TY - BOOK ID - 64866007 TI - Data Science in Cybersecurity and Cyberthreat Intelligence AU - Sikos, Leslie F. AU - Choo, Kim-Kwang Raymond. PY - 2020 SN - 3030387887 3030387879 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Computer security. KW - Computer privacy KW - Computer system security KW - Computer systems KW - Computers KW - Cyber security KW - Cybersecurity KW - Electronic digital computers KW - Protection of computer systems KW - Security of computer systems KW - Data protection KW - Security systems KW - Hacking KW - Protection KW - Security measures KW - Engineering—Data processing. KW - Computational intelligence. KW - Artificial intelligence. KW - Computer crimes. KW - Data Engineering. KW - Computational Intelligence. KW - Artificial Intelligence. KW - Cybercrime. KW - Computer Crime. KW - Computers and crime KW - Cyber crimes KW - Cybercrimes KW - Electronic crimes (Computer crimes) KW - Internet crimes KW - Crime KW - Privacy, Right of KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing UR - https://www.unicat.be/uniCat?func=search&query=sysid:64866007 AB - 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. ER -