Minicursos da ERCEMAPI 2023
Palavras-chave:
Google Earth Engine, Sensoriamento Remoto, Geoprocessamento, Sistemas de Detecção de Intrusão, Aprendizado de Máquina, Análise de Dados, Mobilidade Urbana, PythonSinopse
Este livro oferece uma perspectiva abrangente sobre o sensoriamento remoto, a segurança da informação e a análise de dados de mobilidade urbana, explorando aplicações práticas e conceitos fundamentais em cada área.
No primeiro capítulo, “Google Earth Engine no Ensino de Introdução ao Sensoriamento Remoto e Geoprocessamento”, o leitor é conduzido por uma jornada pelo mundo do geoprocessamento e do sensoriamento remoto, apresentando a plataforma Google Earth Engine e suas aplicações educacionais. Conceitos introdutórios são abordados em conjunto com práticas, utilizando a plataforma em nuvem, proporcionando uma visão abrangente e acessível sobre o tema.
No segundo capítulo, intitulado “Uma Visão sobre Sistemas de Detecção de Intrusão Baseados em Anomalias”, os leitores adentram o universo da segurança da informação, explorando sistemas de detecção de intrusão e seu aprimoramento com técnicas de Aprendizado de Máquina. Os desafios e estratégias na detecção de ameaças cibernéticas são discutidos em detalhes, oferecendo uma compreensão ampla e aprofundada sobre o assunto.
Por fim, o terceiro capítulo, “Tratamento e Análise de Dados de Mobilidade Urbana: Uma Metodologia Teórica e Prática”, aborda a análise de dados de mobilidade urbana, destacando os desafios e oportunidades encontrados nesse campo. Uma metodologia teórica e prática é apresentada, fornecendo insights valiosos sobre o tratamento e análise de dados de mobilidade, juntamente com exemplos práticos e comparações entre bibliotecas Python disponíveis.
Estes capítulos tratam de temas relevantes para área da computação e contribuirão para melhor compreensão dos detalhes acerca do sensoriamento remoto, a segurança da informação e a análise de dados de mobilidade urbana.
Capítulos
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1. Google Earth Engine no Ensino de Introdução ao Sensoriamento Remoto e Geoprocessamento
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2. Uma Visão sobre Sistemas de Detecção de Intrusão Baseados em Anomalias
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3. Tratamento e Análise de Dados de Mobilidade Urbana: Uma Metodologia Teórica e Prática
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Referências
A Reyes et al. “yupi: Generation, Tracking and Analysis of Trajectory data in Python”. In: Environmental Modelling & Software 163 (2023), p. 105679.
Ahmad, T. and Aziz, M. N. (2019). Data preprocessing and feature selection for machine learning intrusion detection systems. ICIC Express Lett, 13(2):93–101.
Ahmed Kharrat et al. “Clustering algorithm for network constraint trajectories”. In: Headway in Spatial Data Handling. Springer, 2008, pp. 631–647.
AL Sayeth Saabith,MMMFareez, and T Vinothraj. “Python current trend applicationsan overview”. In: International Journal of Advance Engineering and Research Development 6.10 (2019).
Alastair R Beresford and Frank Stajano. “Location privacy in pervasive computing”. In: IEEE Pervasive computing 2.1 (2003), pp. 46–55.
Anita Graser and Melitta Dragaschnig. “Exploring movement data in notebook environments”. In: IEEE VIS 2020 - MoVis. 2020.
Bezerra, V. H., da Costa, V. G. T., Junior, S. B., Miani, R. S., and Zarpelao, B. B. (2018). One-class classification to detect botnets in iot devices. Anais do XVIII Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg), Natal, RN, 22-25 Outubro 2018.
Brendan Morris and Mohan Trivedi. “Learning trajectory patterns by clustering: Experimental studies and comparative evaluation”. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE. 2009, pp. 312–319.
Câmara, G. (2005). Representação computacional de dados geográficos. CASANOVA, MA et al. Banco de dados geográficos. Curitiba: Mundogeo, pages 11–52.
Cardille, J. A. (2022). Interpreting image series. [link].
Charles R. Harris et al. “Array programming with NumPy”. In: Nature 585 (2020), pp. 357–362. DOI: 10.1038/s41586-020-2649-2.
Chi-Yin Chow, Mohamed F Mokbel, and Walid G Aref. “Casper* Query processing for location services without compromising privacy”. In: ACM Transactions on Database Systems (TODS) 34.4 (2009), pp. 1–48.
Chorás, M. and Pawlicki, M. (2021). Intrusion detection approach based on optimised artificial neural network. Neurocomputing, 452:705–715.
Clayson Celes et al. “Improving vanet simulation with calibrated vehicular mobility traces”. In: IEEE Transactions on Mobile Computing 16.12 (2017), pp. 3376–3389.
Clayson Celes, Azzedine Boukerche, and Antonio AF Loureiro. “From mobility traces to knowledge: Design guidance for intelligent vehicular networks”. In: IEEE Network 34.4 (2020), pp. 227–233.
DAVIS, C. and Fonseca, F. (2001). Introdução aos sistemas de informação geográficos. Belo Horizonte: Departamento de Cartografia/UFMG.
Deyu Deng et al. “Spatial-temporal data science of COVID-19 data”. In: 2021 IEEE 15th International Conference on Big Data Science and Engineering (Big- DataSE). IEEE. 2021, pp. 7–14.
Dhaliwal, S. S., Nahid, A., and Abbas, R. (2018). Effective intrusion detection system using xgboost. Information, 9(7):1–24.
Diego O Rodrigues and Leandro Villas. “SMAFramework: Arcabouço para Integração de Dados Urbanos Cientes da Correlação Espaço-Temporal”. In: Anais Estendidos do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC. 2019, pp. 113–120.
Engelen, G., Rimmer, V., and Joosen, W. (2021). Troubleshooting an intrusion detection dataset: the cicids2017 case study. 2021 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA, 27 May 2011.
Enrico Ubaldi et al. “Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data”. In: arXiv preprint arXiv:2107.14297 (2021).
Fabrício R de Souza et al. “Mocha: A tool for mobility characterization”. In: Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. 2018, pp. 281–288.
Ferreira, J. (2014). Google Apps Script: Web Application Development Essentials. "O’Reilly Media, Inc.".
García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., and Vázquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers Security, 28(1):18–28.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google earth engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, 202:18–27.
Guido Van Rossum, Barry Warsaw, and Nick Coghlan. “PEP 8–style guide for python code”. In: Python. org 1565 (2001), p. 28.
Gyozo Gidofalvi, Xuegang Huang, and Torben Bach Pedersen. “Privacy-preserving data mining on moving object trajectories”. In: 2007 International Conference on Mobile Data Management. IEEE. 2007, pp. 60–68.
Haitao Yuan and Guoliang Li. “A survey of traffic prediction: from spatio-temporal data to intelligent transportation”. In: Data Science and Engineering 6 (2021), pp. 63–85.
Hajj, S., El Sibai, R., Bou Abdo, J., Demerjian, J., Makhoul, A., and Guyeux, C. (2021). Anomaly-based intrusion detection systems: The requirements, methods, measurements, and datasets. Transactions on Emerging Telecommunications Technologies, 32(4):1–36.
Henry Martin et al. “Trackintel: An open-source Python library for human mobility analysis”. In: Computers, Environment and Urban Systems 101 (2023), p. 101938. DOI: 10.1016/j.compenvurbsys.2023.101938.
Hindy, H., Brosset, D., Bayne, E., Seeam, A. K., Tachtatzis, C., Atkinson, R., and Bellekens, X. (2020). A taxonomy of network threats and the effect of current datasets on intrusion detection systems. IEEE Access, 8:104650–104675.
Hoyoung Jeung et al. “Discovery of convoys in trajectory databases”. In: arXiv preprint arXiv:1002.0963 (2010).
Hoyoung Jeung, Heng Tao Shen, and Xiaofang Zhou. “Convoy queries in spatiotemporal databases”. In: 2008 IEEE 24th International Conference on Data Engineering. IEEE. 2008, pp. 1457–1459.
International Telecommunication Union (2021). Global Cybersecurity Index 2020: Measuring commitment to cybersecurity. ITUPublications, Geneva, Switerland, 1 edition.
Jean-Luc R Stevens, Philipp Rudiger, and James A Bednar. “HoloViews: Building complex visualizations easily for reproducible science”. In: Proceedings of the 14th Python in Science Conference. SciPy. 2015, pp. 61–69.
Jeff Reback et al. “pandas-dev/pandas: Pandas 1.0. 5”. In: Zenodo (2020).
Joachim Gudmundsson and Marc Van Kreveld. “Computing longest duration flocks in trajectory data”. In: Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems. 2006, pp. 35–42.
Justin Shenk et al. “Traja: A Python toolbox for animal trajectory analysis”. In: Journal of Open Source Software 6.63 (2021), p. 3202.
Kai Zheng et al. “On discovery of gathering patterns from trajectories”. In: 2013 IEEE 29th international conference on data engineering (ICDE). IEEE. 2013, pp. 242–253.
Kai Zheng et al. “Online discovery of gathering patterns over trajectories”. In: IEEE Transactions on Knowledge and Data Engineering 26.8 (2013), pp. 1974–1988.
Kelsey Jordahl et al. “geopandas/geopandas: v0. 6.0”. In: Zenodo (2019).
Kenyon, A., Deka, L., and D., E. (2020). Are public intrusion datasets fit for purpose characterising the state of the art in intrusion event datasets. Computers Security, 99:1–26.
Khraisat, A., Gondal, I., Vamplew, P., and Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(20):1–22.
Koehrsen, W. (2018). Overfitting vs. underfitting: A complete example. Towards Data Science, 405.
Layeghy, S. and Portmann, M. (2022). On generalisability of machine learningbased network intrusion detection systems. arXiv preprint arXiv:2205.04112, [s.n.]:1–12.
Liu, H. and Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 9(20):1–28.
Longley, P. (2005). Geographic information systems and science. John Wiley & Sons.
Lu-An Tang et al. “A framework of traveling companion discovery on trajectory data streams”. In: ACM Transactions on Intelligent Systems and Technology (TIST) 5.1 (2014), pp. 1–34.
Luca Pappalardo et al. “scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data”. In: Journal of Statistical Software 103.4 (2022), pp. 1–38. DOI: 10.18637/jss.v103.i04. URL: [link].
Lucas N Silva et al. “MobVis: A Framework for Analysis and Visualization of Mobility Traces”. In: 2022 IEEE Symposium on Computers and Communications (ISCC). IEEE. 2022, pp. 1–6.
Maciá-Fernández, G., Camacho, J., Magán-Carrión, R., García-Teodoro, P., and Theró, R. (2018). Ugr‘16: A new dataset for the evaluation of cyclostationaritybased network idss. Computers Security, 73:411–424.
Mahfouz, A., Abuhussein, A., Venugopal, D., and Shiva, S. (2020). Ensemble classifiers for network intrusion detection using a novel network attack dataset. Future Internet, 12(11):1–19.
Manolis Terrovitis and Nikos Mamoulis. “Privacy preservation in the publication of trajectories”. In: The Ninth international conference on mobile data management (mdm 2008). IEEE. 2008, pp. 65–72.
MapBiomas (2021). Mapbiomas project-collection 6.0 of the annual series of land use and land cover maps of brazil.
Mark Daniel Rintoul. ML for Trajectories: Bridging the Gap from Computer to Analyst with Tracktable. Tech. rep. Sandia National Lab.(SNL-NM), Albuquerque, NM (USA), 2020.
Martínez Torres, J., Iglesias Comesaña, C., and García-Nieto, P. J. (2019). Machine learning techniques applied to cybersecurity. International Journal of Machine Learning and Cybernetics, 10:2823–2836.
Matthew Rocklin et al. “Dask: Parallel computation with blocked algorithms and task scheduling”. In: Proceedings of the 14th python in science conference. Vol. 130. SciPy Austin, TX. 2015, p. 136.
Maurício Cavalcante Bráz. “Implementação de algoritmos para análise de similaridade de trajetória na biblioteca PyMove”. In: Monografia. Universidade Federal do Ceará (2020).
Mehmet Ercan Nergiz, Maurizio Atzori, and Yucel Saygin. “Towards trajectory anonymization: a generalization-based approach”. In: Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS. 2008, pp. 52–61.
Michal Pleskowicz. Mobilipy-from raw GPS data to mobility data. Tech. rep. 2022.
Molina-Coronado, B., Mori, U., Mendiburu, A., and Miguel-Alonso, J. (2020). Survey of network intrusion detection methods from the perspective of the knowledge discovery in databases process. IEEE Transactions on Network and Service Management, 17(4):2451–2479.
Moustafa, N. and Slay, J. (2015). Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). Paper presented at the 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, ACT, Australia, 10-12 November 2015.
Naseer, M., Rusdi, J. F., Shanono, N. M., Salam, S., Muslim, Z. B., Abu, N. A., and Abadi, I. (2021). Malware detection: issues and challenges. Cybersecurity, 1807(1):1–6.
Obaid, H. S., Dheyab, S. A., and Sabry, S. S. (2019). The impact of data preprocessing techniques and dimensionality reduction on the accuracy of machine learning. Paper presented at the 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON), Jaipur, India, 13–15 March 2019.
Osman Abul, Francesco Bonchi, and Mirco Nanni. “Never walk alone: Uncertainty for anonymity in moving objects databases”. In: 2008 IEEE 24th international conference on data engineering. Ieee. 2008, pp. 376–385.
Paulauskas, N. and Auskalnis, J. (2017). Analysis of data pre-processing influence on intrusion detection using nsl-kdd dataset.
Pedro HC Maia, Claudio EC Campelo, and Campina Grande–PB–Brazil. “Mobipy- A Python Library for Analyzing Mobility Patterns.” In: GeoInfo. 2020, pp. 141–152.
Putra, W. and Huang, J. J. (2019). A survey of intrusion detection system. International Journal of Informatics and Computation, 1(1):1–19.
Resende, P. A. A. and Drummond, A. C. (2018). A survey of random forest based methods for intrusion detection systems. ACM Computing Surveys (CSUR), 51(3):1–36.
Ring, M., Wunderlich, S., Scheuring, D., Landes, D., and Hotho, A. (2019). A survey of network-based intrusion detection data sets. Computers Security, 86:147–167.
Rui Moreno and Carina Oliveira. “Avaliação de Desempenho de Redes em Malha Sem Fio em Cenários de Cidades Inteligentes”. In: Anais da X Escola Regional de Computação do Ceará, Maranhão e Piauí. São Luís: SBC, 2022, pp. 109–118. DOI: 10.5753/ercemapi.2022.226045. URL: [link].
Salih, A. A. and Abdulrazaq, M. B. (2019). Combining best features selection using three classifiers in intrusion detection system. 2019 International Conference on Advanced Science and Engineering (ICOASE), Zakho - Duhok, Iraq, 02-04 April 2019.
Salman Haidri et al. “PTRAIL—A python package for parallel trajectory data preprocessing”. In: SoftwareX 19 (2022), p. 101176.
Saranya, T., Sridevi, S., Deisy, C., Chung, T. D., and Khan, M. A. (2020). Performance analysis of machine learning algorithms in intrusion detection system: A review. Procedia Computer Science, 171:1251–1260.
Sarhan, M., Layeghy, S., Gallagher, M., and Portmann, M. (2023). From zero-shot machine learning to zero-day attack detection. International Journal of Information Security, 22:947–959.
Sarker, I. H., Kayes, A., Badsha, S., Alqahtani, H., Watters, P., and Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big data, 7(1):1–29.
Sharafaldin, I., Lashkari, A. H., and Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. Paper presented at the 4th International Conference on Information Systems Security and Privacy (ICISSp), Funchal, Madeira, Portugal, 22–24 January 2018.
Shenfield, A., Day, D., and Ayesh, A. (2018). Intelligent intrusion detection systems using artificial neural networks. Ict Express, 4(2):95–99.
Smith, M. J., Goodchild, M. F., and Longley, P. A. (2007). Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. Troubador Publishing, 2nd edition.
Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A. A. (2009). A detailed analysis of the kdd cup 99 data set. Paper presented at the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada, 08-10 July 2009.
Thakkar, A. and Lohiya, R. (2022). A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions. Artificial Intelligence Review, 55:453–563.
Tomlin, C. D. et al. (1990). Geographic information systems and cartographic modeling, volume 249. Prentice Hall Englewood Cliffs, NJ.
Viegas, E. K., Santin, A. O., and Oliveira, L. S. (2017). Toward a reliable anomaly-based intrusion detection in real-world environments. Computer Networks, 127:200–216.
Willem Eerland et al. “Teetool–a probabilistic trajectory analysis tool”. In: Journal of Open Research Software 5.1 (2017).
Wirfs-Brock, A. and Eich, B. (2020). Javascript: the first 20 years. Proceedings of the ACM on Programming Languages, 4(HOPL):1–189.
Yu Zheng. “Trajectory data mining: an overview”. In: ACM Transactions on Intelligent Systems and Technology (TIST) 6.3 (2015), pp. 1–41.
Zaman, M. and Lung, C. (2018). Evaluation of machine learning techniques for network intrusion detection. NOMS 2018-2018 IEEE/IFIP Network Operations and Management Symposium, Taipei, Taiwan, 23-27 April 2018.
ZhengYu TangLu’an et al. “Discover of Traveling COmpanions from Streaming Trajectoris”. In: ICDE, WashingtonDC, USA (2012).
Zhenhui Li et al. Swarm: Mining relaxed temporal moving object clusters. Tech. rep. ILLINOIS UNIV AT URBANA-CHAMPAIGN DEPT OF COMPUTER SCIENCE, 2010.
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