Full Description
This book presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning 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 how analyzing the likelihood of vulnerability exploitation using machine learning classifiers can offer an alternative to traditional penetration testing solutions. In addition, the book describes a range of techniques that support data aggregation and data fusion to automate data-driven analytics in cyberthreat intelligence, allowing complex and previously unknown cyberthreats to be identified and classified, and countermeasures to be incorporated in novel incident response and intrusion detection mechanisms.
Contents
The Formal Representation of Cyberthreats for Automated Reasoning.- A Logic Programming Approach to Predict Enterprise-Targeted Cyberattacks.- Discovering Malicious URLs Using Machine Learning Techniques.- Machine Learning and Big Data Processing for Cybersecurity Data Analysis.- Systematic Analysis of Security Implementation for Internet of Health Things in Mobile Health Networks.- Seven Pitfalls of Using Data Science in Cybersecurity.
