Build smart cybersecurity systems with the power of machine learning and deep learning to protect your corporate assets
Today's organizations spend billions of dollars globally on cybersecurity. Artificial intelligence has emerged as a great solution for building smarter and safer security systems that allow you to predict and detect suspicious network activity, such as phishing or unauthorized intrusions.
This cybersecurity book presents and demonstrates popular and successful AI approaches and models that you can adapt to detect potential attacks and protect your corporate systems. You'll learn about the role of machine learning and neural networks, as well as deep learning in cybersecurity, and you'll also learn how you can infuse AI capabilities into building smart defensive mechanisms. As you advance, you'll be able to apply these strategies across a variety of applications, including spam filters, network intrusion detection, botnet detection, and secure authentication.
By the end of this book, you'll be ready to develop intelligent systems that can detect unusual and suspicious patterns and attacks, thereby developing strong network security defenses using AI.
If you're a cybersecurity professional or ethical hacker who wants to build intelligent systems using the power of machine learning and AI, you'll find this book useful. Familiarity with cybersecurity concepts and knowledge of Python programming is essential to get the most out of this book.
"synopsis" may belong to another edition of this title.
Alessandro Parisi has been an IT professional for over 20 years, with a vast experience as a security data scientist, AI cyber-security, and blockchain specialist. He has been operating within organizational and decisional contexts, characterized by high complexity. Over the years he is helping companies adopt artificial intelligence. and blockchain DLT technologies, as strategic tools in protecting corporate sensitive assets. He holds a Masters degree in economics and statistics.
"About this title" may belong to another edition of this title.
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