Ändra sökning
Avgränsa sökresultatet
1 - 42 av 42
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Träffar per sida
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
Markera
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1. Baca, Dejan
    et al.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Carlsson, Bengt
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Jacobsson, Andreas
    A Novel Security-Enhanced Agile Software Development Process Applied in an Industrial Setting2015Ingår i: Proceedings 10th International Conference on Availability, Reliability and Security ARES 2015, IEEE Computer Society Digital Library, 2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    A security-enhanced agile software development process, SEAP, is introduced in the development of a mobile money transfer system at Ericsson Corp. A specific characteristic of SEAP is that it includes a security group consisting of four different competences, i.e., security manager, security architect, security master and penetration tester. Another significant feature of SEAP is an integrated risk analysis process. In analyzing risks in the development of the mobile money transfer system, a general finding was that SEAP either solves risks that were previously postponed or solves a larger proportion of the risks in a timely manner. The previous software development process, i.e., the baseline process of the comparison outlined in this paper, required 2.7 employee hours spent for every risk identified in the analysis process compared to, on the average, 1.5 hours for the SEAP. The baseline development process left 50% of the risks unattended in the software version being developed, while SEAP reduced that figure to 22%. Furthermore, SEAP increased the proportion of risks that were corrected from 12.5% to 67.1%, i.e., more than a five times increment. This is important, since an early correction may avoid severe attacks in the future. The security competence in SEAP accounts for 5% of the personnel cost in the mobile money transfer system project. As a comparison, the corresponding figure, i.e., for security, was 1% in the previous development process.

  • 2. Boldt, Martin
    Privacy-Invasive Software2010Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    As computers are increasingly more integrated into our daily lives we become more dependent on software. This situation is exploited by villainous actors on the Internet that distribute malicious software in search for fast financial gains on the expense of deceived computer users. As a result, computer users need more accurate and aiding mechanisms to assist them when separating legitimate software from its unwanted counterparts. However, such separations are complicated due to a greyzone of software that exists between legitimate and purely malicious software. The software in this greyzone often vaguely labeled spyware. This work introduce both user-aiding mechanisms and an attempt to clarify the greyzone by introducing the concept of privacy-invasive software (PIS) as a category of software that ignores the users’ right to be left alone. Such software is distributed with a specific intent (often of commercial nature), which negatively affect the users to various degree. PIS is therefore classified with respect to the degree of informed consent and the amount of negative consequences for the users. To mitigate the effects from PIS, two novel mechanisms for safeguarding user consent during software installation are introduced; a collaborative software reputation system; and an automated End User License Agreement (EULA) classification. In the software reputation system, users collaborate by sharing experiences of previously used software programs, allowing new users to rely on the collective experience when installing software. The EULA classification generalizes patterns from a set of both legitimate and questionable software EULAs, so that computer users can automatically classify previously unknown EULAs as belonging to legitimate software or not. Both techniques increase user awareness about software program behavior, which allow users to make more informed decisions concerning software installations, which arguably reduces the threat from PIS. We present experimental results showing the ability of a set of machine learning algorithms ability to perform automated EULA classification. In addition, we also present a prototype implementation of a software reputation system, together with simulation results of the large-scale use of the system.

  • 3. Boldt, Martin
    Privacy-Invasive Software: Exploring Effects and Countermeasures2007Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    As computers are increasingly more integrated into our daily lives, we need aiding mechanisms for separating legitimate software from their unwanted counterparts. We use the term Privacy-Invasive Software (PIS) to refer to such illegitimate software, sometimes loosely labelled as spyware. In this thesis, we include an introduction to PIS, and how it differs from both legitimate and traditionally malicious software. We also present empirical measurements indicating the effects that PIS have on infected computers and networks. An important contribution of this work is a classification of PIS in which we target both the level of user consent, as well as the degree of user consequences associated with PIS. These consequences, affecting both users and their computers, form a global problem that deteriorates a vast number of users’ computer experiences today. As a way to hinder, or at least mitigate, this development we argue for more user-oriented countermeasures that focus on informing users about the behaviour and consequences associated with using a particular software. In addition to current reactive countermeasures, we also need preventive tools dealing with the threat of PIS before it enters users’ computers. Collaborative reputation systems present an interesting way forward towards such preventive and user-oriented countermeasures against PIS. Moving the software reputations from old channels (such as computer magazines or friends’ recommendations) into an instantly fast reputation system would be beneficial for the users when distinguishing unwanted software from legitimate. It is important that such a reputation system is designed to address antagonistic intentions from both individual users and groups thereof, so that users could depend on the reputations. This would allow users to reach more informed decisions by taking the reported consequences into account when deciding whether they want a specific software to enter their computer or not.

  • 4.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Anton, Borg
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Clustering residential burglaries using multiple heterogeneous variablesIngår i: International Journal of Information Technology & Decision MakingArtikel i tidskrift (Refereegranskat)
    Abstract [en]

    To identify series of residential burglaries, detecting linked crimes performed bythe same constellations of criminals is necessary. Comparison of crime reports today isdicult as crime reports traditionally have been written as unstructured text and oftenlack a common information-basis. Based on a novel process for collecting structured crimescene information the present study investigates the use of clustering algorithms to groupsimilar crime reports based on combined crime characteristics from the structured form.Clustering quality is measured using Connectivity and Silhouette index, stability usingJaccard index, and accuracy is measured using Rand index and a Series Rand index.The performance of clustering using combined characteristics was compared with spatialcharacteristic. The results suggest that the combined characteristics perform better orsimilar to the spatial characteristic. In terms of practical signicance, the presentedclustering approach is capable of clustering cases using a broader decision basis.

  • 5.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Multi-expert estimations of burglars' risk exposure and level of pre-crime preparation using coded crime scene data: Work in progress2018Ingår i: Proceedings - 2018 European Intelligence and Security Informatics Conference, EISIC 2018 / [ed] Brynielsson, J, Institute of Electrical and Electronics Engineers Inc. , 2018, s. 77-80Konferensbidrag (Refereegranskat)
    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.

  • 6.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Evaluating Temporal Analysis Methods UsingResidential Burglary Data2016Ingår i: ISPRS International Journal of Geo-Information, Special Issue on Frontiers in Spatial and Spatiotemporal Crime Analytics, ISSN 2220-9964, Vol. 5, nr 9, s. 1-22Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Law enforcement agencies, as well as researchers rely on temporal analysis methods in many crime analyses, e.g., spatio-temporal analyses. A number of temporal analysis methods are being used, but a structured comparison in different configurations is yet to be done. This study aims to fill this research gap by comparing the accuracy of five existing, and one novel, temporal analysis methods in approximating offense times for residential burglaries that often lack precise time information. The temporal analysis methods are evaluated in eight different configurations with varying temporal resolution, as well as the amount of data (number of crimes) available during analysis. A dataset of all Swedish residential burglaries reported between 2010 and 2014 is used (N = 103,029). From that dataset, a subset of burglaries with known precise offense times is used for evaluation. The accuracy of the temporal analysis methods in approximating the distribution of burglaries with known precise offense times is investigated. The aoristic and the novel aoristic_ext method perform significantly better than three of the traditional methods. Experiments show that the novel aoristic_ext method was most suitable for estimating crime frequencies in the day-of-the-year temporal resolution when reduced numbers of crimes were available during analysis. In the other configurations investigated, the aoristic method showed the best results. The results also show the potential from temporal analysis methods in approximating the temporal distributions of residential burglaries in situations when limited data are available.

  • 7. Boldt, Martin
    et al.
    Borg, Anton
    Carlsson, Bengt
    On the Simulation of a Software Reputation System2010Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, there are difficulties finding all malicious programs due to juridical restrictions and deficits concerning the anti-malicious programs. Also, a "grey-zone" of questionable programs exists, hard for different protection programs to handle and almost impossible for a single user to judge. A software reputation system consisting of expert, average and novice users are proposed as a complement to let anti-malware programs or dedicated human experts decide about questionable programs. A simulation of the factors involved is accomplished by varying the user groups involved, modifying each user's individual trust factor, specifying an upper trust factor limit and accounting for previous rating influence. As a proposed result, a balanced, well-informed rating of judged programs appears, i.e. a balance between quickly reaching a well-informed decision and not giving a single voter too much power.

  • 8.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Ickin, Selim
    Ericsson Research, SWE.
    Gustafsson, Jörgen
    Ericsson Research, SWE.
    Anomaly detection of event sequences using multiple temporal resolutions and Markov chains2019Ingår i: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116Artikel i tidskrift (Refereegranskat)
    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).

  • 9.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Melander, Ulf
    En strukturerad metod för registrering och automatisk analys av brott2014Ingår i: The Past, the Present and the Future of Police Research: Proceedings from the fifth Nordic Police Research seminar / [ed] Rolf Granér och Ola Kronkvist, 2014Konferensbidrag (Refereegranskat)
    Abstract [sv]

    I detta artikel beskrivs en metod som används i polisregionerna Syd, Väst och Stockholm1 för att samla in strukturerade brottsplatsuppgifter från bostadsinbrott, samt hur den insamlade informationen kan analyseras med automatiska metoder som kan assistera brottssamordnare i deras arbete. Dessa automatiserade analyser kan användas som filtrerings- eller selekteringsverktyg för bostadsinbrott och därmed effektivisera och underlätta arbetet. Vidare kan metoden användas för att avgöra sannolikheten att två brott är utförda av samma gärningsman, vilket kan hjälpa polisen att identifiera serier av brott. Detta är möjligt då gärningsmän tenderar att begå brott på ett snarlikt sätt och det är möjligt, baserat på strukturerade brottsplatsuppgifter, att automatiskt hitta dessa mönster. I kapitlet presenteras och utvärderas en prototyp på ett IT-baserat beslutsstödsystem samt två automatiska metoder för brottssamordning.

  • 10.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Svensson, Martin
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för industriell ekonomi.
    Hildeby, Jonas
    Polisen, SWE.
    Predicting burglars' risk exposure and level of pre-crime preparation using crime scene data2018Ingår i: Intelligent Data Analysis, ISSN 1088-467X, Vol. 22, nr 1, s. 167-190, artikel-id IDA 322-3210Artikel i tidskrift (Refereegranskat)
    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.

  • 11. Boldt, Martin
    et al.
    Carlsson, Bengt
    Analysing Countermeasures Against Privacy-Invasive Software2006Konferensbidrag (Refereegranskat)
    Abstract [en]

    User privacy is widely affected by the occurrence of privacy-invasive software (PIS) on the Internet. Various forms of countermeasures try to mitigate the negative effects caused by PIS. We use a computer forensic tool to evaluate an anti-spyware tool, with respect to found PIS over a four years period. Within the anti-spyware tool PIS was slowly identified, caused classification problems, and formely classified PIS were sometimes excluded. Background information on both PIS and countermeasure techniques are also presented, followed by discussions on legal disputes between developers of PIS and vendors of countermeasures. © 2006 IEEE.

  • 12. Boldt, Martin
    et al.
    Carlsson, Bengt
    Analysing Privacy-Invasive Software Countermeasures2006Konferensbidrag (Refereegranskat)
  • 13. Boldt, Martin
    et al.
    Carlsson, Bengt
    Confidentiality Aspects within Road User Charging Systems: the Swedish Case2008Konferensbidrag (Refereegranskat)
    Abstract [sv]

    Analys av integritetsaspekter kopplade till det svenska vägskattesystemet.

  • 14. Boldt, Martin
    et al.
    Carlsson, Bengt
    Privacy-Invasive Software and Preventive Mechanisms2007Ingår i: Malware: An Introduction / [ed] Jain, Ravi K., ICFAI Press , 2007Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 15. Boldt, Martin
    et al.
    Carlsson, Bengt
    Privacy-Invasive Software and Preventive Mechanisms2006Konferensbidrag (Refereegranskat)
  • 16. Boldt, Martin
    et al.
    Carlsson, Bengt
    Jacobsson, Andreas
    Exploring Spyware Effects2004Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we discuss various types of spyware programs, their behaviour, how they typically infect computers, and the propagation of new varieties of spyware programs. In two experiments, we investigate the occurrence and impact of spyware programs found in popular P2P applications. Based on the findings from the empirical investigations, we try to lift the perspective to a more general view on spyware deriving from the theory of (virtual) network effects. In a model, we categorize in what ways spyware might decrease the utility of belonging to a large virtual network. Here, the baseline is that spyware programs intrude systems and networks, but since they profit from user data they also intrude user privacy. In the model, the intrusions are classified as moderate, severe or disastrous. We found that spyware has the potential to overthrow the positive aspects of belonging to a large network, and network owners should therefore be very careful about permitting such programs in applications and on networks.

  • 17. Boldt, Martin
    et al.
    Carlsson, Bengt
    Jacobsson, Andreas
    Exploring Spyware Effects2007Ingår i: Spyware: An Insight / [ed] Jain, Ravi K., Hyderabad: ICFAI University Press , 2007, s. 39-58Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 18. Boldt, Martin
    et al.
    Carlsson, Bengt
    Larsson, Tobias
    Lindén, Niklas
    Preventing Privacy-Invasive Software using Online Reputations2008Konferensbidrag (Refereegranskat)
    Abstract [en]

    Privacy-invasive software, loosely labeled spyware, is an increasingly common problem for today’s computer users, one to which there is no absolute cure. Most of the privacy-invasive software are positioned in a legal gray zone, as the user accepts the malicious behaviour when agreeing to the End User License Agreement. This paper proposes the use of a specialized reputation system to gather and share information regarding software behaviour between community users. A client application helps guide the user at the point of executing software on the local computer, displaying other users’ feedback about the expected behaviour of the software. We discuss important aspects to consider when constructing such a system, and propose possible solutions. Based on the observations made, we implemented a client/server based proof-of-concept tool, which allowed us to demonstrate how such a system would work. We also compare this solution to other, more conventional, protection methods such as anti-virus and anti-spyware software.

  • 19. Boldt, Martin
    et al.
    Carlsson, Bengt
    Martinsson, Roy
    Software Vulnerability Assessment: Version Extraction and Verification2007Konferensbidrag (Refereegranskat)
  • 20.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Jacobsson, Andreas
    Carlsson, Bengt
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    On the risk exposure of smart home automation systems2014Ingår i: Proceedings 2014 International Conferenceon Future Internet of Things and Cloud, IEEE Computer Society Digital Library, 2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    A recent study has shown that more than every fourth person in Sweden feels that they have poor knowledge and control over their energy use, and that four out of ten would like to be more aware and to have better control over their consumption [5]. A solution is to provide the householders with feedback on their energy consumption, for instance, through a smart home automation system [10]. Studies have shown that householders can reduce energy consumption with up to 20% when gaining such feedback [5] [10]. Home automation is a prime example of a smart environment built on various types of cyber-physical systems generating volumes of diverse, heterogeneous, complex, and distributed data from a multitude of applications and sensors. Thereby, home automation is also an example of an Internet of Things (IoT) scenario, where a communication network extends the present Internet by including everyday items and sensors [22]. Home automation is attracting more and more attention from commercial actors, such as, energy suppliers, infrastructure providers, and third party software and hardware vendors [8] [10]. Among the non-commercial stake-holders, there are various governmental institutions, municipalities, as well as, end-users.

  • 21. Boldt, Martin
    et al.
    Jacobsson, Andreas
    Lavesson, Niklas
    Davidsson, Paul
    Automated Spyware Detection Using End User License Agreements2008Konferensbidrag (Refereegranskat)
    Abstract [sv]

    Spridandet av spyware har ökat dramatiskt och det är ofta svårt för användaren att veta om spyware kommer att installeras samtidigt som en nedladdat applikation skall installeras. Den här studien undersöker om det är möjligt att avgöra om en applikation innehåller spyware genom att applicera data mining tekniker på applikationens slutanvändarlicens.

  • 22.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Rekanar, Kaavya
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Analysis and text classification of privacy policies from rogue and top-100 fortune global companies2019Ingår i: International Journal of Information Security and Privacy, ISSN 1930-1650, E-ISSN 1930-1669, Vol. 13, nr 2, s. 47-66Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In the present article, the authors investigate to what extent supervised binary classification can be used to distinguish between legitimate and rogue privacy policies posted on web pages. 15 classification algorithms are evaluated using a data set that consists of 100 privacy policies from legitimate websites (belonging to companies that top the Fortune Global 500 list) as well as 67 policies from rogue websites. A manual analysis of all policy content was performed and clear statistical differences in terms of both length and adherence to seven general privacy principles are found. Privacy policies from legitimate companies have a 98% adherence to the seven privacy principles, which is significantly higher than the 45% associated with rogue companies. Out of the 15 evaluated classification algorithms, Naïve Bayes Multinomial is the most suitable candidate to solve the problem at hand. Its models show the best performance, with an AUC measure of 0.90 (0.08), which outperforms most of the other candidates in the statistical tests used. Copyright © 2019, IGI Global.

  • 23. Boldt, Martin
    et al.
    Wieslander, Johan
    Carlsson, Bengt
    Investigating spyware on the internet2003Konferensbidrag (Refereegranskat)
  • 24.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information2016Ingår i: International Journal of Information Technology and Decision Making, ISSN 0219-6220, Vol. 15, nr 1, s. 23-42Artikel i tidskrift (Refereegranskat)
    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.

  • 25. Borg, Anton
    et al.
    Boldt, Martin
    Carlsson, Bengt
    Simulating malicious users in a software reputation system2011Ingår i: Communications in Computer and Information Science, Springer , 2011, Vol. 186, s. 147-156Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, computer users have trouble in separating malicious and legitimate software. Traditional countermeasures such as anti-virus tools mainly protect against truly malicious programs, but the situation is complicated due to a "grey-zone" of questionable programs that are difficult to classify. We therefore suggest a software reputation system (SRS) to help computer users in separating legitimate software from its counterparts. In this paper we simulate the usage of a SRS to investigate the effects that malicious users have on the system. Our results show that malicious users will have little impact on the overall system, if kept within 10% of the population. However, a coordinated attack against a selected subset of the applications may distort the reputation of these applications. The results also show that there are ways to detect attack attempts in an early stage. Our conclusion is that a SRS could be used as a decision support system to protect against questionable software.

  • 26.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Eliasson, Johan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Detecting Crime Series Based on Route Estimation and Behavioral Similarity2017Ingår i: 2017 EUROPEAN INTELLIGENCE AND SECURITY INFORMATICS CONFERENCE (EISIC) / [ed] Brynielsson, J, IEEE , 2017, s. 1-8Konferensbidrag (Refereegranskat)
    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.

  • 27.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Eliasson, Johan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Detecting Crime Series Based on Route Estimationand Behavioral Similarity2017Konferensbidrag (Refereegranskat)
    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.

  • 28. Borg, Anton
    et al.
    Boldt, Martin
    Lavesson, Niklas
    Informed Software Installation through License Agreement Categorization2011Konferensbidrag (Refereegranskat)
    Abstract [en]

    Spyware detection can be achieved by using machinelearning techniques that identify patterns in the End User License Agreements (EULAs) presented by application installers. However, solutions have required manual input from the user with varying degrees of accuracy. We have implemented an automatic prototype for extraction and classification and used it to generate a large data set of EULAs. This data set is used to compare four different machine learning algorithms when classifying EULAs. Furthermore, the effect of feature selection is investigated and for the top two algorithms, we investigate optimizing the performance using parameter tuning. Our conclusion is that feature selection and performance tuning are of limited use in this context, providing limited performance gains. However, both the Bagging and the Random Forest algorithms show promising results, with Bagging reaching an AUC measure of 0.997 and a False Negative Rate of 0.062. This shows the applicability of License Agreement Categorization for realizing informed software installation.

  • 29.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Melander, Ulf
    Boeva, Veselka
    Detecting serial residential burglaries using clustering2014Ingår i: Expert Systems with Applications, ISSN 0957-4174 , Vol. 41, nr 11, s. 5252-5266Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    According to the Swedish National Council for Crime Prevention, law enforcement agencies solved approximately three to five percent of the reported residential burglaries in 2012. Internationally, studies suggest that a large proportion of crimes are committed by a minority of offenders. Law enforcement agencies, consequently, are required to detect series of crimes, or linked crimes. Comparison of crime reports today is difficult as no systematic or structured way of reporting crimes exists, and no ability to search multiple crime reports exist. This study presents a systematic data collection method for residential burglaries. A decision support system for comparing and analysing residential burglaries is also presented. The decision support system consists of an advanced search tool and a plugin-based analytical framework. In order to find similar crimes, law enforcement officers have to review a large amount of crimes. The potential use of the cut-clustering algorithm to group crimes to reduce the amount of crimes to review for residential burglary analysis based on characteristics is investigated. The characteristics used are modus operandi, residential characteristics, stolen goods, spatial similarity, or temporal similarity. Clustering quality is measured using the modularity index and accuracy is measured using the rand index. The clustering solution with the best quality performance score were residential characteristics, spatial proximity, and modus operandi, suggesting that the choice of which characteristic to use when grouping crimes can positively affect the end result. The results suggest that a high quality clustering solution performs significantly better than a random guesser. In terms of practical significance, the presented clustering approach is capable of reduce the amounts of cases to review while keeping most connected cases. While the approach might miss some connections, it is also capable of suggesting new connections. The results also suggest that while crime series clustering is feasible, further investigation is needed.

  • 30.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Svensson, Johan
    Telenor Sverige AB, SWE.
    Using conformal prediction for multi-label document classification in e-Mail support systems2019Ingår i: Lect. Notes Comput. Sci., Springer Verlag , 2019, Vol. 11536, s. 308-322Konferensbidrag (Refereegranskat)
    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.

  • 31. Carlsson, Bengt
    et al.
    Boldt, Martin
    Security Analysis of the Swedish Road User Charging System2008Konferensbidrag (Refereegranskat)
    Abstract [sv]

    Hotanalys på vägskattesystemet i Sverige.

  • 32.
    Erlandsson, Fredrik
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Boldt, Martin
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Johnson, Henric
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Privacy threats related to user profiling in online social networks2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    The popularity of Online Social Networks (OSNs) has increased the visibility of users profiles and interactions performed between users. In this paper we structure different privacy threats related to OSNs and describe six different types of privacy threats. One of these threats, named public information harvesting, is previously not documented so we therefore present it in further detail by also presenting the results from a proof-of-concept implementation of that threat. The basis of the attack is gathering of user interactions from various open groups on Facebook which then is transformed into a social interaction graph. Since the data gathered from the OSN originates from open groups it could be executed by any third-party connected to the Internet independently of the users' privacy settings. In addition to presenting the different privacy threats we also we propose a range of different protection techniques.

  • 33.
    Erlandsson, Fredrik
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Bródka, Piotr
    Wrocław University of Science and Technology, POL.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Johnson, Henric
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Do We Really Need To Catch Them All?: A New User-Guided Social Media Crawling Method2017Ingår i: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 19, nr 12, artikel-id 686Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    With the growing use of popular social media services like Facebook and Twitter it is hard to collect all content from the networks without access to the core infrastructure or paying for it. Thus, if all content cannot be collected one must consider which data are of most importance.In this work we present a novel User-Guided Social Media Crawling method (USMC) that is able to collect data from social media, utilizing the wisdom of the crowd to decide the order in which user generated content should be collected, to cover as many user interactions as possible. USMC is validated by crawling 160 Facebook public pages, containing 368 million users and 1.3 billion interactions, and it is compared with two other crawling methods. The results show that it is possible to cover approximately 75% of the interactions on a Facebook page by sampling just 20% of its posts, and at the same time reduce the crawling time by 53%.What is more, the social network constructed from the 20% sample has more than 75% of the users and edges compared to the social network created from all posts, and has very similar degree distribution.

  • 34.
    Erlandsson, Fredrik
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Nia, Roozbeh
    Boldt, Martin
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Johnson, Henric
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Wu, S. Felix
    Crawling Online Social Networks2015Ingår i: SECOND EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2015), IEEE Computer Society, 2015, s. 9-16Konferensbidrag (Refereegranskat)
    Abstract [en]

    Researchers put in tremendous amount of time and effort in order to crawl the information from online social networks. With the variety and the vast amount of information shared on online social networks today, different crawlers have been designed to capture several types of information. We have developed a novel crawler called SINCE. This crawler differs significantly from other existing crawlers in terms of efficiency and crawling depth. We are getting all interactions related to every single post. In addition, are we able to understand interaction dynamics, enabling support for making informed decisions on what content to re-crawl in order to get the most recent snapshot of interactions. Finally we evaluate our crawler against other existing crawlers in terms of completeness and efficiency. Over the last years we have crawled public communities on Facebook, resulting in over 500 million unique Facebook users, 50 million posts, 500 million comments and over 6 billion likes.

  • 35.
    Jacobsson, Andreas
    et al.
    Malmo Univ, Dept Comp Sci, S-20505 Malmo, Sweden..
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Carlsson, Bengt
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    A risk analysis of a smart home automation system2016Ingår i: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 56, s. 719-733Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Enforcing security in Internet of Things environments has been identified as one of the top barriers for realizing the vision of smart, energy-efficient homes and buildings. In this context, understanding the risks related to the use and potential misuse of information about homes, partners, and end-users, as well as, forming methods for integrating security-enhancing measures in the design is not straightforward and thus requires substantial investigation. A risk analysis applied on a smart home automation system developed in a research project involving leading industrial actors has been conducted. Out of 32 examined risks, 9 were classified as low and 4 as high, i.e., most of the identified risks were deemed as moderate. The risks classified as high were either related to the human factor or to the software components of the system. The results indicate that with the implementation of standard security features, new, as well as, current risks can be minimized to acceptable levels albeit that the most serious risks, i.e., those derived from the human factor, need more careful consideration, as they are inherently complex to handle. A discussion of the implications of the risk analysis results points to the need for a more general model of security and privacy included in the design phase of smart homes. With such a model of security and privacy in design in place, it will contribute to enforcing system security and enhancing user privacy in smart homes, and thus helping to further realize the potential in such loT environments. (C) 2015 Elsevier B.V. All rights reserved.

  • 36. Jacobsson, Andreas
    et al.
    Boldt, Martin
    Carlsson, Bengt
    Privacy-Invasive Software in Filesharing2004Konferensbidrag (Refereegranskat)
    Abstract [en]

    Personal privacy is affected by the occurrence of adware and spyware in peer-topeer tools. In an experiment, we investigated five file-sharing tools and found that they all contained ad-/spyware programs, and, that these hidden components communicated with several servers on the Internet. Although there was no exchange of files by way of the file-sharing tools, they generated a significant amount of network traffic. Amongst the retrieved ad-/spyware components that communicated with the Internet, we discovered that privacy-invasive information such as, e.g., user data and Internet browsing history was transmitted. In effect, ad-/spyware activity in file-sharing tools creates serious problems not only to user privacy and security, but also to network and system performance. The increasing presence of hidden and bundled ad /spyware programs are therefore not beneficial for the development of a secure and stable use of the Internet.

  • 37. Lavesson, Niklas
    et al.
    Boldt, Martin
    Davidsson, Paul
    Jacobsson, Andreas
    Learning to detect spyware using end user license agreements2011Ingår i: Knowledge and Information Systems, ISSN 0219-1377, Vol. 26, nr 2, s. 285-307Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The amount of software that hosts spyware has increased dramatically. To avoid legal repercussions, the vendors need to inform users about inclusion of spyware via end user license agreements (EULAs) during the installation of an application. However, this information is intentionally written in a way that is hard for users to comprehend. We investigate how to automatically discriminate between legitimate software and spyware associated software by mining EULAs. For this purpose, we compile a data set consisting of 996 EULAs out of which 9.6% are associated to spyware. We compare the performance of 17 learning algorithms with that of a baseline algorithm on two data sets based on a bag-of-words and a meta data model. The majority of learning algorithms significantly outperform the baseline regardless of which data representation is used. However, a non-parametric test indicates that bag-of-words is more suitable than the meta model. Our conclusion is that automatic EULA classification can be applied to assist users in making informed decisions about whether to install an application without having read the EULA. We therefore outline the design of a spyware prevention tool and suggest how to select suitable learning algorithms for the tool by using a multi-criteria evaluation approach.

  • 38. Lavesson, Niklas
    et al.
    Davidsson, Paul
    Boldt, Martin
    Jacobsson, Andreas
    Spyware Prevention by Classifying End User License Agreements2008Ingår i: New Challenges in Applied Intelligence Technologies / [ed] Nguyen, Ngoc Thanh; Katarzyniak, Radoslaw, Berlin / Heidelberg: Springer , 2008, s. 373-382Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [sv]

    Vi undersöker hypotesen att det är möjligt att via slutanvändarlicensen detektera om en mjukvaruapplikation innehåller spionprogram eller ej. Vi applicerar 15 inlärningsalgoritmer på en datamängd som innehåller 100 klassificerade slutanvändarlicenser. Resultaten visar att 13 algoritmer är signifikant mer korrekta än slumpvis gissning. Vi drar därför slutsatsen att hypotesen skall accepteras. Baserat på dessa resultat presenterar vi ett nytt verktyg som kan användas för att förhindra installationen av spionprogram genom att automatiskt pausa mjukvaruinstallationer och klassificera slutanvändarlicensen för att ge användaren chansen att göra ett upplyst val om att avbryta eller fortsätta installation. Vi diskuterar positiva och negativa aspekter med denna preventionsansats och föreslår en metod för att utvärdera kandidatalgoritmer för en framtida implementation.

  • 39. Lavesson, Niklas
    et al.
    Davidsson, Paul
    Boldt, Martin
    Jacobsson, Andreas
    Spyware Prevention by Classifying End User License Agreements2008Konferensbidrag (Refereegranskat)
    Abstract [en]

    We investigate the hypothesis that it is possible to detect from the End User License Agreement (EULA) if the associated software hosts spyware. We apply 15 learning algorithms on a, data set consisting of 100 applications with classified EULAs. The results show that 13 algorithms are significantly more accurate than random guessing. Thus, we conclude that the hypothesis can be accepted. Based on the results, we present a novel tool that can be used to prevent spyware by automatically halting application installers and classifying the EULA, giving users the opportunity to make an informed choice about whether to continue with the installation. We discuss positive and negative aspects of this prevention approach and suggest a method for evaluating candidate algorithms for a future implementation.

  • 40. Olsson, Jens
    et al.
    Boldt, Martin
    Computer Forensic Timeline Visualization Tool2009Ingår i: Digital Investigation. The International Journal of Digital Forensics and Incident Response, ISSN 1742-2876, E-ISSN 1873-202X, Vol. 6, nr Supplement 1, s. 78-87Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Computer Forensics is mainly about investigating crime where computers have been involved. There are many tools available to aid the investigator with this task. We have created a prototype of a new type of tool called CyberForensic TimeLab where all evidence is indexed by their time variables and plotted on a timeline. We believed that this way of visualizing the evidence allows the investigators to find coherent evidence faster and more intuitively. We have performed a user test where a group of people has evaluated our prototype tool against a modern commercial computer forensic tool and the results of this preliminary test are very promising. The results show that users completed the task in shorter time, with greater accuracy and with less errors using CyberForensic TimeLab. The subjects also experienced that the prototype were more intuitive to use and that it allowed them to easier locate evidence that was coherent in time.

  • 41. Persson, Jan A.
    et al.
    Davidsson, Paul
    Boldt, Martin
    Carlsson, Bengt
    Fiedler, Markus
    Evaluation of Road User Charging Systems: The Swedish Case2007Konferensbidrag (Refereegranskat)
  • 42.
    Shahzad, Raja Khurram
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Mehwish, Fatima
    Lavesson, Niklas
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Consensus decision making in random forests2015Ingår i: Revised Selected Papers of the First International Workshop on Machine Learning, Optimization, and Big Data, 2015, Vol. 9432, s. 347-358Konferensbidrag (Refereegranskat)
    Abstract [en]

    The applications of Random Forests, an ensemble learner, are investigated in different domains including malware classification. Random Forests uses the majority rule for the outcome, however, a decision from the majority rule faces different challenges such as the decision may not be representative or supported by all trees in Random Forests. To address such problems and increase accuracy in decisions, a consensus decision making (CDM) is suggested. The decision mechanism of Random Forests is replaced with the CDM. The updated Random Forests algorithm is evaluated mainly on malware data sets, and results are compared with unmodified Random Forests. The empirical results suggest that the proposed Random Forests, i.e., with CDM performs better than the original Random Forests.

1 - 42 av 42
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf