Machine Learning and Security
Regular price
€65.99
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
Clarence Chio has a B.S. and M.S. in Computer Science from Stanford, specializing in data mining and artificial intelligence. He has spoken on machine learning and/or security at DEF CON and 11 other infosec/software engineering conferences in 8 countries between 2015 and 2016. He had been a community speaker with Intel, and a security consultant for Oracle. Clarence currently works as a Security Research Engineer at Shape Security, building a product that protects high valued web assets from automated attacks. He is also the founder and organizer of the "Data Mining for Cyber Security" meetup group, the largest gathering of security data scientists in the San Francisco Bay Area.David Freeman is head of Anti-Abuse Relevance at LinkedIn, where he leads a team of machine learning engineers charged with detecting and preventing fraud and abuse across the LinkedIn site and ecosystem. He has a Ph.D. in mathematics from UC Berkeley and did postdoctoral research in cryptography and security at CWI and Stanford University.