Cyber threat intelligence. ([2018])

• Record Type: Book
• Further Information: Note: Ali Dehghantanha, Mauro Conti, Tooska Dargahi, editors.
• Editors: Dehghantanha, Ali; (Associate professor), Conti, Mauro; Dargahi, Tooska
• 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.
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2018
• Extent: 1 online resource (vi, 334 pages), illustrations (some color)
• Subjects: 005.8; Computer science; Computer security; Internet -- Security measures; COMPUTERS -- Security -- General; Computer Science; Security; Artificial Intelligence (incl. Robotics); Information Systems and Communication Service; Computer Communication Networks; Computer security; Internet -- Security measures; Computers -- Intelligence (AI) & Semantics; Computers -- Online Services -- General; Computers -- Hardware -- Network Hardware; Artificial intelligence; Computer networking & communications; Network hardware; Data protection; Artificial intelligence; Information systems; Computer Communication Networks; Computer security; Electronic books
• Languages: English
• ISBNs: 9783319739519; 3319739514
• Related ISBNs: 9783319739502; 3319739506
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed May 2, 2018).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.