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Boldt, M., Borg, A., Ickin, S. & Gustafsson, J. (2019). Anomaly detection of event sequences using multiple temporal resolutions and Markov chains. Knowledge and Information Systems
Open this publication in new window or tab >>Anomaly detection of event sequences using multiple temporal resolutions and Markov chains
2019 (English)In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116Article in journal (Refereed) Epub ahead of print
Abstract [en]

Streaming data services, such as video-on-demand, are getting increasingly more popular, and they are expected to account for more than 80% of all Internet traffic in 2020. In this context, it is important for streaming service providers to detect deviations in service requests due to issues or changing end-user behaviors in order to ensure that end-users experience high quality in the provided service. Therefore, in this study we investigate to what extent sequence-based Markov models can be used for anomaly detection by means of the end-users’ control sequences in the video streams, i.e., event sequences such as play, pause, resume and stop. This anomaly detection approach is further investigated over three different temporal resolutions in the data, more specifically: 1 h, 1 day and 3 days. The proposed anomaly detection approach supports anomaly detection in ongoing streaming sessions as it recalculates the probability for a specific session to be anomalous for each new streaming control event that is received. Two experiments are used for measuring the potential of the approach, which gives promising results in terms of precision, recall, F 1 -score and Jaccard index when compared to k-means clustering of the sessions. © 2019, The Author(s).

Place, publisher, year, edition, pages
Springer London, 2019
Keywords
Anomaly detection, Event sequences, Markov Chains, Multiple temporal resolutions, Video-on-demand
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18026 (URN)10.1007/s10115-019-01365-y (DOI)2-s2.0-85066031197 (Scopus ID)
Available from: 2019-06-14 Created: 2019-06-14 Last updated: 2019-06-17Bibliographically approved
Borg, A., Boldt, M. & Svensson, J. (2019). Using conformal prediction for multi-label document classification in e-Mail support systems. In: Lect. Notes Comput. Sci.: . Paper presented at International Conference on Computational Science, ICCS, Faro, Algarve, 9 July 2019 through 11 July 2019 (pp. 308-322). Springer Verlag, 11536
Open this publication in new window or tab >>Using conformal prediction for multi-label document classification in e-Mail support systems
2019 (English)In: Lect. Notes Comput. Sci., Springer Verlag , 2019, Vol. 11536, p. 308-322Conference paper, Published paper (Refereed)
Abstract [en]

For any corporation the interaction with its customers is an important business process. This is especially the case for resolving various business-related issues that customers encounter. Classifying the type of such customer service e-mails to provide improved customer service is thus important. The classification of e-mails makes it possible to direct them to the most suitable handler within customer service. We have investigated the following two aspects of customer e-mail classification within a large Swedish corporation. First, whether a multi-label classifier can be introduced that performs similarly to an already existing multi-class classifier. Second, whether conformal prediction can be used to quantify the certainty of the predictions without loss in classification performance. Experiments were used to investigate these aspects using several evaluation metrics. The results show that for most evaluation metrics, there is no significant difference between multi-class and multi-label classifiers, except for Hamming loss where the multi-label approach performed with a lower loss. Further, the use of conformal prediction did not introduce any significant difference in classification performance for neither the multi-class nor the multi-label approach. As such, the results indicate that conformal prediction is a useful addition that quantifies the certainty of predictions without negative effects on the classification performance, which in turn allows detection of statistically significant predictions. © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Series
Lecture Notes in Computer Science ; 11536
Keywords
Conformal prediction, Customer support e-mail, Multi-label classification, Electronic mail, Forecasting, Information retrieval systems, Intelligent systems, Sales, Classification performance, Conformal predictions, Customer support, Document Classification, Email classification, Evaluation metrics, Multi label classification, Multi-class classifier, Classification (of information)
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18592 (URN)10.1007/978-3-030-22999-3_28 (DOI)2-s2.0-85068624865 (Scopus ID)9783030229986 (ISBN)
Conference
International Conference on Computational Science, ICCS, Faro, Algarve, 9 July 2019 through 11 July 2019
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-20Bibliographically approved
Boldt, M., Boeva, V. & Borg, A. (2018). Multi-expert estimations of burglars' risk exposure and level of pre-crime preparation using coded crime scene data: Work in progress. In: Brynielsson, J (Ed.), Proceedings - 2018 European Intelligence and Security Informatics Conference, EISIC 2018: . Paper presented at 8th European Intelligence and Security Informatics Conference, EISIC, Karlskrona, 24 October 2018 through 25 October 2018 (pp. 77-80). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Multi-expert estimations of burglars' risk exposure and level of pre-crime preparation using coded crime scene data: Work in progress
2018 (English)In: Proceedings - 2018 European Intelligence and Security Informatics Conference, EISIC 2018 / [ed] Brynielsson, J, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 77-80Conference paper, Published paper (Refereed)
Abstract [en]

Law enforcement agencies strive to link crimes perpetrated by the same offenders into crime series in order to improve investigation efficiency. Such crime linkage can be done using both physical traces (e.g., DNA or fingerprints) or 'soft evidence' in the form of offenders' modus operandi (MO), i.e. their behaviors during crimes. However, physical traces are only present for a fraction of crimes, unlike behavioral evidence. This work-in-progress paper presents a method for aggregating multiple criminal profilers' ratings of offenders' behavioral characteristics based on feature-rich crime scene descriptions. The method calculates consensus ratings from individual experts' ratings, which then are used as a basis for classification algorithms. The classification algorithms can automatically generalize offenders' behavioral characteristics from cues in the crime scene data. Models trained on the consensus rating are evaluated against models trained on individual profiler's ratings. Thus, whether the consensus model shows improved performance over individual models. © 2018 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
classification, crime linkage, Multi-expert decision making, offender profiling, Classification (of information), Risk management, Risk perception, Behavioral characteristics, Classification algorithm, Consensus models, Individual models, Law-enforcement agencies, Work in progress, Crime
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18621 (URN)10.1109/EISIC.2018.00021 (DOI)000483031300012 ()2-s2.0-85069498311 (Scopus ID)9781538694008 (ISBN)
Conference
8th European Intelligence and Security Informatics Conference, EISIC, Karlskrona, 24 October 2018 through 25 October 2018
Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-09-19Bibliographically approved
Boldt, M., Borg, A., Svensson, M. & Hildeby, J. (2018). Predicting burglars' risk exposure and level of pre-crime preparation using crime scene data. Intelligent Data Analysis, 22(1), 167-190, Article ID IDA 322-3210.
Open this publication in new window or tab >>Predicting burglars' risk exposure and level of pre-crime preparation using crime scene data
2018 (English)In: Intelligent Data Analysis, ISSN 1088-467X, Vol. 22, no 1, p. 167-190, article id IDA 322-3210Article in journal (Refereed) Published
Abstract [en]

Objectives: The present study aims to extend current research on how offenders’ modus operandi (MO) can be used in crime linkage, by investigating the possibility to automatically estimate offenders’ risk exposure and level of pre-crime preparation for residential burglaries. Such estimations can assist law enforcement agencies when linking crimes into series and thus provide a more comprehensive understanding of offenders and targets, based on the combined knowledge and evidence collected from different crime scenes. Methods: Two criminal profilers manually rated offenders’ risk exposure and level of pre-crime preparation for 50 burglaries each. In an experiment we then analyzed to what extent 16 machine-learning algorithms could generalize both offenders’ risk exposure and preparation scores from the criminal profilers’ ratings onto 15,598 residential burglaries. All included burglaries contain structured and feature-rich crime descriptions which learning algorithms can use to generalize offenders’ risk and preparation scores from.Results: Two models created by Naïve Bayes-based algorithms showed best performance with an AUC of 0.79 and 0.77 for estimating offenders' risk and preparation scores respectively. These algorithms were significantly better than most, but not all, algorithms. Both scores showed promising distinctiveness between linked series, as well as consistency for crimes within series compared to randomly sampled crimes.Conclusions: Estimating offenders' risk exposure and pre-crime preparation  can complement traditional MO characteristics in the crime linkage process. The estimations are also indicative to function for cross-category crimes that otherwise lack comparable MO. Future work could focus on increasing the number of manually rated offenses as well as fine-tuning the Naïve Bayes algorithm to increase its estimation performance.

Place, publisher, year, edition, pages
IOS Press, 2018
Keywords
Predictive models, Classification, Crime linkage, Offender behavior, Serial crime, Residential burglary
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-13935 (URN)10.3233/IDA-163220 (DOI)000426790500009 ()
Available from: 2017-02-21 Created: 2017-02-21 Last updated: 2018-04-04Bibliographically approved
Erlandsson, F., Bródka, P. & Borg, A. (2018). Seed selection for information cascade in multilayer networks. In: Cherifi H.,Cherifi C.,Musolesi M.,Karsai M. (Ed.), Studies in Computational IntelligenceVolume , 2018, Pages -436: . Paper presented at 6th International Conference on Complex Networks and Their Applications, Complex Networks, 2017, Lyon (pp. 426-436). Springer-Verlag New York, 689
Open this publication in new window or tab >>Seed selection for information cascade in multilayer networks
2018 (English)In: Studies in Computational IntelligenceVolume , 2018, Pages -436 / [ed] Cherifi H.,Cherifi C.,Musolesi M.,Karsai M., Springer-Verlag New York, 2018, Vol. 689, p. 426-436Conference paper, Published paper (Refereed)
Abstract [en]

Information spreading is an interesting field in the domain of online social media. In this work, we are investigating how well different seed selection strategies affect the spreading processes simulated using independent cascade model on eighteen multilayer social networks. Fifteen networks are built based on the user interaction data extracted from Facebook public pages and tree of them are multilayer networks downloaded from public repository (two of them being Twitter networks). The results indicate that various state of the art seed selection strategies for single-layer networks like K-Shell or VoteRank do not perform so well on multilayer networks and are outperformed by Degree Centrality.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2018
Series
Studies in Computational Intelligence, ISSN 1860-949X
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15326 (URN)10.1007/978-3-319-72150-7_35 (DOI)978-3-319-72149-1 (ISBN)978-3-319-72150-7 (ISBN)
Conference
6th International Conference on Complex Networks and Their Applications, Complex Networks, 2017, Lyon
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2018-01-13Bibliographically approved
Boldt, M. & Borg, A. (2017). A statistical method for detecting significant temporal hotspots using LISA statistics. In: Proceedings - 2017 European Intelligence and Security Informatics Conference, EISIC 2017: . Paper presented at European Intelligence and Security Informatics Conference (EISIC), Athens (pp. 123-126). IEEE Computer Society
Open this publication in new window or tab >>A statistical method for detecting significant temporal hotspots using LISA statistics
2017 (English)In: Proceedings - 2017 European Intelligence and Security Informatics Conference, EISIC 2017, IEEE Computer Society, 2017, p. 123-126Conference paper, Published paper (Refereed)
Abstract [en]

This work presents a method for detecting statisticallysignificant temporal hotspots, i.e. the date and time of events,which is useful for improved planning of response activities.Temporal hotspots are calculated using Local Indicators ofSpatial Association (LISA) statistics. The temporal data is ina 7x24 matrix that represents a temporal resolution of weekdaysand hours-in-the-day. Swedish residential burglary events areused in this work for testing the temporal hotspot detectionapproach. Although, the presented method is also useful forother events as long as they contain temporal information, e.g.attack attempts recorded by intrusion detection systems. Byusing the method for detecting significant temporal hotspotsit is possible for domain-experts to gain knowledge about thetemporal distribution of the events, and also to learn at whichtimes mitigating actions could be implemented.

Place, publisher, year, edition, pages
IEEE Computer Society, 2017
Series
European Intelligence and Security Informatics Conference, ISSN 2572-3723
Keywords
Temporal analysis, temporal hotspot, computational criminology, LISA statistics, local indicators of spatial association.
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15166 (URN)000425928200016 ()978-1-5386-2385-5 (ISBN)
Conference
European Intelligence and Security Informatics Conference (EISIC), Athens
Available from: 2017-09-20 Created: 2017-09-20 Last updated: 2018-05-18Bibliographically approved
Borg, A., Boldt, M. & Eliasson, J. (2017). Detecting Crime Series Based on Route Estimation and Behavioral Similarity. In: Brynielsson, J (Ed.), 2017 EUROPEAN INTELLIGENCE AND SECURITY INFORMATICS CONFERENCE (EISIC): . Paper presented at European Intelligence and Security Informatics Conference (EISIC), Athens (pp. 1-8). IEEE
Open this publication in new window or tab >>Detecting Crime Series Based on Route Estimation and Behavioral Similarity
2017 (English)In: 2017 EUROPEAN INTELLIGENCE AND SECURITY INFORMATICS CONFERENCE (EISIC) / [ed] Brynielsson, J, IEEE , 2017, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

A majority of crimes are committed by a minority of offenders. Previous research has provided some support for the theory that serial offenders leave behavioral traces on the crime scene which could be used to link crimes to serial offenders. The aim of this work is to investigate to what extent it is possible to use geographic route estimations and behavioral data to detect serial offenders. Experiments were conducted using behavioral data from authentic burglary reports to investigate if it was possible to find crime routes with high similarity. Further, the use of burglary reports from serial offenders to investigate to what extent it was possible to detect serial offender crime routes. The result show that crime series with the same offender on average had a higher behavioral similarity than random crime series. Sets of crimes with high similarity, but without a known offender would be interesting for law enforcement to investigate further. The algorithm is also evaluated on 9 crime series containing a maximum of 20 crimes per series. The results suggest that it is possible to detect crime series with high similarity using analysis of both geographic routes and behavioral data recorded at crime scenes.

Place, publisher, year, edition, pages
IEEE, 2017
Series
European Intelligence and Security Informatics Conference, ISSN 2572-3723
Keywords
Crime route analysis, crime linkage, residential burglary, Behavioral analysis
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15985 (URN)10.1109/EISIC.2017.10 (DOI)000425928200001 ()978-1-5386-2385-5 (ISBN)
Conference
European Intelligence and Security Informatics Conference (EISIC), Athens
Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-05-18Bibliographically approved
Borg, A., Boldt, M. & Eliasson, J. (2017). Detecting Crime Series Based on Route Estimationand Behavioral Similarity. In: : . Paper presented at European Intelligence and Security Informatics Conference (EISIC), Attica, Greece. IEEE Computer Society
Open this publication in new window or tab >>Detecting Crime Series Based on Route Estimationand Behavioral Similarity
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

A majority of crimes are committed by a minority of offenders. Previous research has provided some support for the theory that serial offenders leave behavioral traces on the crime scene which could be used to link crimes to serial offenders. The aim of this work is to investigate to what extent it is possible to use geographic route estimations and behavioral data to detect serial offenders. Experiments were conducted using behavioral data from authentic burglary reports to investigate if it was possible to find crime routes with high similarity. Further, the use of burglary reports from serial offenders to investigate to what extent it was possible to detect serial offender crime routes. The result show that crime series with the same offender on average had a higher behavioral similarity than random crime series. Sets of crimes with high similarity, but without a known offender would be interesting for law enforcement to investigate further. The algorithm is also evaluated on 9 crime series containing a maximum of 20 crimes per series. The results suggest that it is possible to detect crime series with high similarity using analysis of both geographic routes and behavioral data recorded at crime scenes.

Place, publisher, year, edition, pages
IEEE Computer Society, 2017
Keywords
Crime route analysis, crime linkage, residential burglary, Behavioral analysis
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15167 (URN)
Conference
European Intelligence and Security Informatics Conference (EISIC), Attica, Greece
Available from: 2017-09-20 Created: 2017-09-20 Last updated: 2018-01-13Bibliographically approved
Borg, A. & Boldt, M. (2016). Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information. International Journal of Information Technology and Decision Making, 15(1), 23-42
Open this publication in new window or tab >>Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information
2016 (English)In: International Journal of Information Technology and Decision Making, ISSN 0219-6220, Vol. 15, no 1, p. 23-42Article in journal (Refereed) Published
Abstract [en]

To identify series of residential burglaries, detecting linked crimes performed by the same constellations of criminals is necessary. Comparison of crime reports today is difficult as crime reports traditionally have been written as unstructured text and often lack a common information-basis. Based on a novel process for collecting structured crime scene information, the present study investigates the use of clustering algorithms to group similar crime reports based on combined crime characteristics from the structured form. Clustering quality is measured using Connectivity and Silhouette index (SI), stability using Jaccard index, and accuracy is measured using Rand index (RI) and a Series Rand index (SRI). The performance of clustering using combined characteristics was compared with spatial characteristic. The results suggest that the combined characteristics perform better or similar to the spatial characteristic. In terms of practical significance, the presented clustering approach is capable of clustering cases using a broader decision basis.

Place, publisher, year, edition, pages
World Scientific, 2016
Keywords
Crime clustering, residential burglary analysis, decision support system, combined distance metric
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-11779 (URN)10.1142/S0219622015500339 (DOI)000371127600003 ()
Available from: 2016-04-01 Created: 2016-04-01 Last updated: 2018-01-10Bibliographically approved
Erlandsson, F., Bródka, P., Borg, A. & Johnson, H. (2016). Finding Influential Users in Social Media Using Association Rule Learning. Entropy, 18(5)
Open this publication in new window or tab >>Finding Influential Users in Social Media Using Association Rule Learning
2016 (English)In: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 18, no 5Article in journal (Refereed) Published
Abstract [en]

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI AG, 2016
Keywords
social media, data mining, association rule learning, prediction, social network analysis
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-13575 (URN)10.3390/e18050164 (DOI)000377262900009 ()
Note

open access

Available from: 2016-12-12 Created: 2016-12-12 Last updated: 2018-01-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8929-7220

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