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Ashcroft, Michael
Publikasjoner (9 av 9) Visa alla publikasjoner
Ashcroft, M., Johansson, F., Kaati, L. & Shrestha, A. (2016). Multi-domain alias matching using machine learning. In: Proc. 3rd European Network Intelligence Conference: . Paper presented at ENIC 2016, September 5–7, Wroclaw, Poland (pp. 77-84). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Multi-domain alias matching using machine learning
2016 (engelsk)Inngår i: Proc. 3rd European Network Intelligence Conference, IEEE, 2016, s. 77-84Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We describe a methodology for linking aliases belonging to the same individual based on a user's writing style (stylometric features extracted from the user generated content) and her time patterns (time-based features extracted from the publishing times of the user generated content). While most previous research on social media identity linkage relies on matching usernames, our methodology can also be used for users who actively try to choose dissimilar usernames when creating their aliases. In our experiments on a discussion forum dataset and a Twitter dataset, we evaluate the performance of three different classifiers. We use the best classifier (AdaBoost) to evaluate how well it works on different datasets using different features. Experiments show that combining stylometric and time based features yield good results on our synthetic datasets and a small-scale evaluation on real-world blog data confirm these results, yielding a precision over 95%. The use of emotion-related and Twitter-related features yield no significant impact on the results.

sted, utgiver, år, opplag, sider
IEEE, 2016
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-306944 (URN)10.1109/ENIC.2016.019 (DOI)000399097600011 ()9781509034550 (ISBN)
Konferanse
ENIC 2016, September 5–7, Wroclaw, Poland
Tilgjengelig fra: 2017-02-02 Laget: 2016-11-07 Sist oppdatert: 2019-03-22bibliografisk kontrollert
Ashcroft, M., Magnani, M., Vega, D., Montesi, D. & Rossi, L. (2016). Multilayer Analysis of Online Illicit Marketplaces. In: Brynielsson, J Johansson, F (Ed.), 2016 European Intelligence And Security Informatics Conference (EISIC): . Paper presented at Conference on European Intelligence and Security Informatics Conference (EISIC), AUG 17-19, 2016, Uppsala, SWEDEN (pp. 199-199). IEEE
Åpne denne publikasjonen i ny fane eller vindu >>Multilayer Analysis of Online Illicit Marketplaces
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2016 (engelsk)Inngår i: 2016 European Intelligence And Security Informatics Conference (EISIC) / [ed] Brynielsson, J Johansson, F, IEEE , 2016, s. 199-199Konferansepaper, Publicerat paper (Fagfellevurdert)
sted, utgiver, år, opplag, sider
IEEE, 2016
Serie
European Intelligence and Security Informatics Conference, ISSN 2572-3723
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-346563 (URN)10.1109/EISIC.2016.47 (DOI)000411272300042 ()978-1-5090-2857-3 (ISBN)
Konferanse
Conference on European Intelligence and Security Informatics Conference (EISIC), AUG 17-19, 2016, Uppsala, SWEDEN
Tilgjengelig fra: 2018-03-19 Laget: 2018-03-19 Sist oppdatert: 2018-03-19bibliografisk kontrollert
Ashcroft, M., Kaati, L. & Meyer, M. (2015). A step towards detecting online grooming: Identifying adults pretending to be children. In: Proc. 5th European Intelligence and Security Informatics Conference: . Paper presented at EISIC 2015, September 7–9, Manchester, UK (pp. 98-104). IEEE Computer Society
Åpne denne publikasjonen i ny fane eller vindu >>A step towards detecting online grooming: Identifying adults pretending to be children
2015 (engelsk)Inngår i: Proc. 5th European Intelligence and Security Informatics Conference, IEEE Computer Society, 2015, s. 98-104Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Online grooming is a major problem in todays society where more and more time is spent online. To become friends and establish a relationship with their young victims in online communities, groomers often pretend to be children. In this paper we describe an approach that can be used to detect if an adult is pretending to be a child in a chat room conversation. The approach involves a two step process wherein authors are first classified as being children or adults, and then each child is being examined and false children distinguished from genuine children. Our results show that even if it is hard to separate ordinary adults from children in chat logs it is possible to distinguish real children from adults pretending to be children with a high accuracy. In this paper we will discuss the accuracy of the methods proposed, as well as the features that were important in their success. We believe that this work is an important step towards automated analysis of chat room conversation to detect and possible attempts of grooming. Our approach where we use text analysis to distinguish adults who are pretending to be children from actual children could be used to inform children about the true age of the person that they are communicating. This would be a step towards making the Internet more secure for young children and eliminate grooming.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2015
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-272202 (URN)10.1109/EISIC.2015.41 (DOI)000380550100014 ()9781479986514 (ISBN)
Eksternt samarbeid:
Konferanse
EISIC 2015, September 7–9, Manchester, UK
Tilgjengelig fra: 2015-09-09 Laget: 2016-01-12 Sist oppdatert: 2018-01-10bibliografisk kontrollert
Ashcroft, M., Kaati, L. & Meyer, M. (2015). Are You Really a Child?: A Machine Learning Approach To Protect Children from Online Grooming. In: Proc. National Symposium on Technology and Methodology for Security and Crisis Management: TAMSEC 2015. Paper presented at TAMSEC 2015, November 24–25, Kista, Sweden.
Åpne denne publikasjonen i ny fane eller vindu >>Are You Really a Child?: A Machine Learning Approach To Protect Children from Online Grooming
2015 (engelsk)Inngår i: Proc. National Symposium on Technology and Methodology for Security and Crisis Management: TAMSEC 2015, 2015Konferansepaper, Poster (with or without abstract) (Fagfellevurdert)
Abstract [en]

Online grooming and sexual abuse of children is a major threat towards the security of todays society where more and more time is spent online. To become friends and establish a relationship with their young victims in online communities, groomers often pretend to be children. In this work we describe an approach that can be used to detect if an adult is pretending to be a child in a chat room conversation. Our results show that even if it is hard to separate ordinary adults from children in chat logs it is possible to distinguish real children from adults pretending to be children with a high accuracy.

HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-272244 (URN)
Konferanse
TAMSEC 2015, November 24–25, Kista, Sweden
Tilgjengelig fra: 2016-01-12 Laget: 2016-01-12 Sist oppdatert: 2018-01-10bibliografisk kontrollert
Ashcroft, M., Fisher, A., Kaati, L., Omer, E. & Prucha, N. (2015). Detecting jihadist messages on twitter. In: Proc. 5th European Intelligence and Security Informatics Conference: . Paper presented at EISIC 2015, September 7–9, Manchester, UK (pp. 161-164). IEEE Computer Society
Åpne denne publikasjonen i ny fane eller vindu >>Detecting jihadist messages on twitter
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2015 (engelsk)Inngår i: Proc. 5th European Intelligence and Security Informatics Conference, IEEE Computer Society, 2015, s. 161-164Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Jihadist groups such as ISIS are spreading online propaganda using various forms of social media such as Twitter and YouTube. One of the most common approaches to stop these groups is to suspend accounts that spread propaganda when they are discovered. This approach requires that human analysts manually read and analyze an enormous amount of information on social media. In this work we make a first attempt to automatically detect messages released by jihadist groups on Twitter. We use a machine learning approach that classifies a tweet as containing material that is supporting jihadists groups or not. Even tough our results are preliminary and more tests needs to be carried out we believe that results indicate that an automated approach to aid analysts in their work with detecting radical content on social media is a promising way forward. It should be noted that an automatic approach to detect radical content should only be used as a support tool for human analysts in their work.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2015
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-272203 (URN)10.1109/EISIC.2015.27 (DOI)000380550100027 ()9781479986514 (ISBN)
Eksternt samarbeid:
Konferanse
EISIC 2015, September 7–9, Manchester, UK
Tilgjengelig fra: 2015-09-09 Laget: 2016-01-12 Sist oppdatert: 2018-01-10bibliografisk kontrollert
Ashcroft, M. (2013). Performing Decision-Theoretic Inference in Bayesian Network Ensemble Models. In: Jaeger, M; Nielsen, TD; Viappiani, P (Ed.), Twelfth Scandinavian Conference on Artificial Intelligence: . Paper presented at Twelfth Scandinavian Conference on Artificial Intelligence, 20-22 November, 2013, Aalborg, Denmark (pp. 25-34). , 257
Åpne denne publikasjonen i ny fane eller vindu >>Performing Decision-Theoretic Inference in Bayesian Network Ensemble Models
2013 (engelsk)Inngår i: Twelfth Scandinavian Conference on Artificial Intelligence / [ed] Jaeger, M; Nielsen, TD; Viappiani, P, 2013, Vol. 257, s. 25-34Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The purpose of this paper is to present a simple extension to an existing inference algorithm on influence diagrams (i.e. decision theoretic extensions to Bayesian networks) that permits these algorithms to be applied to ensemble models. The extension, though simple, is important because of the power and robustness that such ensemble models provide [1]. This paper is intended principally as a 'recipe' that can be used even by those unfamiliar with the algorithms extended. Accordingly, I present the algorithms that the original contribution builds upon in full, though references are given to less concise renditions. Those familiar with these algorithms are invited to skip the elucidation. The consequence is a useful paper with more background and less original input than usual.

Serie
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389 ; 257
Emneord
bayesian model averaging, influence diagrams, probabilistic models, graphical models, artificial intelligence, machine learning
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-211926 (URN)10.3233/978-1-61499-330-8-25 (DOI)000343477100004 ()978-1-61499-329-2 (ISBN)
Konferanse
Twelfth Scandinavian Conference on Artificial Intelligence, 20-22 November, 2013, Aalborg, Denmark
Tilgjengelig fra: 2013-12-03 Laget: 2013-12-03 Sist oppdatert: 2018-01-11bibliografisk kontrollert
Ashcroft, M. (2012). An Introduction To Bayesian Networks in Systems and Control. Paper presented at 2012 18th International Conference on Automation and Computing (ICAC).
Åpne denne publikasjonen i ny fane eller vindu >>An Introduction To Bayesian Networks in Systems and Control
2012 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-185566 (URN)
Konferanse
2012 18th International Conference on Automation and Computing (ICAC)
Tilgjengelig fra: 2012-11-26 Laget: 2012-11-26 Sist oppdatert: 2018-01-12
Ashcroft, M. (2012). Bayesian Networks in Business Analytics. In: 2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS). Paper presented at Federated Conference On Computer Science And Information Systems; 9 - 12 September, 2012; Wrocław, Poland (pp. 955-961).
Åpne denne publikasjonen i ny fane eller vindu >>Bayesian Networks in Business Analytics
2012 (engelsk)Inngår i: 2012 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS (FEDCSIS), 2012, s. 955-961Konferansepaper, Publicerat paper (Fagfellevurdert)
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-185567 (URN)000312714400134 ()978-83-60810-48-4 (ISBN)
Konferanse
Federated Conference On Computer Science And Information Systems; 9 - 12 September, 2012; Wrocław, Poland
Tilgjengelig fra: 2012-11-26 Laget: 2012-11-26 Sist oppdatert: 2018-01-12bibliografisk kontrollert
Ashcroft, M. (2012). Using Bayesian networks in business analytics: Overview and short case study. Business Informatics, 3(25)
Åpne denne publikasjonen i ny fane eller vindu >>Using Bayesian networks in business analytics: Overview and short case study
2012 (engelsk)Inngår i: Business Informatics, ISSN 1507-3858, Vol. 3, nr 25Artikkel i tidsskrift (Fagfellevurdert) Published
HSV kategori
Identifikatorer
urn:nbn:se:uu:diva-188761 (URN)
Tilgjengelig fra: 2012-12-19 Laget: 2012-12-19 Sist oppdatert: 2018-01-11bibliografisk kontrollert
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