Coverart for item
The Resource Cyber threat intelligence, Ali Dehghantanha, Mauro Conti, Tooska Dargahi, editors, (electronic book)

Cyber threat intelligence, Ali Dehghantanha, Mauro Conti, Tooska Dargahi, editors, (electronic book)

Label
Cyber threat intelligence
Title
Cyber threat intelligence
Statement of responsibility
Ali Dehghantanha, Mauro Conti, Tooska Dargahi, editors
Contributor
Editor
Subject
Language
eng
Summary
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 backgrounds in 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
Member of
Cataloging source
GW5XE
Dewey number
005.8
Illustrations
illustrations
Index
index present
LC call number
QA76.9.A25
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Dehghantanha, Ali
  • Conti, Mauro
  • Dargahi, Tooska
Series statement
Advances in information security,
Series volume
volume 70
http://library.link/vocab/subjectName
  • Computer security
  • Internet
  • Computer Science
  • Security
  • Artificial Intelligence (incl. Robotics)
  • Information Systems and Communication Service
  • Computer Communication Networks
Label
Cyber threat intelligence, Ali Dehghantanha, Mauro Conti, Tooska Dargahi, editors, (electronic book)
Instantiates
Publication
Note
Includes index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Contents; Cyber Threat Intelligence: Challenges and Opportunities; 1 Introduction; 1.1 Cyber Threat Intelligence Challenges; 1.1.1 Attack Vector Reconnaissance; 1.1.2 Attack Indicator Reconnaissance; 1.2 Cyber Threat Intelligence Opportunities; 2 A Brief Review of the Book Chapters; References; Machine Learning Aided Static Malware Analysis:A Survey and Tutorial; 1 Introduction; 2 An Overview of Machine Learning-Aided Static Malware Detection; 2.1 Static Characteristics of PE Files; 2.2 Machine Learning Methods Used for Static-Based Malware Detection; 2.2.1 Statistical Methods
  • 2.2.2 Rule Based2.2.3 Distance Based; 2.2.4 Neural Networks; 2.2.5 Open Source and Freely Available ML Tools; 2.2.6 Feature Selection and Construction Process; 2.3 Taxonomy of Malware Static Analysis Using Machine Learning; 3 Approaches for Malware Feature Construction; 4 Experimental Design; 5 Results and Discussions; 5.1 Accuracy of ML-Aided Malware Detection Using Static Characteristics; 5.1.1 PE32 Header; 5.1.2 Bytes n-Gram; 5.1.3 Opcode n-Gram; 5.1.4 API Call n-Grams; 6 Conclusion; References
  • Application of Machine Learning Techniques to Detecting Anomalies in Communication Networks: Datasets and Feature Selection Algorithms1 Introduction; 1.1 Border Gateway Protocol (BGP); 1.2 Approaches for Detecting Network Anomalies; 2 Examples of BGP Anomalies; 3 Analyzed BGP Datasets; 3.1 Processing of Collected Data; 4 Extraction of Features from BGP Update Messages; 5 Review of Feature Selection Algorithms; 5.1 Fisher Algorithm; 5.2 Minimum Redundancy Maximum Relevance (mRMR) Algorithms; 5.3 Odds Ratio Algorithms; 5.4 Decision Tree Algorithm; 6 Conclusion; References
  • Application of Machine Learning Techniques to Detecting Anomalies in Communication Networks: Classification Algorithms1 Introduction; 1.1 Machine Learning Techniques; 2 Classification Algorithms; 2.1 Performance Metrics; 3 Support Vector Machine (SVM); 4 Long Short-Term Memory (LSTM) Neural Network; 5 Hidden Markov Model (HMM); 6 Naive Bayes; 7 Decision Tree Algorithm; 8 Extreme Learning Machine Algorithm (ELM); 9 Discussion; 10 Conclusion; References; Leveraging Machine LearningTechniques for Windows Ransomware Network Traffic Detection; 1 Introduction; 2 Related Works; 3 Methodology
  • 3.1 Data Collection Phase3.1.1 Malicious Applications; 3.1.2 Benign Applications; 3.2 Feature Selection and Extraction; 3.3 Machine Learning Classifiers; 4 Experiments and Results; 4.1 Evaluation Measures; 4.2 Malware Experiment and Results; 4.3 Result Comparison; 5 Conclusion and Future Works; References; Leveraging Support Vector Machine for Opcode Density Based Detection of Crypto-Ransomware; 1 Introduction; 2 Related Works and Research Literature; 3 Methodology; 3.1 Data Collection; 3.2 Feature Extraction; 3.3 Dataset Creation; 3.3.1 Merging the Data; 3.3.2 Normalising the Data
Extent
1 online resource (vi, 334 pages)
Form of item
online
Isbn
9783319739519
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-73951-9
Other physical details
illustrations (some color).
System control number
  • on1033603488
  • (OCoLC)1033603488
Label
Cyber threat intelligence, Ali Dehghantanha, Mauro Conti, Tooska Dargahi, editors, (electronic book)
Publication
Note
Includes index
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Intro; Contents; Cyber Threat Intelligence: Challenges and Opportunities; 1 Introduction; 1.1 Cyber Threat Intelligence Challenges; 1.1.1 Attack Vector Reconnaissance; 1.1.2 Attack Indicator Reconnaissance; 1.2 Cyber Threat Intelligence Opportunities; 2 A Brief Review of the Book Chapters; References; Machine Learning Aided Static Malware Analysis:A Survey and Tutorial; 1 Introduction; 2 An Overview of Machine Learning-Aided Static Malware Detection; 2.1 Static Characteristics of PE Files; 2.2 Machine Learning Methods Used for Static-Based Malware Detection; 2.2.1 Statistical Methods
  • 2.2.2 Rule Based2.2.3 Distance Based; 2.2.4 Neural Networks; 2.2.5 Open Source and Freely Available ML Tools; 2.2.6 Feature Selection and Construction Process; 2.3 Taxonomy of Malware Static Analysis Using Machine Learning; 3 Approaches for Malware Feature Construction; 4 Experimental Design; 5 Results and Discussions; 5.1 Accuracy of ML-Aided Malware Detection Using Static Characteristics; 5.1.1 PE32 Header; 5.1.2 Bytes n-Gram; 5.1.3 Opcode n-Gram; 5.1.4 API Call n-Grams; 6 Conclusion; References
  • Application of Machine Learning Techniques to Detecting Anomalies in Communication Networks: Datasets and Feature Selection Algorithms1 Introduction; 1.1 Border Gateway Protocol (BGP); 1.2 Approaches for Detecting Network Anomalies; 2 Examples of BGP Anomalies; 3 Analyzed BGP Datasets; 3.1 Processing of Collected Data; 4 Extraction of Features from BGP Update Messages; 5 Review of Feature Selection Algorithms; 5.1 Fisher Algorithm; 5.2 Minimum Redundancy Maximum Relevance (mRMR) Algorithms; 5.3 Odds Ratio Algorithms; 5.4 Decision Tree Algorithm; 6 Conclusion; References
  • Application of Machine Learning Techniques to Detecting Anomalies in Communication Networks: Classification Algorithms1 Introduction; 1.1 Machine Learning Techniques; 2 Classification Algorithms; 2.1 Performance Metrics; 3 Support Vector Machine (SVM); 4 Long Short-Term Memory (LSTM) Neural Network; 5 Hidden Markov Model (HMM); 6 Naive Bayes; 7 Decision Tree Algorithm; 8 Extreme Learning Machine Algorithm (ELM); 9 Discussion; 10 Conclusion; References; Leveraging Machine LearningTechniques for Windows Ransomware Network Traffic Detection; 1 Introduction; 2 Related Works; 3 Methodology
  • 3.1 Data Collection Phase3.1.1 Malicious Applications; 3.1.2 Benign Applications; 3.2 Feature Selection and Extraction; 3.3 Machine Learning Classifiers; 4 Experiments and Results; 4.1 Evaluation Measures; 4.2 Malware Experiment and Results; 4.3 Result Comparison; 5 Conclusion and Future Works; References; Leveraging Support Vector Machine for Opcode Density Based Detection of Crypto-Ransomware; 1 Introduction; 2 Related Works and Research Literature; 3 Methodology; 3.1 Data Collection; 3.2 Feature Extraction; 3.3 Dataset Creation; 3.3.1 Merging the Data; 3.3.2 Normalising the Data
Extent
1 online resource (vi, 334 pages)
Form of item
online
Isbn
9783319739519
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-319-73951-9
Other physical details
illustrations (some color).
System control number
  • on1033603488
  • (OCoLC)1033603488

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