Adepoju O, Wosowei J, Lawte S, Jaiman H (2019) Comparative evaluation of credit card fraud detection using machine learning techniques. In: Proceedings of the 2019 Global conference for Advancement in Technology (GCAT), Bangalore, India, https://doi.org/10.1109/GCAT47503.2019.8978372
Almutairi A, Wheeler JP, Slutzky DL, Lambert JH (2019) Integrating stakeholder mapping and risk scenarios to improve resilience of cyber-physical-social networks. Risk Anal. https://doi.org/10.1111/risa.13292
Article
Google Scholar
Andrews DJ, Polmateer TL, Wheeler JP, Slutzky DL, Lambert JH (2020) Enterprise risk and resilience of electric-vehicle charging infrastructure and the future mobile power grid. Curr Sustain Renew Energy Rep Transport 7:9–15. https://doi.org/10.1007/s40518-020-00144-6
CAS
Article
Google Scholar
ANFAVEA (2013–2017) Cars and light trucks
Anton-Haro C, Dohler M (2014) Machine-to-machine (M2M) communications, architecture performance and applications. Elsevier, Amsterdam, pp 1–23
Google Scholar
Begian C, Kettani H (2020) Analysis of fuel pump skimming devices. In: Proceedings of the 2020 the 4th International Conference on Information System and Data Mining, pp. 157–162. https://doi.org/10.1145/3404663.3406874
Beteto A (2019) Proposta de automação para controle da vazão de combustíveis líquidos como nova abordagem de giscalização para o gerenciamento de riscos em postos revendedores, University of São Paulo, Master thesis
Bundi D, Maraga MJ (2020) Effects of cybercrime on oil and gas industry. Glob Sci J 8(6) ISSN 2320-9186
Carrasco RS, Silcilia-Urban MA (2020) Evaluation of deep neural networks for reduction of credit card fraud alerts. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3026222
Article
Google Scholar
Catota FE, Morgan MG, Sicker DC (2019) Cybersecurity education in a developing nation: the Ecuadorian environment. J Cybersecurity. https://doi.org/10.1093/cybsec/tyz001
Article
Google Scholar
Collier ZA, Lambert JH (2019) Principles and methods of model validation for model risk reduction. IEEE Environ Syst Decis 39:146–153. https://doi.org/10.1007/s10669-019-09723-5
Article
Google Scholar
Collier ZA, Linkov I, DiMase D, Walters S, Tehranipoor M, Lambert JH (2014) Cybersecurity standards: managing risk and creating resilience. IEEE Comput 47(9):70–76
Article
Google Scholar
Coma-Puig B, Carmona J, Gavaldà R, Alcoverro S, Martin V (2016) Fraud detection in energy consumption: a supervised approach. In: Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Montreal, QC, pp. 120–129. https://doi.org/10.1109/DSAA.2016.19
Dahee C, Kyungho L (2018) An artificial intelligence approach to financial fraud detection under IoT environment: a survey and implementation. Security Commun Netw. https://doi.org/10.1155/2018/5483472
Article
Google Scholar
Davis MV (2019) Strategies to prevent and detect occupational fraud in small retail businesses, Doctoral Thesis, College of Management and Technology, Walden University
Dong C, Wang H, Ni D, Liu Y, Chen Q (2020) Impact evaluation of cyber-attacks on traffic flow of connected and automated vehicles. IEEE Access 8:86824–86835. https://doi.org/10.1109/ACCESS.2020.2993254
Article
Google Scholar
Fenza G, Gallo M, Loia V (2019) Drift-aware methodology for anomaly detection in smart grid. IEEE Access 7:9645–9657. https://doi.org/10.1109/ACCESS.2019.2891315
Article
Google Scholar
Ferdous AHMI et al (2021) A hybrid structured PCF for fuel adulteration detection in terahertz regime. Sens Bio-Sens Res. https://doi.org/10.1016/j.sbsr.2021.100438
Article
Google Scholar
Golan MS et al (2020) Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID 19 pandemic. Environmental Systems and Decisions 40:222–243
Article
Google Scholar
Golfarelli M, Rizzi S (2019) A model-driven approach to automate data visualization in big data analytics. Sage J 19(1):24–47. https://doi.org/10.1177/1473871619858933
Article
Google Scholar
Gottwalt F, Chang EJ, Dillon TS (2021) Contextual anomaly detection methods for addressing intrusion detection: security & forensics book chapter | IGI Global (igi-global.com). https://doi.org/10.4018/978-1-7998-5728-0.ch009
General Accreditation Guidance—Validation and verification of quantitative and qualitative test methods. NATA - National Association of Testing Authorities, Australia (2018). https://www.nata.com.au/phocadownload/gen-accreditation-guidance/Validation-and-Verification-of-Quantitative-and-Qualitative-Test-Methods.pdf
Hosseini SS, Noorosana R (2018) Performance evaluation of EWMA and CUSUM control charts to detect anomalies in social networks using average and standard deviation of degree measures. Qual Reliab Eng Int 34(4):477–500. https://doi.org/10.1002/qre.2267
Article
Google Scholar
Hou E, Miller K, Hero A (2018) Anomaly detection in the monitoring of nuclear facilities. University of Michigan. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=2ahUKEwjtw_rFptvlAhUXuZ4KHeJ5DkUQFjAAegQIBBAC&url=http%3A%2F%2Fcvt.engin.umich.edu%2Fwpcontent%2Fuploads%2Fsites%2F173%2F2018%2F10%2F11_01_2018-0800-Hou.pdf&usg=AOvVaw3QG-Cd_PPlDae5UrOXmaAl. Accessed 31 Oct 2018
Kara I, Aydos M (2020) Cyber fraud: detection and analysis of the crypto-ransomware. In: Proceedings of the 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). https://doi.org/10.1109/UEMCON51285.2020.9298128
Kott A et al (2021) Cyber resilience: by design or by intervention? Computer 54(8):112–117
Article
Google Scholar
Lewis J, English C, Gesick J, Mukkamala S (2018) Validation Process Methods. National Renewable Energy Laboratory of US Department of Energy
Linkov I, Baiardi F, Florin MV, Greer S, Lambert JH, Pollock M, Rickli JM (2019a) Applying resilience to hybrid threats. IEEE Secur Priv 17(5):78–83
Article
Google Scholar
Linkov I, Baiardi F, Florin MV, Greer S, Lambert JH, Pollack M, Rickli JM, Roslycky L, Seager T, Thorisson H, Trump BD (2019b) Apply resilience to hybrid threats. IEEE Security Privacy Mag. https://doi.org/10.1109/MSEC.2019.2922866
Article
Google Scholar
Maldonado K et al (2019) Situational strategic awareness monitoring surveillance system—microcomputer and microcomputer clustering used for intelligent, economical, scalable, and deployable approach for safeguarding materials. J Imaging Sci Technol. https://doi.org/10.2352/J.ImagingSci.Technol.2019.63.6.060408
Article
Google Scholar
Maniraj SP, Saini A, Sarkar SD, Ahmed S (2019) Credit card fraud detection using machine learning and data science. Int J Eng Res Technol. https://doi.org/10.17577/IJERTV8IS090031
Article
Google Scholar
Melo V, Augusto Z, and Castro S (2015) Autenticador e transmissor (SAT): modelo tecnológico de automação e controle de processos em cidades inteligentes com exemplo de aplicação ao setor tributário, University of São Paulo, Doctoral thesis
Muggah R, Thompson NB (2018) Brazil struggles with effective cyber-crime response. Instituto Igarapé. https://www.igarape.org.br/en/brazil-struggles-with-effective-cyber-crime-response/.
Muggah R, Thompson N (2019) Brazil's cybercrime problem. Foreign Affairs. Available at: www.foreignaffairs.com/articles/south-america/2015-09-17/brazils-cybercrime-problem
Osborne C (2021) Colonial pipeline attack: everything you need to know. Zero Day. Available at https://www.zdnet.com/article/colonial-pipeline-ransomware-attack-everything-you-need-to-know/
Papale G, Sgaglione L (2017) SDD sentinel: a support tool for detecting and investigating electronic transaction frauds. In: Proceedings of the 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 318–323. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.53
Pereira France dos Santos A et al (2020) Fuel quality monitoring by color detection. Color Detect. https://doi.org/10.5772/intechopen.86531
Article
Google Scholar
Roege PE, Collier ZA, Chevardin V, Chouinard P, Florin MV, Lambert JH, Nielsen K, Nogal M, Todorovic B (2017), Bridging the gap from cyber security to resilience. In: Proceedings of the NATO Science for Peace and Security Series C:Environmental Security. https://doi.org/10.1007/978-94-024-1123-2_14
Rosa-Aquino P, Danner C (2021) What we know about the colonial pipeline shutdown. Intelligencer, https://www.Intelligencer.nymag.com/intelligencer/article/what-we-know-about-the-colonial-pipeline-shutdown-updates.html.
Ryman-Tubb N, Krause P (2011) Neural network rule extraction to detect credit card fraud, Engineering Applications of Neural Networks. In: Proceedings of the EANN 2011, AIAI 2011 IFIP Advances in Information and Communication Technology, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23957-1_12
Sahu A et al (2021) Multi-source multi-domain data fusion for cyberattack detection in power systems. IEEE Access 9:119118–119138. https://doi.org/10.1109/ACCESS.2021.3106873
Article
Google Scholar
Sample Procedure for Method Validation, NIST. https://www.nist.gov/system/files/documents/2016/12/21/sapmethodvalidation2016-12-21.pdf
Santoyo S (2017) A brief overview of outlier detection techniques. Medium, Towards Data Science. https://towardsdatascience.com/a-brief-overview-of-outlier-detection-techniques-1e0b2c19e561.
Smith RG, Urbas G (2001) Controlling fraud on the Internet: a CAPA perspective: report for the confederation of asian and pacific accountants. Research and Public Policy Series, No. 39. Australian Institute of Criminology, Canberra
Thorisson H, Lambert JH, Angeler DG, Baiardi F, Taveter K, Vasheasta A (2019), Resilience of critical infrastructure systems to hybrid threats with information disruption. In: Proceedings of the Resilience and Hybrid Threats: Security and Integrity for the Digital World, vol 55(13). https://doi.org/10.3233/NICSP190017
Wieringa R (2014) Empirical research methods for technology validation: scaling up to practice. J Syst Softw 95:19–31
Article
Google Scholar