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  • 1.
    Ali Hamad, Rebeen
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Lundström, Jens
    JeCom Consulting, Halmstad, Sweden.
    Stability analysis of the t-SNE algorithm for human activity pattern data2018Conference paper (Refereed)
    Abstract [en]

    Health technological systems learning from and reacting on how humans behave in sensor equipped environments are today being commercialized. These systems rely on the assumptions that training data and testing data share the same feature space, and residing from the same underlying distribution - which is commonly unrealistic in real-world applications. Instead, the use of transfer learning could be considered. In order to transfer knowledge between a source and a target domain these should be mapped to a common latent feature space. In this work, the dimensionality reduction algorithm t-SNE is used to map data to a similar feature space and is further investigated through a proposed novel analysis of output stability. The proposed analysis, Normalized Linear Procrustes Analysis (NLPA) extends the existing Procrustes and Local Procrustes algorithms for aligning manifolds. The methods are tested on data reflecting human behaviour patterns from data collected in a smart home environment. Results show high partial output stability for the t-SNE algorithm for the tested input data for which NLPA is able to detect clusters which are individually aligned and compared. The results highlight the importance of understanding output stability before incorporating dimensionality reduction algorithms into further computation, e.g. for transfer learning.

  • 2.
    Cooney, Martin
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Ong, Linda
    I+ srl, Florence, Italy.
    Pashami, Sepideh
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Ashfaq, Awais
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Avoiding Improper Treatment of Dementia Patients by Care Robots2019Conference paper (Refereed)
    Abstract [en]

    The phrase “most cruel and revolting crimes” has been used to describe some poor historical treatment of vulnerable impaired persons by precisely those who should have had the responsibility of protecting and helping them. We believe we might be poised to see history repeat itself, as increasingly humanlike aware robots become capable of engaging in behavior which we would consider immoral in a human–either unknowingly or deliberately. In the current paper we focus in particular on exploring some potential dangers affecting persons with dementia (PWD), which could arise from insufficient software or external factors, and describe a proposed solution involving rich causal models and accountability measures: Specifically, the Consequences of Needs-driven Dementia-compromised Behaviour model (C-NDB) could be adapted to be used with conversation topic detection, causal networks and multi-criteria decision making, alongside reports, audits, and deterrents. Our aim is that the considerations raised could help inform the design of care robots intended to support well-being in PWD.

  • 3.
    Järpe, Eric
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    An Ising-Type Model for Spatio-Temporal Interactions2005In: Markov Processes and Related Fields, ISSN 1024-2953, Vol. 11, no 3, p. 535-552Article in journal (Refereed)
    Abstract [en]

    A model which possesses both spatial and time dependence is the Markov chain Markov field (see X. Guyon, 1995). Here inference about the parameter for spatio-temporal interaction of a special case of a Markov chain Markov field model is considered. A statistic which is minimal sufficient for the interaction parameter and its asympotic distribution are derived. A condition for stationarity of the sufficient statistic process and the stationary distribution are given. Likelihood based inference such as estimation, hypothesis testing and monitoring are briefly examined.

  • 4.
    Järpe, Eric
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Räkna med rester: Matematik att tillämpas inom kryptologi2013Book (Other academic)
    Abstract [sv]

    Ämnet kryptologi, dvs. kryptering, dekryptering och kodknäckning ­omfattar såväl matematik, dator­programmering som allmän finurlighet. Denna bok behandlar Caesarkrypto, substitutionskrypto, Vignèrekrypto, RSA-krypto och den bakom­liggande ­matematiken (ekvations­lösning, räkning med exponential­uttryck, resträkning, primtalsteori och rekursion) samt angränsande matematik (såsom kombinatorik, statisti­ska metoder för t.ex. detektering av krypterad kod och beräkning av överföringskvalitet). Boken innehåller även en så pass bred ­genomgång av elementär algebra (mängdlära, logik, trigono­metri, komplexa tal och rekurrensekva­tioner) och analys (funktioner i en variabel, derivata och integraler) att den kan användas vid inledande studier i matematik inom en mängd olika utbildningar. Boken syftar till att ge en introduktion till kryptologisk problem­lösning och visa på de stora synergieffekter som uppnås genom att tillämpa en väl­balanserad ­kombination av grundläggande matematik och elementär programmering inom området. Förhoppningen är också att läsaren, sporrad av de nyvunna insikterna om kryptologi, lockas till vidare kunskapsfördjupning. Boken vänder sig i första hand till blivande IT-forensiker som kan behöva kompetensen att kryptera och knäcka krypton i sin yrkesroll men som inte har en omfattande matematisk förkunskap. Den ­vänder sig även till studenter på landets tekniska högskolor och ­universitet.

  • 5.
    Järpe, Eric
    Department of Statistics, Göteborg University, Göteborg, Sweden.
    Surveillance of the Interaction Parameter of the Ising Model1999In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 28, no 12, p. 3009-3027Article in journal (Refereed)
    Abstract [en]

    Surveillance to detect changes of spatial patterns is of interest in many areas such as environmental control and regional analysis. Here the interaction parameter of the Ising model, is considered. A minimal sufficient statistic and its asymptotic distribution are used. It is demonstrated that the convergence to normal distribution is rapid. The main result is that when the lattice is large, all approximations are better in several respects. It is shown that, for large lattice sizes, earlier results on surveillance of a normally distributed random variable can be used in cases of most interest. The expected delay of alarm at a fixed level of false alarm probability is examined for some examples. Copyright © 1999 by Marcel Dekker, Inc.

  • 6.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Visit to care center Angeles Cobo Lopez, Alcaudete, Andalucia, Spain: A secondment within the REMIND project2019Report (Other (popular science, discussion, etc.))
  • 7.
    Järpe, Eric
    et al.
    Department of Statistics, Gothenburg University, Gothenburg, Sweden.
    Wessman, Peter
    Department of Statistics, Gothenburg University, Gothenburg, Sweden.
    Some Power Aspects of Methods for Detecting Different Shifts in the Mean2000In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 29, no 2, p. 633-646Article in journal (Refereed)
    Abstract [en]

    We study, by means of simulations, the performance of the Shewhart method, the Cusum method, the Shiryaev-Roberts method and the likelihood ratio method in the case when the true shift differs from the shift for which the methods are optimal. The methods are compared for a fixed expected time until false alarm. The comparisons are made with respect to some measures associated with power such as probability of alarm when the change occurs immediately, expected delay of true alarm and predictive value of an alarm. Copyright © 2000 by Marcel Dekker, Inc.

  • 8.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Detecting and exploring deviating behaviour of people in their own homesManuscript (preprint) (Other academic)
    Abstract [en]

    A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. It is believed that such systems will be very important in ambient assisted living services. Three types of deviations are considered in this work: deviation in activity intensity, deviation in time and deviation in space. Detection of deviations in activity intensity is formulated as the on-line quickest detection of a parameter shift in a sequence of independent Poisson random variables. Random forests trained in an unsupervised fashion are used to learn the spatial and temporal structure of data representing normal behaviour and are thereafter utilised to find deviations.The experimental investigations have shown that the Page and Shiryaev change-point detection methods are preferable in terms of expected delay of motivated alarm. Interestingly only a little is lost when the methods are specified with estimated intensity parameters rather than the true intensity values which are not available in a real situation. As to the spatial and temporal deviations, they can be revealed through analysis of a 2D map of high dimensional data. It was demonstrated that such a map is stable in terms of the number of clusters formed. We have shown that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree.

  • 9.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Detecting and exploring deviating behaviour of smart home residents2016In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 55, p. 429-440Article in journal (Refereed)
    Abstract [en]

    A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.

    Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.

    The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection-this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. © 2016 Elsevier Ltd. All rights reserved.

  • 10.
    Lundström, Jens
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Synnott, Jonathan
    Ulster University, Jordanstown, United Kingdom.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nugent, Christopher
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Ulster University, Jordanstown, United Kingdom.
    Smart Home Simulation using Avatar Control and Probabilistic Sampling2015In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Piscataway, NJ: IEEE Press, 2015, p. 336-341Conference paper (Refereed)
    Abstract [en]

    Development, testing and validation of algorithms for smart home applications are often complex, expensive and tedious processes. Research on simulation of resident activity patterns in Smart Homes is an active research area and facilitates development of algorithms of smart home applications. However, the simulation of passive infrared (PIR) sensors is often used in a static fashion by generating equidistant events while an intended occupant is within sensor proximity. This paper suggests the combination of avatar-based control and probabilistic sampling in order to increase realism of the simulated data. The number of PIR events during a time interval is assumed to be Poisson distributed and this assumption is used in the simulation of Smart Home data. Results suggest that the proposed approach increase realism of simulated data, however results also indicate that improvements could be achieved using the geometric distribution as a model for the number of PIR events during a time interval. © IEEE 2015

  • 11.
    Nilsson, Emil
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), MPE-lab.
    Nilsson, Björn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Embedded Systems (CERES).
    Järpe, Eric
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), MPE-lab.
    A Pharmaceutical Anti-counterfeiting Method Using Time Controlled Numeric Tokens2011In: 2011 IEEE International Conference on RFID-Technologies and Applications, Piscataway, NJ: IEEE Press, 2011, p. 335-339Conference paper (Refereed)
    Abstract [en]

    An anti-counterfeit and authentication method usingtime controlled numeric tokens enabling a secure logistic chain ispresented. Implementation of the method is illustrated with apharmaceutical anti-counterfeit system. The method uses activeRFID technology in combination with product seal. Authenticityis verified by comparing time controlled ID-codes, i.e. numerictokens, stored in RFID tags and by identical numeric tokensstored in a secure database. The pharmaceutical products areprotected from the supplier to the pharmacist, with thepossibility to extend the authentication out to the end customer.The ability of the method is analyzed by discussion of severalpossible scenarios. It is shown that an accuracy of 99.9% tellingthe customer she has an authentic product is achieved by the useof 11-bit ID-code strings.

  • 12.
    Premaratne, Hemakumar Lalith
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Järpe, Eric
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bigun, Josef
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Lexicon and hidden Markov model-based optimisation of the recognised Sinhala script2006In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 27, no 6, p. 696-705Article in journal (Refereed)
    Abstract [en]

    The Brahmi descended Sinhala script is used by 75% of the 18 million population in Sri Lanka. To the best of our knowledge, none of the Brahmi descended scripts used by hundreds of millions of people in South Asia, possess commercial OCR products. In the process of implementation of an OCR system for the printed Sinhala script which is easily adoptable to similar scripts [Premaratne, L., Assabie, Y., Bigun, J., 2004. Recognition of modification-based scripts using direction tensors. In: 4th Indian Conf. on Computer Vision, Graphics and Image Processing (ICVGIP2004), pp. 587–592]; a segmentation-free recognition method using orientation features has been proposed in [Premaratne, H.L., Bigun, J., 2004. A segmentation-free approach to recognise printed Sinhala script using linear symmetry. Pattern Recognition 37, 2081–2089]. Due to the limitations in image analysis techniques the character level accuracy of the results directly produced by the proposed character recognition algorithm saturates at 94%. The false rejections from the recognition algorithm are initially identified only as ‘missing character positions’ or ‘blank characters’. It is necessary to identify suitable substitutes for such ‘missing character positions’ and optimise the accuracy of words to an acceptable level. This paper proposes a novel method that explores the lexicon in association with the hidden Markov models to improve the rate of accuracy of the recognised script. The proposed method could easily be extended with minor changes to other modification-based scripts consisting of confusing characters. The word-level accuracy which was at 81.5% is improved to 88.5% by the proposed optimisation algorithm.

  • 13.
    Rögnvaldsson, Thorsteinn
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Norrman, Henrik
    Halmstad University, School of Information Technology.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Estimating p-Values for Deviation Detection2014In: Proceedings: 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems SASO 2014 / [ed] Randall Bilof, Los Alamitos, CA: IEEE Computer Society, 2014, p. 100-109Conference paper (Refereed)
    Abstract [en]

    Deviation detection is important for self-monitoring systems. To perform deviation detection well requires methods that, given only "normal" data from a distribution of unknown parametric form, can produce a reliable statistic for rejecting the null hypothesis, i.e. evidence for devating data. One measure of the strength of this evidence based on the data is the p-value, but few deviation detection methods utilize p-value estimation. We compare three methods that can be used to produce p-values: one class support vector machine (OCSVM), conformal anomaly detection (CAD), and a simple "most central pattern" (MCP) algorithm. The SVM and the CAD method should be able to handle a distribution of any shape. The methods are evaluated on synthetic data sets to test and illustrate their strengths and weaknesses, and on data from a real life self-monitoring scenario with a city bus fleet in normal traffic. The OCSVM has a Gaussian kernel for the synthetic data and a Hellinger kernel for the empirical data. The MCP method uses the Mahalanobis metric for the synthetic data and the Hellinger metric for the empirical data. The CAD uses the same metrics as the MCP method and has a k-nearest neighbour (kNN) non-conformity measure for both sets. The conclusion is that all three methods give reasonable, and quite similar, results on the real life data set but that they have clear strengths and weaknesses on the synthetic data sets. The MCP algorithm is quick and accurate when the "normal" data distribution is unimodal and symmetric (with the chosen metric) but not otherwise. The OCSVM is a bit cumbersome to use to create (quantized) p-values but is accurate and reliable when the data distribution is multimodal and asymmetric. The CAD is also accurate for multimodal and asymmetric distributions. The experiment on the vehicle data illustrate how algorithms like these can be used in a self-monitoring system that uses a fleet of vehicles to conduct deviation detection without supervisi- n and without prior knowledge about what is being monitored. © 2014 IEEE.

  • 14.
    Sandberg, Mikael
    et al.
    Halmstad University, School of Social and Health Sciences (HOS), Center for Social Analysis (CESAM), Centre for Studies of Political Science, Communication and Media (CPKM).
    Florén, Henrik
    Halmstad University, School of Business and Engineering (SET), Centre for Innovation, Entrepreneurship and Learning Research (CIEL).
    Halila, Fawzi
    Halmstad University, School of Business and Engineering (SET), Centre for Innovation, Entrepreneurship and Learning Research (CIEL).
    Hörte, Sven-Åke
    Halmstad University, School of Business and Engineering (SET), Centre for Innovation, Entrepreneurship and Learning Research (CIEL).
    Järpe, Eric
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Evolution of Green Innovation in Sweden: Models, Management, Policies2011In: Evolution of Green Innovation in Sweden: Models, Management, Policies, 2011Conference paper (Other academic)
    Abstract [en]

    Quantitative analysis of the evolution of innovations at national systems level is not alwayspossible due to the lack of reliable, comprehensive and adequate data sets. Therefore, managerialpractice among organizations as well as policy decision making are often myopic anduninformed about actual dynamics.In the Swedish case, there are promising data sets, even if the adequacy of existing variabledefinitions needs to be explored and debated. Official data collected by the central statisticsauthority SCB (Statistics Sweden) includes several potentially relevant variables on all privateand public organizations in Sweden and their employees. These data are compiled into timeseries for a number of years which allows for longitudinal analysis. Data can also be mergedwith other data sets on the environmental goods and services sector and energy consumption dataand therefore allow for a detailed “demographic” or “population ecology” analysis ofenvironmentally oriented or friendly innovation since at least 2003. Halmstad University hasrecently gained full access to these data.In this paper, these databases are described in some detail. Problems of definitions andmeasurement are particularly discussed, and some initial descriptive statistics are presented.Further, the paper advocates the use of models inspired by population ecology and demographyin analyzing existing data. In particular it is suggested that interactive diffusion models mayenhance the understanding of the evolution of green innovations and their dynamics. It is alsosuggested that multi-level regression analysis is applicable in estimating the power of factors thatbring progress to the “greening” of the Swedish innovation system.Together, such models are potentially useful in forecasting the development of innovationsystems. The models can also be used in generating, testing by simulating and thus evaluatingapproaches to management of innovation and innovation policy implementation. A dynamicunderstanding of the “greening” of the innovation system is a critical asset in the development oftools to be used for continuous improvements in both policy making and the management ofinnovation in organizations.

  • 15.
    Sandberg, Mikael
    et al.
    Halmstad University, School of Business and Engineering (SET), Centre for Innovation, Entrepreneurship and Learning Research (CIEL).
    Florén, Henrik
    Halmstad University, School of Business and Engineering (SET), Centre for Innovation, Entrepreneurship and Learning Research (CIEL).
    Järpe, Eric
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    The Greening of the Swedish Innovation System: Exploring Official Registry Data2011Conference paper (Other academic)
    Abstract [en]

    Most countries aim to transform towards becoming greener societies. In parallel, many companies struggle with the question of how to build more sustainable operations while at the same time sustaining or developing their competitive advantage.  Research has, up until today, however, largely failed to provide solid explanations for how to achieve these aims, from which policy and managerial decision-making can deduced. One reason for this failure is that quantitative analysis of “green” innovation at national systems level is not always possible due to the lack of reliable, comprehensive and adequate data sets. In the Swedish case, there are promising data sets, even if one always can debate the adequacy of existing variable definitions. Official data collected by Statistics Sweden (SCB) includes several interesting variables on all private and public organizations in Sweden and all employees, compiled into time series for a number of years. These can be merged with other data sets on the environmental goods and services sector and energy consumption data and therefore allow for a detailed “demographic” or “population ecology” analysis of environmentally oriented or friendly innovation since at least 2003. In this paper, these databases are described in some detail. Problems of definitions and measurement are particularly discussed. Initial explorations describe the shift from fossil to non-fossil energy sources in the Swedish innovation system. Further, we also suggest some models inspired by demography and population ecology and also multi-level models. In particular it is suggested that diffusion models could be applied, including models in which diffusion processes interact in micro-level systems. It is suggested to apply multi-level regression analysis in order to estimate the power of factors affecting the “greening” of Swedish innovation system.

  • 16.
    Weckstén, Mattias
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Frick, Jan
    Halmstad University.
    Sjostrom, Andreas
    Halmstad University.
    Järpe, Eric
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    A Novel Method for Recovery from Crypto Ransomware Infections2016In: 2016 2nd IEEE International Conference on Computer and Communications, ICCC 2016 - Proceedings, New York: IEEE, 2016, p. 1354-1358Conference paper (Refereed)
    Abstract [en]

    Extortion using digital platforms is an increasing form of crime. A commonly seen problem is extortion in the form of an infection of a Crypto Ransomware that encrypts the files of the target and demands a ransom to recover the locked data. By analyzing the four most common Crypto Ransomwares, at writing, a clear vulnerability is identified; all infections rely on tools available on the target system to be able to prevent a simple recovery after the attack has been detected. By renaming the system tool that handles shadow copies it is possible to recover from infections from all four of the most common Crypto Ransomwares. The solution is packaged in a single, easy to use script. © 2016 IEEE.

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