Data-Driven Cybersecurity (Final) by Mariano Mattei (.PDF)

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File Size: 47.3 MB

Data-Driven Cybersecurity: Reducing risk with proven metrics (Final Release) by Mariano Mattei
Requirements: .PDF reader, 47.3 MB
Overview: Measure, improve, and communicate the value of your security program. Every business decision should be driven by data—and cyber security is no exception. In Data-Driven Cybersecurity, you’ll master the art and science of quantifiable cybersecurity, learning to harness data for enhanced threat detection, response, and mitigation. You’ll turn raw data into meaningful intelligence, better evaluate the performance of your security teams, and proactively address the vulnerabilities revealed by the numbers. AI is a broad term that encompasses various technologies, including Machine Learning (ML), natural language processing (NLP), and Generative AI with Large Language Models (LLMs). In cybersecurity, AI is leveraged to identify complex patterns and correlations that human analysts might overlook. When trained on high-quality historical and real-time data, Machine Learning models can recognize subtle indicators of compromise, enabling predictive security measures. As you move forward, you’ll discover hands-on examples of Python and Jupyter Notebooks that make collecting and visualizing metrics straight-forward—no advanced coding knowledge is required. For those looking to push the envelope, I also discuss advanced statistical methods, machine learning approaches, and even Generative AI techniques that can forecast potential threats and anomalies in real time. For readers familiar with the basics of cybersecurity and data analysis.
Genre: Non-Fiction > Tech & Devices

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