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Title Cyber threat intelligence / Ali Dehghantanha, Mauro Conti, Tooska Dargahi, editors
Published Cham, Switzerland : Springer, [2018]
Online access available from:
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Description 1 online resource (vi, 334 pages) : illustrations (some color)
Series Advances in information security, 1568-2633 ; volume 70
Advances in information security ; 70. 1568-2633
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
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
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
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
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
Notes Includes index
Online resource; title from PDF title page (SpringerLink, viewed May 2, 2018)
Subject Computer security.
Internet -- Security measures.
Form Electronic book
Author Conti, Mauro (Associate professor), editor
Dargahi, Tooska, editor
Dehghantanha, Ali, editor
ISBN 3319739514 (electronic bk.)
9783319739519 (electronic bk.)