Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis.

Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike.

• Learn how machine learning has contributed to the success of modern spam filters
• Quickly detect anomalies, including breaches, fraud, and impending system failure
• Conduct malware analysis by extracting useful information from computer binaries
• Uncover attackers within the network by finding patterns inside datasets
• Examine how attackers exploit consumer-facing websites and app functionality
• Translate your machine learning algorithms from the lab to production
• Understand the threat attackers pose to machine learning solutions

Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis.

Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike.

• Learn how machine learning has contributed to the success of modern spam filters
• Quickly detect anomalies, including breaches, fraud, and impending system failure
• Conduct malware analysis by extracting useful information from computer binaries
• Uncover attackers within the network by finding patterns inside datasets
• Examine how attackers exploit consumer-facing websites and app functionality
• Translate your machine learning algorithms from the lab to production
• Understand the threat attackers pose to machine learning solutions