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Diante das mudanças progressivas em cibersegurança, muitas delas impulsionadas pela ciência de dados e pela oferta de mais recursos computacionais, é cada vez mais necessária a disseminação do tema e a formação e/ou atualização de profissionais. Este tutorial visa oferecer aos participantes um primeiro contato com o tema de ciência de dados aplicada à cibersegurança através de uma perspectiva híbrida: teórica e prática. O tutorial inicia com uma parte teórica sobre cibersegurança e ciência de dados destacando seu uso na prevenção, detecção e mitigação de ataques. Serão apresentadas brevemente as plataformas utilizadas em ciência de dados, como Kaggle e Jupyter, através de exemplos na área de cibersegurança. O tutorial segue com uma série de diferentes estudos de caso, em Python, representativos da área de cibersegurança e relacionados aos principais problemas, como a detecção de malware, detecção de anomalia, de bots, de spams, vazamentos de informações e phishing. O tutorial segue uma abordagem introdutória e não se aprofundará nos tópicos abordados, porém oferecerá uma visão geral e ampla dos avanços e conceitos relacionados ao tema.

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Given the progressive changes in cybersecurity, many of which are driven by data science and the availability of more computational resources, the dissemination of this subject and the training or updating of professionals are becoming increasingly necessary. This tutorial aims to provide participants with a first contact with the topic of data science applied to cybersecurity through a hybrid perspective: theoretical and practical. The tutorial begins with a theoretical section on cybersecurity and data science, highlighting their use in preventing, detecting, and mitigating attacks. The platforms used in data science, such as Kaggle and Jupyter, will be briefly presented with examples in the cybersecurity domain. The tutorial continues with a series of different case studies, representative from the cybersecurity field, and related to major issues, such as malware detection, anomaly detection, bot detection, spam, information leaks, and phishing. The tutorial follows an introductory approach and will not delve deeply into the topics covered. Nevertheless, it will offer a comprehensive overview of advancements and concepts related to the subject.

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#	Name
1	Michele Nogueira(michele@dcc.ufmg.br)
2	Ligia Borges(ligia.borges@dcc.ufmg.br)

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Reference

#	Reference
1	Data Science in Cybersecurity and Cyberthreat Intelligence. Leslie F. Sikos and Kim-Kwang Raymond Choo (editors), Intelligent Systems Reference Library, Springer, 2020.
2	An Introduction to Statistical Learning. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. Second edition.
3	Data Science for Cyber-Security. Nick Heard, Nial Adams, Patrick Rubin-Delanchy, Melissa Turcotte. 2019 World Scientific Publishing Europe Ltd.
4	Cybersecurity Essentials. Charles J. Brooks, Christopher Grow, Philip Craig, Donald Short. 2018, John Wiley & Sons, Inc.
5	Cyber Warfare - Truth, Tactics, and Strategies: Strategic concepts and truths to help you and your organization survive on the battleground of cyber warfare. Chase Cunningham and Gregory J. Touhill. 2020, Packt Publishing.
6	M. Pelloso, A. Vergutz, A. Santos and M. Nogueira, ``A Self-Adaptable System for DDoS Attack Prediction Based on the Metastability Theory'', 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018, pp. 1-6.
7	M. Nogueira, A. Santos and J. M. F. Moura, ``Early Signals from Volumetric DDoS Attacks: An Empirical Study'', eprint 1609.09560, arXiv. 2016.
8	I.H. Sarker, A.S.M. Kayes, S. Badsha, et al.. Cybersecurity data science: an overview from machine learning perspective. J. Big Data 7, 41 (2020). https://doi.org/10.1186/s40537-020-00318-5
9	P.K. Mvula, P. Branco, G.V. Jourdan, et al. A systematic literature review of cyber-security data repositories and performance assessment metrics for semi-supervised learning. Discov Data 1, 4 (2023). https://doi.org/10.1007/s44248-023-00003-x

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