, Site officiel : http://www.scadabr.com.br/ Liste de publications IV.5 Article de revue ? Pedro Merino Laso Dataset of anomalies and malicious acts in a cyber-physical subsystem, ScadaBR est un logiciel libre qui permet le contrôle et supervision de systèmes industriels, pp.186-191, 2017.

P. M. Laso, D. Brosset, and J. Puentes, Monitoring approach of cyberphysical systems by quality measures, IV.6 Articles de International Conference on Sensor Systems and Software, pp.105-117, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01609035

?. Pedro, M. Laso, D. Brosset, and J. Puentes, Analysis of quality measurements to categorize anomalies in sensor systems, Proceedings of computing conference 2017, pp.1330-1338, 2017.

. Bibliographie, A. Andronicus, . Akinyelu, O. Aderemi, and . Adewumi, Classification of phishing email using random forest machine learning technique, Journal of Applied Mathematics, issue.2

A. Arning, R. Agrawal, and . Et-prabhakar-raghavan, A linear method for deviation detection in large databases, KDD

B. Abraham and A. Chuang, Outlier Detection and Time Series Modeling, Technometrics, vol.81, issue.2
DOI : 10.1214/aos/1176350027

. Accenture, Cost of cyber crime study : Insights on the security investments that make a difference, 2017.

C. Charu and . Aggarwal, On abnormality detection in spuriously populated data streams, Proceedings of the 2005 SIAM International Conference on Data Mining, pp.80-91, 2005.

S. Ardalan, Kalman parametric and nonparametric system identification applied to echo cancellation Center for communications and signal processing, 1983.

B. Barnett and A. Crapo, A semantic model for cyber security, Proceedings of the Fifth Grid-Interop Forum. Gridwise Architectural Council, 2011.

[. Baheti and H. Gill, Cyber-physical systems. The impact of control technology

, BIBLIOGRAPHIE

L. Anna, E. Buczak, and . Guven, A survey of data mining and machine learning methods for cyber security intrusion detection, IEEE Communications Surveys & Tutorials, vol.8, issue.1 2

W. Joel, C. Branch, B. Giannella, R. Szymanski, and . Wolff, In-network outlier detection in wireless sensor networks. Knowledge and information systems, pp.2-3

J. Richard, . Bolton, and J. David, Unsupervised profiling methods for fraud detection. Credit Scoring and Credit Control VII, pp.235-255, 2001.

[. Brause, M. Langsdorf, and . Hepp, Neural data mining for credit card fraud detection, Proceedings 11th International Conference on Tools with Artificial Intelligence, 1999.
DOI : 10.1109/TAI.1999.809773

P. Donald, . Ballou, L. Harold, and . Pazer, Modeling data and process quality in multi-input, multi-output information systems, Management science, vol.31, issue.2, pp.150-162, 1985.

C. Batini and M. Scannapieco, Data Quality : Concepts, Methodologies and Techniques (Data-Centric Systems and Applications).S p r i n g e r -V e r l a g, 2006.

R. R. , R. Barbosa, R. Sadre, and A. Pras, Towards periodicity based anomaly detection in scada networks, Emerging Technologies & Factory Automation (ETFA), 2012 IEEE 17th Conference on, pp.1-4
DOI : 10.1109/etfa.2012.6489745

, IEEE, 2012.

R. R. , R. Barbosa, R. Sadre, and A. Pras, Flow whitelisting in scada networks, International journal of critical infrastructure protection, vol.6, issue.3, pp.150-158, 2013.
DOI : 10.1016/j.ijcip.2013.08.003
URL : http://vbn.aau.dk/files/168630429/index.pdf

D. Bailey and E. Wright, Practical SCADA for Industry. Newnes, 2003.

[. Belikovetsky, M. Yampolskiy, J. Toh, and . Elovici, dr0wned-cyber-physical attack with additive manufacturing

V. Chandola, A. Banerjee, . Et-vipin, and . Kumar, Anomaly Detection, ACM computing surveys (CSUR), vol.4, issue.4
DOI : 10.1007/s10115-006-0020-z

[. Cuppens, N. Cuppens-boulahia, and J. Garcia-alfaro, Detection and removal of firewall misconfiguration, Proceedings of the 2005 IASTED International Conference on Communication, Network and Information Security

, Center for Strategic and International Studies Net losses : Estimating the global cost of cybercrime. economic impact of cybercrime ii, 2014.

C. Kenneth, . Cox, G. Stephen, . Eick, J. Graham et al., Brief application description ; visual data mining : Recognizing telephone calling fraud, Data Mining and Knowledge Discovery, vol.1, issue.2, pp.2-2

Z. Chen, Data mining and uncertain reasoning : an integrated approach, 2001.

Y. Cao, H. He, H. Man, and X. Shen, Integration of selforganizing map (som) and kernel density estimation (kde) for network intrusion detection, SPIE Europe Security+ Defence, pp.74800-74800

, International Society for Optics and Photonics, 2009.

W. William and . Cohen, Fast effective rule induction, Proceedings of the twelfth international conference on machine learning

, Eu transport in figures. statistical pocketbook 2016, 2016.

[. Chatzigiannakis, S. Papavassiliou, M. Grammatikou, and . Maglaris, Hierarchical Anomaly Detection in Distributed Large-Scale Sensor Networks, 11th IEEE Symposium on Computers and Communications (ISCC'06), 2006.
DOI : 10.1109/ISCC.2006.1691116

K. [. Chandrasekhar and . Raghuveer, Intrusion detection technique by using k-means, fuzzy neural network and SVM classifiers, 2013 International Conference on Computer Communication and Informatics, pp.1-7, 2013.
DOI : 10.1109/ICCCI.2013.6466310

B. Kim, Y. Clark-carliss, and . Baldwin, Design Rules : The power of modularity, 2000.

, BIBLIOGRAPHIE

L. Cai and Y. Zhu, The Challenges of Data Quality and Data Quality Assessment in the Big Data Era, Data Science Journal, vol.14, issue.0
DOI : 10.5334/dsj-2015-002

A. Batini-de, A methodology for data quality assessment on financial data, Studies in Communication Sciences

S. Dua and X. Du, Data mining and machine learning in cybersecurity, 2016.
DOI : 10.1201/b10867

, Direction centrale de la sécurité des systèmes d'information. Menaces sur les systèmes informatiques guide num 650, 2006.

F. Edgeworth and . Xli, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol.23, issue.143, pp.364-375, 1887.
DOI : 10.1080/14786448708628471

P. Larry and . English, Improving Data Warehouse and Business Information Quality : Methods for Reducing Costs and Increasing Profits, 1999.

E. Eskin, Anomaly detection over noisy data using learned probability distributions, Proceedings of the International Conference on Machine Learning.C i t e s e e r

, Federal Ministery of Interior Cyb er security strategy for germany, 2011.

N. Falliere, O. Liam, E. Murchu, and . Chien, W32. stuxnet dossier. White paper, Symantec Corp., Security Response

[. Feng, Q. Zhang, G. Hu, and J. X. Huang, Mining network data for intrusion detection through combining SVMs with ant colony networks, Future Generation Computer Systems, vol.37
DOI : 10.1016/j.future.2013.06.027

S. Mohinder, . Grewal, P. Angus, and . Andrews, Applications of kalman filtering in aerospace 1960 to the present [historical perspectives], IEEE Control Systems, vol.30, issue.3, pp.69-78, 2010.

[. Goh, S. Adepu, K. Nazir-junejo, and A. Mathur, A Dataset to Support Research in the Design of Secure Water Treatment Systems
DOI : 10.1016/S1389-1286(00)00139-0

, The 11th International Conference on Critical Information Infrastructures Security, 2016.

J. Guo and F. Liu, Automatic data quality control of observations in wireless sensor network. Geoscience and Remote Sensing Letters, IEEE, vol.12, issue.4, pp.716-720, 2015.

]. R. Gsm-+-11, A. Gandhi, W. Sharma, W. Mahoney, Q. Sousan et al., Dimensions of cyber-attacks : Cultural, social, economic, and political. Technology and Society Magazine

M. Guerriero, P. Willett, S. Coraluppi, and C. Carthel, Radar/ais data fusion and sar tasking for maritime surveillance, Information Fusion 11th International Conference on, 2008.

E. Horvitz and M. Barry, Display of information for time-critical decision making, Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, pp.9-9

F. Hamilton, T. Berry, and T. Sauer, Ensemble Kalman Filtering without a Model, Physical Review X, vol.6, issue.1
DOI : 10.1073/pnas.1015753108
URL : http://link.aps.org/pdf/10.1103/PhysRevX.6.011021

W. Hu, Y. Liao, and R. Vemuri, Robust anomaly detection using support vector machines, Proceedings of the international conference on machine learning

J. David, . Hill, S. Barbara, . Minsker, and . Amir, Real-time bayesian anomaly detection for environmental sensor data, Proceedings of the Congress- International Association for Hydraulic Research,v olume32,page503.Cite- seer, 2007.

. Hoa-+-15-]-nutan-farah-haq, M. Abdur-rahman-onik, K. Avishek, M. Hridoy, M. Rafni et al., Application of machine learning approaches in intrusion detection system : a survey, International Journal of Advanced Research in Artificial Intelligence

R. [. Ilgun, P. A. Kemmerer, and . Porras, State transition analysis: a rule-based intrusion detection approach, IEEE Transactions on Software Engineering, vol.21, issue.3
DOI : 10.1109/32.372146

[. Incose, Systems engineering handbook. A guide for system life cycle processes and activities, INCOSE, 2007.

[. Ansi, , p.95

[. Iso, Iec 27001 : 2005. information technology. security techniques. information security management systems. requirements, 2005.

A. Joudaki, B. Rashidian, M. Minaei-bidgoli, B. Mahmoodi, M. Geraili et al., Using Data Mining to Detect Health Care Fraud and Abuse: A Review of Literature, Global Journal of Health Science, vol.7, issue.1
DOI : 10.5539/gjhs.v7n1p194
URL : http://www.ccsenet.org/journal/index.php/gjhs/article/download/37879/22169

P. Argyro and . Karanasiou, The changing face of protests in the digital age : on occupying cyberspace and distributed-denial-of-services (ddos) attacks, International Review of Law Computers & Technology, vol.8, issue.2 1

G. Kheir, H. Blanc, J. Debar, D. Garcia-alfaro, and . Yang, Automated Classification of C&C Connections Through Malware URL Clustering, IFIP International Information Security Conference, pp.2-5, 2015.
DOI : 10.1007/978-3-319-18467-8_17
URL : https://hal.archives-ouvertes.fr/hal-01255089

F. Knorn, J. Douglas, and . Leith, Adaptive Kalman Filtering for anomaly detection in software appliances, IEEE INFOCOM 2008, IEEE Conference on Computer Communications Workshops, pp.1-6, 2008.
DOI : 10.1109/INFOCOM.2008.4544581
URL : http://eprints.maynoothuniversity.ie/1751/1/HamiltonKalman.pdf

A. Klein and W. Lehner, How to optimize the quality of sensor data streams ICC- GI'09. Fourth International Multi-Conference on, Computing in the Global Information Technology, 2009.
DOI : 10.1109/iccgi.2009.10

A. Klein and W. Lehner, Representing Data Quality in Sensor Data Streaming Environments, Journal of Data and Information Quality, vol.1, issue.2, p.10, 2009.
DOI : 10.1145/1577840.1577845

C. John and . Knight, Safety critical systems : challenges and directions, Software Engineering Proceedings of the 24rd International Conference on, 2002.

M. Gunupudi-rajesh-kumar, G. Nimmala, and . Narasimha, An approach for intrusion detection using novel gaussian based kernel function, Journal of Universal Computer Science, vol.2, issue.2 4

L. Ronald and . Krutz, Securing SCADA systems, 2005.

[. Kobayashi, L. Donald, and . Simon, Hybrid kalman filter approach for aircraft engine in-flight diagnostics : Sensor fault detection case Journal of engineering for gas turbines and power, pp.7-11

S. Ron, G. Kenett, and . Shmueli, Information Quality : The Potential of Data and Analytics to Generate Knowledge, 2016.

J. Lee, B. Bagheri, and H. Kao, A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems, Manufacturing Letters, vol.3, issue.2, pp.1-8
DOI : 10.1016/j.mfglet.2014.12.001

S. Liu and B. Cheng, Cyberattacks : Why, what, who, and how. IT professional, pp.1-4
DOI : 10.1109/mitp.2009.46

A. Lakhina, M. Crovella, and C. Diot, Mining anomalies using traffic feature distributions, In ACM SIGCOMM Computer Communication Review, vol.5
DOI : 10.1145/1080091.1080118
URL : https://open.bu.edu/bitstream/2144/1823/1/2005-002-anomaly-mining.pdf

J. Zhong, L. Ali, and A. Ghorbani, Improved competitive learning neural networks for network intrusion and fraud detection, Neurocomputing, vol.75, issue.1, pp.135-145, 2012.

W. Lin, C. Shih-wen-ke, and . Tsai, CANN: An intrusion detection system based on combining cluster centers and nearest neighbors, Knowledge-Based Systems, vol.78
DOI : 10.1016/j.knosys.2015.01.009

, Knowledge-based systems

D. Loshin, Enterprise Knowledge Management. The Data Quality Approach, 2001.

Y. W. Lee, D. M. Strong, B. K. Kahn, and R. Y. Wang, AIMQ: a methodology for information quality assessment, Information & Management, vol.40, issue.2, pp.133-146, 2002.
DOI : 10.1016/S0378-7206(02)00043-5

Y. Liao and R. Vemuri, Use of k-nearest neighbor classifier for intrusion detection. Computers & security
DOI : 10.1016/s0167-4048(02)00514-x
URL : http://wwwcsif.cs.ucdavis.edu/~liaoy/research/science.pdf

B. Lee and D. Xiang, Information-theoretic measures for anomaly detection, Security and Privacy Proceedings. 2001 IEEE Symposium, 2001.

J. C. Mingers, An evaluation of theories of information with regard to the semantic and pragmatic aspects of information systems, Systems Practice, vol.3, issue.July, pp.187-209, 1996.
DOI : 10.1007/978-1-4471-3265-3

P. Merino-laso, D. Brosset, and J. Puentes, Monitoring Approach of Cyber-Physical Systems by Quality Measures, International Conference on Sensor Systems and Software
DOI : 10.1002/asi.20508
URL : https://hal.archives-ouvertes.fr/hal-01609035

[. Laso, D. Brosset, and J. Puentes, Analysis of quality measurements to categorize anomalies in sensor systems, 2017 Computing Conference
DOI : 10.1109/SAI.2017.8252263
URL : https://hal.archives-ouvertes.fr/hal-01597458

P. Merino-laso, D. Brosset, and J. Puentes, Dataset of anomalies and malicious acts in a cyber-physical subsystem, Data in Brief, vol.14, pp.186-191, 2017.
DOI : 10.1016/j.dib.2017.07.038
URL : https://hal.archives-ouvertes.fr/hal-01597298

[. Modicon and . Inc, Modicon. Modbus Protocol. Reference Guide, vol.1, pp.9-9

D. Moore, V. Paxson, S. Savage, C. Shannon, S. Staniford et al., Inside the slammer worm, IEEE Security & Privacy Magazine, vol.1, issue.4, pp.33-39, 2003.
DOI : 10.1109/MSECP.2003.1219056

A. Motro and P. Smets, Uncertainty management in information systems : from needs to solutions.S p r i n g e rS c i e n c e&B u s i n e s sM e d i a, pp.9-9
DOI : 10.1007/978-1-4615-6245-0

M. Seok-mok, S. Y. Sohn, . Et-yong-han, and . Ju, Random effects logistic regression model for anomaly detection, Expert Systems with Applications, vol.37, issue.10, pp.7162-7166, 2010.
DOI : 10.1016/j.eswa.2010.04.017

H. Thomas, Z. Morris, I. Thornton, and . Turnipseed, Industrial control system simulation and data logging for intrusion detection system research

, 7th Annual Southeastern Cyber Security Summit. Huntsvile, AL,2 0 1 5

F. Mazzarella, M. Vespe, A. Alessandrini, D. Tarchi, G. Aulicino et al., A novel anomaly detection approach to identify intentional AIS on-off switching, Expert Systems with Applications, vol.78, pp.110-123, 2017.
DOI : 10.1016/j.eswa.2017.02.011
URL : https://doi.org/10.1016/j.eswa.2017.02.011

E. Stuart, . Madnick, Y. Richard, . Wang, W. Yang et al., Overview and framework for data and information quality research, Journal of Data and Information Quality (JDIQ), vol.1, issue.1

, National Marine Electronics Association and others. Nmea 2000 standard for serial-data networking of marine electronic devices, 2004.

F. Naumann, From databases to information systems-information quality makes the difference, IQ

C. Caleb, . Noble, J. Diane, and . Cook, Graph-based anomaly detection, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

J. Nivethan and M. Papa, On the use of open-source firewalls in ics/scada systems Information Security Journal : A Global Perspective, pp.83-93, 2016.

L. Obrst, P. Chase, and R. Markeloff, Developing an ontology of the cyber security domain, STIDS

A. Oltramari, L. F. Cranor, J. Robert, . Walls, D. Patrick et al., Building an ontology of cyber security, STIDS, pp.5-9, 2014.

M. Petit, Approche spatiale pour la caractérisation du contexte d'exécution d'un système d'information ubiquitaire, Arts et Métiers ParisTech. IRENav -´ Ecole Navale, 2010.

E. Roland and . Pittman, Taxonomy of learning. A Brief Introduction, PediaPress, 2011.

L. Leo, Y. W. Pipino, R. Y. Lee, and . Wang,

, Commun. ACM,4, vol.5, issue.4

J. Puentes, J. Montagner, L. Lecornu, and J. Lähteenmäki, Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices
DOI : 10.1007/978-1-4614-4514-2_10
URL : https://hal.archives-ouvertes.fr/hal-01184500

, BIBLIOGRAPHIE

I. R. Bali, I. Troshani, and S. Goldberg, Pervasive Health Knowledge Management, Healthcare Delivery in the Information Age, pp.103-133, 2013.

J. Petit and S. E. Shladover, Potential cyberattacks on automated vehicles. Intelligent Transportation Systems, IEEE Transactions on, issue.99, pp.1-11, 2014.
DOI : 10.1109/tits.2014.2342271

[. Pwc, The global state of information security survey 2015 -managing cyber risks in an interconnected world, p.2015

[. Pwc, The global state of information security survey 2016 -turnaround and transformation in cybersecurity, p.2016

[. Pwc, The global state of information security survey 2017 -how businesses are addressing iot risks, p.2017

L. Galina, E. Rogova, and . Bosse, Information quality in information fusion, 13th Conference on, 2010.

J. Rrushi and R. Campbell, Detecting Cyber Attacks On Nuclear Power Plants, Critical Infrastructure Protection II
DOI : 10.1007/978-0-387-88523-0_4
URL : https://link.springer.com/content/pdf/10.1007%2F978-0-387-88523-0_4.pdf

E. Jennifer, . Rowley, J. Richard, and . Hartley, Organizing knowledge : an introduction to managing access to information, 2008.

[. Rubio-hernán, L. De-cicco, and . Et-joaquín-garcía-alfaro, Revisiting a Watermark-Based Detection Scheme to Handle Cyber-Physical Attacks, 2016 11th International Conference on Availability, Reliability and Security (ARES), pp.0-1
DOI : 10.1109/ARES.2016.2

[. Rajasegarar, C. Leckie, M. Palaniswami, C. James, and . Bezdek,

, Communication systems 10th IEEE Singapore International Conference on, 2006.

L. Galina, V. Rogova, and . Nimier, Reliability in information fusion : literature survey, Proceedings of the seventh international conference on information fusion

M. Shyu, S. Chen, K. Sarinnapakorn, and L. Chang,

A. , t i o ns c h e m eb a s e do np r i n c i p a lc o m p o n e n tc l a s s i fi e r, 2003.

L. Sha, S. Gopalakrishnan, X. Liu, and E. Q. Wang, Cyber-physical systems : A new frontier, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, pp.1-9, 2008.
DOI : 10.1007/978-0-387-88735-7_1
URL : http://www.cs.mcgill.ca/~xueliu/publications/2008_SUTC.pdf

]. C. Sha48 and . Shannon, A mathematical theory of communication, SIGMOBILE Mob. Comput. Commun. Rev, vol.5, issue.1, pp.3-55, 1948.

T. William and . Shaw, Cybersecurity for SCADA systems.P e n n w e l lb o o k s

K. [. Stubberud and . Kramer, Threat assessment for GPS navigation, 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, pp.287-292, 2014.
DOI : 10.1109/INISTA.2014.6873632

P. Smets, Imperfect information : Imprecision and uncertainty. In Uncertainty management in information systems, 1997.
DOI : 10.1007/978-1-4615-6245-0_8

B. Shah, H. Bhushan, and . Trivedi, Artificial Neural Network based Intrusion Detection System: A Survey, International Journal of Computer Applications, vol.39, issue.6, pp.13-18, 2012.
DOI : 10.5120/4823-7074

M. Scannapieco, A. Virgillito, C. Marchetti, M. Mecella, and R. Baldoni, The DaQuinCIS architecture: a platform for exchanging and improving data quality in cooperative information systems, Information Systems, vol.29, issue.7, pp.551-582, 2004.
DOI : 10.1016/j.is.2003.12.004
URL : http://www.dis.uniroma1.it/~midlab/articoli/SVMMB_IS.pdf

J. Sun, Y. Xie, H. Zhang, and C. Faloutsos, Less is More: Compact Matrix Decomposition for Large Sparse Graphs, Proceedings of the 2007 SIAM International Conference on Data Mining, pp.366-377, 2007.
DOI : 10.1137/1.9781611972771.33
URL : https://epubs.siam.org/doi/pdf/10.1137/1.9781611972771.33

, Sym17] Symantec. Istr. internet security threat report

K. Sha and S. Zeadally, Data Quality Challenges in Cyber-Physical Systems, Journal of Data and Information Quality, vol.6, issue.2-3
DOI : 10.1145/2018673.2018679

[. Todoran,

, J. Data and Information Quality, vol.61123, issue.2-3, pp.1-11, 2015.

[. Todoran, L. Lecornu, A. Khenchaf, and J. Caillec, Information quality evaluation in fusion systems, Information Fusion (FUSION), 2013 16th International Conference on
URL : https://hal.archives-ouvertes.fr/hal-00846769

[. Todoran, ´ Etude dynamique de la qualité de l'information et des données d'un système d'information complexe.P h D t h e s i s , T ´ e l ´ e c o m B r e tagne, 2014.

, Review of maritime transport 2016, United Nations, 2016.

Y. Richard and . Wang, A product perspective on total data quality management, Communications of the ACM,4, vol.11, issue.8, pp.5-8

Y. Wang, A multinomial logistic regression modeling approach for anomaly intrusion detection, Computers & Security, vol.24, issue.8, p.4
DOI : 10.1016/j.cose.2005.05.003

Y. Wang, W. Fu, P. Et-dharma, and . Agrawal, Gaussian versus Uniform Distribution for Intrusion Detection in Wireless Sensor Networks, IEEE Transactions on Parallel and Distributed systems
DOI : 10.1109/TPDS.2012.105
URL : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.338.1999&rep=rep1&type=pdf

[. Warrender, S. Forrest, and B. Pearlmutter, Detecting intrusions using system calls: alternative data models, Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344), pp.1-3, 1999.
DOI : 10.1109/SECPRI.1999.766910
URL : http://eprints.maynoothuniversity.ie/1418/1/DetectingIntrusions.pdf

P. Woodall-et-ajith-kumar and . Parlikad, A hybrid approach to assessing data quality, Proceedings of the 2010 international conference on information quality, pp.1-1

Y. Richard, D. M. Wang, and . Strong, Beyond accuracy : What data quality means to data consumers, J. Manage. Inf. Syst, vol.2, issue.1 4, pp.5-8

H. Wan, D. Yan, K. Li, L. Zhou, and . Zeng, Cyber-Physical Systems for Optimal Energy Management Scheme of Autonomous Electric Vehicle, The Computer Journal, vol.56, issue.8
DOI : 10.1093/comjnl/bxt043
URL : https://academic.oup.com/comjnl/article-pdf/56/8/947/1018808/bxt043.pdf

, The Computer Journal, issue.6 8