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Cyber Threat Intelligence Softcover Reprint of the Original 1st 2018 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 334 pages, height x width: 235x155 mm, weight: 522 g, 77 Illustrations, color; 28 Illustrations, black and white; VI, 334 p. 105 illus., 77 illus. in color., 1 Paperback / softback
  • Sērija : Advances in Information Security 70
  • Izdošanas datums: 02-Feb-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 303008891X
  • ISBN-13: 9783030088910
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  • Mīkstie vāki
  • Cena: 145,08 €*
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  • Standarta cena: 170,69 €
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  • Formāts: Paperback / softback, 334 pages, height x width: 235x155 mm, weight: 522 g, 77 Illustrations, color; 28 Illustrations, black and white; VI, 334 p. 105 illus., 77 illus. in color., 1 Paperback / softback
  • Sērija : Advances in Information Security 70
  • Izdošanas datums: 02-Feb-2019
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 303008891X
  • ISBN-13: 9783030088910
Citas grāmatas par šo tēmu:
This book provides readers with up-to-date research of emerging cyber threats and defensive mechanisms, which are timely and essential. It covers cyber threat intelligence concepts against a range of threat actors and threat tools (i.e. ransomware) in cutting-edge technologies, i.e., Internet of Things (IoT), Cloud computing and mobile devices. This book also provides the technical information on cyber-threat detection methods required for the researcher and digital forensics experts, in order to build intelligent automated systems to fight against advanced cybercrimes.

The ever increasing number of cyber-attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost real-time, and with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions this in essence defines cyber threat intelligence notion. However, such intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyze, and interpret cyber-attack campaigns which is covered in this book. This book will focus on cutting-edge research from both academia and industry, with a particular emphasis on providing wider knowledge of the field, novelty of approaches, combination of tools and so forth to perceive reason, learn and act on a wide range of data collected from different cyber security and forensics solutions. This book introduces the notion of cyber threat intelligence and analytics and presents different attempts in utilizing machine learning and data mining techniques to create threat feeds for a range of consumers. Moreover, this book sheds light on existing and emerging trends in the field which could pave the way for future works.





The inter-disciplinary nature of this book, makes it suitable for a wide range of audiences with  backgroundsin artificial intelligence, cyber security, forensics, big data and data mining, distributed systems and computer networks. This would include industry professionals, advanced-level students and researchers that work within these related fields.

Recenzijas

Cyber Threat Intelligence offers responsible security professionals a chance to come face to face with the cyberthreat detectors of the modern era. Many may be intimidated by the computerese, equations, and algorithms but they have the educational advantage of engaging with the genuine article, not a sugar-coated primer. (James T. Dunne, Security Management, June 01, 2019)

Cyber Threat Intelligence: Challenges and Opportunities
1(6)
Mauro Conti
Tooska Dargahi
Ali Dehghantanha
Machine Learning Aided Static Mai ware Analysis: A Survey and Tutorial
7(40)
Andrii Shalaginov
Sergii Banin
Ali Dehghantanha
Katrin Franke
Application of Machine Learning Techniques to Detecting Anomalies in Communication Networks: Datasets and Feature Selection Algorithms
47(24)
Qingye Ding
Zhida Li
Soroush Haeri
Ljiljana Trajkovic
Application of Machine Learning Techniques to Detecting Anomalies in Communication Networks: Classification Algorithms
71(22)
Zhida Li
Qingye Ding
Soroush Haeri
Ljiljana Trajkovic
Leveraging Machine Learning Techniques for Windows Ransom ware Network Traffic Detection
93(14)
Omar M. K. Alhawi
James Baldwin
Ali Dehghantanha
Leveraging Support Vector Machine for Opcode Density Based Detection of Crypto-Ransomware
107(30)
James Baldwin
Ali Dehghantanha
BoTShark: A Deep Learning Approach for Hot net Ira the Detection
137(18)
Sajad Homayoun
Marzieh Ahmadzadeh
Sattar Hashemi
Ali Dehghantanha
Raouf Khayami
A Practical Analysis of the Rise in Mobile Phishing
155(14)
Brad Wardman
Michael Weideman
Jakub Burgis
Nicole Harris
Blake Butler
Nate Pratt
PDF-Malware Detection: A Survey and Taxonomy of Current Techniques
169(24)
Michele Elingiusti
Leonardo Aniello
Leonardo Querzoni
Roberto Baldoni
Adaptive TYaffic Fingerprinting for Darknet Threat Intelligence
193(26)
Hamish Haughey
Gregory Epiphaniou
Haider Al-Khateeb
Ali Dehghantanha
A Model for Android and iOS Applications Risk Calculation: CVSS Analysis and Enhancement Using Case-Control Studies
219(20)
Milda Petraityte
Ali Dehghantanha
Gregory Epiphaniou
A Honeypot Proxy Framework for Deceiving Attackers with Fabricated Content
239(20)
Jarko Papalitsas
Sampsa Rauti
Jani Tammi
Ville Leppanen
Investigating the Possibility of Data Leakage in Time of Live VM Migration
259(22)
Rehana Yasmin
Mohammad Reza Memarian
Shohreh Hosseinzadeh
Mauro Conti
Ville Leppanen
Forensics Investigation of OpenFIow-Based SDN Platforms
281(16)
Mudit Kalpesh Pandya
Sajad Homayoun
Ali Dehghantanha
Mobile Forensics: A Bibliometric Analysis
297(14)
James Gill
Ihechi Okere
Hamed Haddad Pajouh
Ali Dehghantanha
Emerging from the Cloud: A Bibliometric Analysis of Cloud Forensics Studies
311(22)
James Baldwin
Omar M. K. Alhawi
Simone Shaughnessy
Alex Akinbi
Ali Dehghantanha
Index 333