Titlebook: Data Science in Cybersecurity and Cyberthreat Intelligence; Leslie F. Sikos,Kim-Kwang Raymond Choo Book 2020 Springer Nature Switzerland A

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978-3-030-38790-7Springer Nature Switzerland AG 2020

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The Formal Representation of Cyberthreats for Automated Reasoning,ncident response, and comprehensive and automated data analysis. This chapter reviews the most influential and widely deployed cyberthreat classification models, machine-readable taxonomies, and machine-interpretable ontologies that are well-utilized in cyberthreat intelligence applications.

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Seven Pitfalls of Using Data Science in Cybersecurity,results provided by a machine learning algorithm. There is some evidence to suggest that algorithm choice is not a discriminator. In particular, we explore the importance of feature set selection and evaluate the inherent problems in relying on synthetic data.

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Book 2020oning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in enterprises, to identify malicious URLs and DGA-generated domain names, and to improve the security of mHealth wearables. This book details h

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Discovering Malicious URLs Using Machine Learning Techniques,urce Locators (URLs) as an example dataset, with a method that automatically detects malicious URLs by leveraging machine learning techniques. We demonstrate the effectiveness of the method through performance evaluations.

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