Data Science in Cybersecurity and Cyberthreat Intelligence
Synopsis
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.Book details
- Edition:
- 1st ed. 2020
- Series:
- Intelligent Systems Reference Library (Book 177)
- Author:
- Leslie F. Sikos, Kim-Kwang Raymond Choo
- ISBN:
- 9783030387884
- Related ISBNs:
- 9783030387877
- Publisher:
- Springer International Publishing
- Pages:
- N/A
- Reading age:
- Not specified
- Includes images:
- Yes
- Date of addition:
- 2020-03-02
- Usage restrictions:
- Copyright
- Copyright date:
- 2020
- Copyright by:
- Springer Nature Switzerland AG
- Adult content:
- No
- Language:
- English
- Categories:
- Computers and Internet, Nonfiction, Social Studies, Technology