Cyber Threat Intelligence
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 Thi...
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| Published in | Advances in information security Vol. 70 |
|---|---|
| Main Authors | , , |
| Format | eBook Book |
| Language | English |
| Published |
Cham
Springer Nature
2018
Springer Springer International Publishing AG Springer International Publishing |
| Edition | 1 |
| Series | Advances in Information Security |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783319739519 3319739514 3319739506 9783319739502 |
| ISSN | 1568-2633 2512-2193 |
| DOI | 10.1007/978-3-319-73951-9 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. |
| Author | Dargahi, Tooska Dehghantanha, Ali Conti, Mauro |
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| Notes | Includes bibiographical references and index |
| OCLC | 1033603488 |
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| PublicationSeriesTitle | Advances in Information Security |
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| PublicationTitle | Advances in information security |
| PublicationYear | 2018 |
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| RelatedPersons | Jajodia, Sushil |
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| Snippet | 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... |
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| SubjectTerms | Artificial Intelligence Computer Communication Networks Computer programming, programs, data Computer Science Computer security Data protection Information systems Information Systems and Communication Service Internet -- Security measures Security |
| TableOfContents | Application of Machine Learning Techniques to Detecting Anomalies in Communication Networks: Classification Algorithms -- 1 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 Phase -- 3.1.1 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 -- 3.3.3 Opcode Breakdown -- 3.4 Machine Learning Classification -- 3.4.1 SVM and Kernel Functions -- 3.4.2 Feature/Attribute Selection Process -- 3.5 Implementation -- 3.5.1 Pre-processing the Dataset (1) -- 3.5.2 Creating the Training and Test Datasets (2) -- 3.5.3 Training and Testing the SVM Classifier (3.1) -- 3.5.4 Training and Testing the Attribute Selection Evaluators -- 3.5.5 Evaluation Metrics -- 3.5.6 Machine Specifications -- 4 Experiments and Results -- 4.1 SMO (Two Classes) -- 4.2 SMO (Six Classes) -- 4.3 Training and Testing the Attribute Selection Evaluators -- 4.3.1 CFSSubsetEval 4.3.2 CorrelationAttributeEval -- 4.3.3 GainRatioAttributeEval -- 4.3.4 InfoGainAttributeEval -- 4.3.5 OneRAttributeEval -- 4.3.6 PrincipalComponents -- 4.3.7 RelieffAttributeEval -- 4.3.8 SymmetricalUncertAttributeEval -- 4.4 Tuning the Attribute Selection Evaluators to Achieve Further Feature Reduction (4) -- 4.5 Important Opcodes -- 5 Conclusion -- References -- BoTShark: A Deep Learning Approach for Botnet Traffic Detection -- 1 Introduction -- 2 Related Work -- 3 Background: Deep Learning -- 3.1 Autoencoders -- 3.2 Convolutional Neural Network (CNN) -- 4 Data Collection and Primary Feature Extraction -- 5 Proposed BoTShark -- 5.1 BoTShark-SA: Using Stacked Autoencoders -- 5.2 SocialBoTShrak-CNN: Using CNNs -- 6 Evaluation -- 7 Conclusion -- References -- A Practical Analysis of the Rise in Mobile Phishing -- 1 Introduction -- 2 Measuring the Impact of Phishing -- 3 Methodology for Visitors to Phishing Websites -- 4 Mobile Phishing Kits in the Wild -- 5 Mobile Phishing Campaigns -- 6 Recommended Changes -- 7 Conclusion -- A.1 Appendix -- References -- PDF-Malware Detection: A Survey and Taxonomyof Current Techniques -- 1 Introduction -- 2 Background on Malicious PDF Files -- 2.1 The Portable Document Format -- 2.2 PDF Document Obfuscation Techniques -- 3 Taxonomy of PDF Malware Detection Approaches -- 3.1 Features -- 3.1.1 Metadata -- 3.1.2 JavaScript -- 3.1.3 Whole File -- 3.1.4 Feature Selection -- 3.2 Detection Approaches -- 3.2.1 Statistical Analysis -- 3.2.2 Machine Learning Classification -- 3.2.3 Clustering -- 3.2.4 Signature Matching -- 4 State of the Art Discussion -- 4.1 Related Works -- 5 Conclusions -- References -- Adaptive Traffic Fingerprinting for Darknet Threat Intelligence -- 1 Introduction -- 2 Background -- 2.1 Analysis of Attack Vectors in Tor -- 2.2 Hidden Services -- 2.3 Combining Methods 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 Based -- 2.2.3 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 Algorithms -- 1 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 3 Adaptive Traffic Association and BGP Interception Algorithm (ATABI) -- 3.1 BGP Interception Component -- 3.2 MITM Component -- 3.3 Detection Scheme -- 4 Experimentation and Results -- 4.1 Experiment Setup -- 4.2 Evaluation Criteria -- 4.3 Results -- 5 Discussion -- 5.1 Use Cases -- 5.2 Proposed Defences -- 6 Conclusion and Future Work -- References -- A Model for Android and iOS Applications Risk Calculation: CVSS Analysis and Enhancement Using Case-Control Studies -- 1 Introduction -- 1.1 Background -- 1.2 Impact Sub-Score -- 1.3 Exploitability Sub-Score -- 1.4 Research Data Set -- 1.5 The CVSS Analysis of Data Set -- 2 Proposed Model -- 2.1 Results and Discussion -- 3 Conclusions and Future Works -- References -- A Honeypot Proxy Framework for Deceiving Attackers with Fabricated Content -- 1 Introduction -- 2 Deceiving Cyber Adversaries -- 3 Desirable Properties for a Fake Content Generator -- 4 The Design and Implementation of a Fake Content Generator -- 4.1 A Conceptual Design of a Fake Content Generator -- 4.2 The Implementation -- 4.3 An Example on the Usage of Honeyproxy -- 4.4 Recognizing Names Using Regular Expressions -- 4.5 Fake Entity Generation -- 5 Experiments -- 5.1 Recognizing Entity Attributes -- 5.2 Performance -- 6 Discussion and Limitations -- 7 Related Work -- 8 Conclusions and Future Work -- References -- Investigating the Possibility of Data Leakage in Time of Live VM Migration -- 1 Introduction -- 2 Background on Live Virtual Machine Migration -- 2.1 Memory Migration -- 2.2 Migration Algorithms -- 2.3 Live VM Migration Process -- 3 Security Threat Model -- 3.1 Threat Model -- 3.2 Security Threats and Attacks -- 3.2.1 Control Plane -- 3.2.2 Data Plane -- 3.2.3 Migration Module -- 3.2.4 Insecure Algorithms and Implementations -- 4 Secure Live Migration -- 4.1 Essential Security Requirements -- 4.2 Existing Solutions 4.2.1 Trusted Computing -- 4.2.2 VM-vTPM Live Migration -- 4.2.3 Trusted Third Party -- 4.2.4 Role-Based Migration -- 4.2.5 VLANs -- 5 Uncovered Threats with Potential Research Directions -- 5.1 Bugs in VMM -- 5.2 Replay of VM Data Messages -- 5.3 Privileged Access -- 5.4 Lack of Access Control -- 6 Proposed Secure Live VM Migration Protocol -- 7 Conclusion -- References -- Forensics Investigation of OpenFlow-Based SDN Platforms -- 1 Introduction -- 2 Related Work -- 3 Framework Specification and Design -- 4 Framework Development and Implementation -- 5 SDN Southbound Forensics Tool -- 6 Testing Environment Setup -- 7 Evaluation and Discussion -- 8 Conclusion -- References -- Mobile Forensics: A Bibliometric Analysis -- 1 Introduction -- 2 Methodology -- 2.1 Web of Science -- 3 Finding in Publications Distribution -- 3.1 Productivity -- 3.2 Research Areas -- 3.3 Institutions -- 3.4 Impact Journals -- 3.5 Highly Cited Articles -- 4 Conclusion and Future Works -- References -- Emerging from the Cloud: A Bibliometric Analysis of Cloud Forensics Studies -- 1 Introduction -- 2 Methodology -- 3 Results and Discussion -- 3.1 Productivity -- 3.2 Research Areas -- 3.3 Institutions -- 3.4 Impact Journals -- 3.5 Highly-Cited Articles -- 3.6 Keywords Frequency -- 4 Challenges and Future Trends -- 4.1 Evidence Identification -- 4.2 Legal Issues in the Cloud -- 4.3 Data Collection and Preservation -- 4.4 Analysis and Presentation -- 4.5 Future Trends -- 5 Conclusion -- References -- Index |
| Title | Cyber Threat Intelligence |
| URI | http://digital.casalini.it/9783319739519 https://cir.nii.ac.jp/crid/1130577121818496654 https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=5376067 http://link.springer.com/10.1007/978-3-319-73951-9 https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9783319739519&uid=none http://usir.salford.ac.uk/id/eprint/47128/ |
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