Advanced Information Networking and Applications (AINA) / [ed] Leonard Barolli, Makoto Takizawa, Tomoya Enokido, Marek R. Ogiela, Lidia Ogiela & Nadeem Javaid, Krakow: IEEE, 2018Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

As end-to-end encryption on the Internet is becoming more prevalent, techniques such as deep packet inspection (DPI) can no longer be expected to be able to classify traffic. In many cellular networks a large fraction of all traffic is video traffic, and being able to divide flows in the network into video and non-video can provide considerable traffic engineering benefits. In this study we examine machine learning based flow classification using features that are available also for encrypted flows. Using a data set of several several billion packets from a live cellular network we examine the obtainable classification performance for two different ensemble-based classifiers. Further, we contrast the classification performance of a statistical-based feature set with a less computationally demanding alternate feature set. To also examine the runtime aspects of the problem, we export the trained models and use a tailor-made C implementation to evaluate the runtime performance. The results quantify the trade-off between classification and runtime performance, and show that up to 1 million classifications per second can be achieved for a single core. Considering that only the subset of flows reaching some minimum flow length will need to be classified, the results are promising with regards to deployment also in scenarios with very high flow arrival rates.

Ort, förlag, år, upplaga, sidor
Krakow: IEEE, 2018
Serie
Advanced Information Networking and Applications, ISSN 1550-445X, E-ISSN 2332-5658
Nyckelord
Cryptography, Runtime, Cellular networks, Machine learning, Forestry, Data models, Support vector machines
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-68705 (URN)10.1109/AINA.2018.00061 (DOI)000454817500048 ()978-1-5386-2196-7 (ISBN)978-1-5386-2195-0 (ISBN)
Konferens
32nd International Conference on Advanced Information Networking and Applications (AINA). Krakow, Poland, 16-18 May 2018.
Projekt
HITS
Tillgänglig från: 2018-08-14 Skapad: 2018-08-14 Senast uppdaterad: 2019-02-14Bibliografiskt granskad
Garcia, J. (2017). A clustering-based analysis of DPI-labeled video flow characteristics in cellular networks. In: Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network Management: . Paper presented at Integrated Network and Service Management (IM), 2017 IFIP/IEEE Symposium 8-12 May 2017, Lisbon, Portugal (pp. 1-4). New York: IEEE
Öppna denna publikation i ny flik eller fönster >>A clustering-based analysis of DPI-labeled video flow characteristics in cellular networks
2017 (Engelska)Ingår i: Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network Management, New York: IEEE, 2017, s. 1-4Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Using a specially instrumented deep packet inspection (DPI) appliance placed inside the core network of a commercial cellular operator we collect data from almost four million flows produced by a `heavy-hitter' subset of the customer base. The data contains per packet information for the first 100 packets in each flow, along with the classification done by the DPI engine. The data is used with unsupervised learning to obtain clusters of typical video flow behaviors, with the intent to quantify the number of such clusters and examine their characteristics. Among the flows identified as belonging to video applications by the DPI engine, a subset are actually video application signaling flows or other flows not carrying actual transfers of video data. Given that DPI-labeled data can be used to train supervised machine learning models to identify flows carrying video transfers in encrypted traffic, the potential presence and structure of such `noise' flows in the ground truth is important to examine. In this study K-means and DBSCAN is used to cluster the flows marked by the DPI engine as being from a video application. The clustering techniques identify a set of 4 to 6 clusters with archetypal flow behaviors, and a subset of these clusters are found to represent flows that are not actually transferring video data.

Ort, förlag, år, upplaga, sidor
New York: IEEE, 2017
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-64540 (URN)10.23919/INM.2017.7987420 (DOI)978-3-901882-89-0 (ISBN)
Konferens
Integrated Network and Service Management (IM), 2017 IFIP/IEEE Symposium 8-12 May 2017, Lisbon, Portugal
Projekt
HITS
Forskningsfinansiär
KK-stiftelsen, 4707
Tillgänglig från: 2017-10-13 Skapad: 2017-10-13 Senast uppdaterad: 2019-06-17Bibliografiskt granskad
Jalili, L., Parichehreh, A., Alfredsson, S., Garcia, J. & Brunström, A. (2017). Efficient traffic offloading for seamless connectivity in 5G networks onboard high speed trains. In: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC: . Paper presented at 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017, 8-13 October 2017, Montreal, Canada (pp. 1-6). IEEE
Öppna denna publikation i ny flik eller fönster >>Efficient traffic offloading for seamless connectivity in 5G networks onboard high speed trains
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2017 (Engelska)Ingår i: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, IEEE, 2017, s. 1-6Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Seamless wireless connectivity in high mobility scenarios (≥ 300 km/h), is one of the fundamental key requirements for the future 5G networks. High speed train (HST) is one of the preferred mid-range transportation systems, and highlights the challenges of providing wireless connectivity in high mobility scenarios for the 5G networks. Advanced version of Long Term Evolution (LTE-A) from the Third Generation Partnership Project (3GPP) with peak data rate up to 100 Mbps in high mobility scenarios paved the road toward high quality and cost effective onboard Internet in HSTs. However, frequent handovers (HO) of large number of onboard users increase the service interruptions that in turn inevitably decrease the experienced quality of service (QoS). In this paper, according to the two-tier architecture of the HST wireless connectivity, we propose a novel and practically viable onboard traffic offloading mechanism among the HST carriages that effectively mitigates the service interruptions caused by frequent HOs of massive number of onboard users. The proposed architecture does not imply any change on the LTE network standardization. Conclusions are supported by numerical results for realistic LTE parameters and current HST settings.

Ort, förlag, år, upplaga, sidor
IEEE, 2017
Serie
IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops, ISSN 2166-9570, E-ISSN 2166-9589
Nyckelord
5G networks, High speed trains, QoS provisioning, Traffic offloading, Cost effectiveness, Long Term Evolution (LTE), Mobile telecommunication systems, Network architecture, Quality of service, Queueing networks, Radio communication, Railroad cars, Railroad transportation, Railroads, Wireless telecommunication systems, High speed train (HST), Proposed architectures, Seamless connectivity, Third generation partnership project (3GPP), Two-tier architecture, Wireless connectivities, 5G mobile communication systems
Nationell ämneskategori
Telekommunikation Datavetenskap (datalogi) Programvaruteknik
Identifikatorer
urn:nbn:se:kau:diva-67276 (URN)10.1109/PIMRC.2017.8292462 (DOI)000426970901150 ()2-s2.0-85045264762 (Scopus ID)9781538635315 (ISBN)
Konferens
28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017, 8-13 October 2017, Montreal, Canada
Projekt
HITS, 4707
Forskningsfinansiär
KK-stiftelsen
Tillgänglig från: 2018-05-04 Skapad: 2018-05-04 Senast uppdaterad: 2019-11-10Bibliografiskt granskad
Garcia, J., Alfredsson, S. & Brunström, A. (2017). Examining cellular access systems on trains: Measurements and change detection. In: Proceedings of the 1st Network Traffic Measurement and Analysis Conference: . Paper presented at Network Traffic Measurement and Analysis Conference (TMA), 21-23 June, 2017. Dublin, Ireland (pp. 1-6). New York: IEEE
Öppna denna publikation i ny flik eller fönster >>Examining cellular access systems on trains: Measurements and change detection
2017 (Engelska)Ingår i: Proceedings of the 1st Network Traffic Measurement and Analysis Conference, New York: IEEE, 2017, s. 1-6Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Abstract:Access to reliable high-quality communication services on trains is important for today's mobile users. Train-mounted aggregation routers that provide WiFi access to train passengers and bundle external communication over multiple cellular modems/links is an efficient way of providing such services. Still, the characteristics of such systems have received limited attention in the literature. In this paper we examine the communication characteristics of such systems based on a large data set gathered over six months from an operational Swedish railway system. We characterize the conditions in terms of usage load, train velocity profiles, and observed throughput and delay as well as the relation between these parameters. Furthermore, we examine the data from an anomaly detection perspective. Based on a changepoint detection method, we examine how the collected metrics varies over the six months. Being able to detect shifts in the metrics over time can help detect anomalous changes in the hardware or environment, and also further helps explain the factors affecting the observed behaviors.

Ort, förlag, år, upplaga, sidor
New York: IEEE, 2017
Nyckelord
cellular radio, mobile radio, railway communication, railways, wireless LAN
Nationell ämneskategori
Datavetenskap (datalogi) Telekommunikation
Forskningsämne
Datavetenskap
Identifikatorer
urn:nbn:se:kau:diva-64541 (URN)10.23919/TMA.2017.8002916 (DOI)000426454700021 ()978-3-901882-95-1 (ISBN)
Konferens
Network Traffic Measurement and Analysis Conference (TMA), 21-23 June, 2017. Dublin, Ireland
Projekt
HITS
Forskningsfinansiär
KK-stiftelsen, 4707
Tillgänglig från: 2017-10-16 Skapad: 2017-10-16 Senast uppdaterad: 2019-11-10Bibliografiskt granskad
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0003-3461-7079

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