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E-raamat: Data Mining and Machine Learning in Cybersecurity

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(Louisiana Tech University, Ruston, USA), (Louisiana Tech University, Ruston, USA)
  • Formaat: 256 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: Taylor & Francis Inc
  • Keel: eng
  • ISBN-13: 9781040053409
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Data Mining and Machine Learning in CybersecuritySuurem pilt
  • Formaat: 256 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: Taylor & Francis Inc
  • Keel: eng
  • ISBN-13: 9781040053409
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With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need.

From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challengesdetailing cutting-edge machine learning and data mining techniques. It also:











Unveils cutting-edge techniques for detecting new attacks Contains in-depth discussions of machine learning solutions to detection problems Categorizes methods for detecting, scanning, and profiling intrusions and anomalies Surveys contemporary cybersecurity problems and unveils state-of-the-art machine learning and data mining solutions Details privacy-preserving data mining methods

This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.
List of Figures
xi
List of Tables
xv
Preface xvii
Authors xxi
1 Introduction
1(22)
1.1 Cybersecurity
2(3)
1.2 Data Mining
5(2)
1.3 Machine Learning
7(1)
1.4 Review of Cybersecurity Solutions
8(6)
1.4.1 Proactive Security Solutions
8(1)
1.4.2 Reactive Security Solutions
9(1)
1.4.2.1 Misuse/Signature Detection
10(1)
1.4.2.2 Anomaly Detection
10(3)
1.4.2.3 Hybrid Detection
13(1)
1.4.2.4 Scan Detection
13(1)
1.4.2.5 Profiling Modules
13(1)
1.5 Summary
14(1)
1.6 Further Reading
15(1)
References
16(7)
2 Classical Machine-Learning Paradigms for Data Mining
23(34)
2.1 Machine Learning
24(20)
2.1.1 Fundamentals of Supervised Machine-Learning Methods
24(1)
2.1.1.1 Association Rule Classification
24(1)
2.1.1.2 Artificial Neural Network
25(2)
2.1.1.3 Support Vector Machines
27(2)
2.1.1.4 Decision Trees
29(1)
2.1.1.5 Bayesian Network
30(1)
2.1.1.6 Hidden Markov Model
31(3)
2.1.1.7 Kalman Filter
34(1)
2.1.1.8 Bootstrap, Bagging, and AdaBoost
34(3)
2.1.1.9 Random Forest
37(1)
2.1.2 Popular Unsupervised Machine-Learning Methods
38(1)
2.1.2.1 k-Means Clustering
38(1)
2.1.2.2 Expectation Maximum
38(2)
2.1.2.3 k-Nearest Neighbor
40(1)
2.1.2.4 SOM ANN
41(1)
2.1.2.5 Principal Components Analysis
41(2)
2.1.2.6 Subspace Clustering
43(1)
2.2 Improvements on Machine-Learning Methods
44(6)
2.2.1 New Machine-Learning Algorithms
44(2)
2.2.2 Resampling
46(1)
2.2.3 Feature Selection Methods
46(1)
2.2.4 Evaluation Methods
47(2)
2.2.5 Cross Validation
49(1)
2.3 Challenges
50(3)
2.3.1 Challenges in Data Mining
50(1)
2.3.1.1 Modeling Large-Scale Networks
50(1)
2.3.1.2 Discovery of Threats
50(1)
2.3.1.3 Network Dynamics and Cyber Attacks
51(1)
2.3.1.4 Privacy Preservation in Data Mining
51(1)
2.3.2 Challenges in Machine Learning (Supervised Learning and Unsupervised Learning)
51(1)
2.3.2.1 Online Learning Methods for Dynamic Modeling of Network Data
52(1)
2.3.2.2 Modeling Data with Skewed Class Distributions to Handle Rare Event Detection
52(1)
2.3.2.3 Feature Extraction for Data with Evolving Characteristics
53(1)
2.4 Research Directions
53(2)
2.4.1 Understanding the Fundamental Problems of Machine-Learning Methods in Cybersecurity
54(1)
2.4.2 Incremental Learning in Cyberinfrastructures
54(1)
2.4.3 Feature Selection/Extraction for Data with Evolving Characteristics
54(1)
2.4.4 Privacy-Preserving Data Mining
55(1)
2.5 Summary
55(1)
References
55(2)
3 Supervised Learning for Misuse/Signature Detection
57(28)
3.1 Misuse/Signature Detection
58(2)
3.2 Machine Learning in Misuse/Signature Detection
60(1)
3.3 Machine-Learning Applications in Misuse Detection
61(21)
3.3.1 Rule-Based Signature Analysis
61(1)
3.3.1.1 Classification Using Association Rules
62(3)
3.3.1.2 Fuzzy-Rule-Based
65(3)
3.3.2 Artificial Neural Network
68(1)
3.3.3 Support Vector Machine
69(1)
3.3.4 Genetic Programming
70(3)
3.3.5 Decision Tree and CART
73(1)
3.3.5.1 Decision-Tree Techniques
74(1)
3.3.5.2 Application of a Decision Tree in Misuse Detection
75(2)
3.3.5.3 CART
77(2)
3.3.6 Bayesian Network
79(1)
3.3.6.1 Bayesian Network Classifier
79(3)
3.3.6.2 Naive Bayes
82(1)
3.4 Summary
82(1)
References
82(3)
4 Machine Learning for Anomaly Detection
85(30)
4.1 Introduction
85(1)
4.2 Anomaly Detection
86(1)
4.3 Machine Learning in Anomaly Detection Systems
87(1)
4.4 Machine-Learning Applications in Anomaly Detection
88(23)
4.4.1 Rule-Based Anomaly Detection (Table 1.3, C.6)
89(1)
4.4.1.1 Fuzzy Rule-Based (Table 1.3, C.6)
90(3)
4.4.2 ANN (Table 1.3, C.9)
93(1)
4.4.3 Support Vector Machines (Table 1.3, C.12)
94(1)
4.4.4 Nearest Neighbor-Based Learning (Table 1.3, C.11)
95(3)
4.4.5 Hidden Markov Model
98(1)
4.4.6 Kalman Filter
99(1)
4.4.7 Unsupervised Anomaly Detection
100(1)
4.4.7.1 Clustering-Based Anomaly Detection
101(2)
4.4.7.2 Random Forests
103(1)
4.4.7.3 Principal Component Analysis/Subspace
104(2)
4.4.7.4 One-Class Supervised Vector Machine
106(4)
4.4.8 Information Theoretic (Table 1.3, C.5)
110(1)
4.4.9 Other Machine-Learning Methods Applied in Anomaly Detection (Table 1.3, C.2)
110(1)
4.5 Summary
111(1)
References
112(3)
5 Machine Learning for Hybrid Detection
115(24)
5.1 Hybrid Detection
116(2)
5.2 Machine Learning in Hybrid Intrusion Detection Systems
118(1)
5.3 Machine-Learning Applications in Hybrid Intrusion Detection
119(16)
5.3.1 Anomaly-Misuse Sequence Detection System
119(1)
5.3.2 Association Rules in Audit Data Analysis and Mining (Table 1.4, D.4)
120(2)
5.3.3 Misuse-Anomaly Sequence Detection System
122(6)
5.3.4 Parallel Detection System
128(4)
5.3.5 Complex Mixture Detection System
132(2)
5.3.6 Other Hybrid Intrusion Systems
134(1)
5.4 Summary
135(1)
References
136(3)
6 Machine Learning for Scan Detection
139(20)
6.1 Scan and Scan Detection
140(2)
6.2 Machine Learning in Scan Detection
142(1)
6.3 Machine-Learning Applications in Scan Detection
143(13)
6.4 Other Scan Techniques with Machine-Learning Methods
156(1)
6.5 Summary
156(1)
References
157(2)
7 Machine Learning for Profiling Network Traffic
159(18)
7.1 Introduction
159(1)
7.2 Network Traffic Profiling and Related Network Traffic Knowledge
160(1)
7.3 Machine Learning and Network Traffic Profiling
161(1)
7.4 Data-Mining and Machine-Learning Applications in Network Profiling
162(12)
7.4.1 Other Profiling Methods and Applications
173(1)
7.5 Summary
174(1)
References
175(2)
8 Privacy-Preserving Data Mining
177(30)
8.1 Privacy Preservation Techniques in PPDM
180(4)
8.1.1 Notations
180(1)
8.1.2 Privacy Preservation in Data Mining
180(4)
8.2 Workflow of PPDM
184(5)
8.2.1 Introduction of the PPDM Workflow
184(1)
8.2.2 PPDM Algorithms
185(1)
8.2.3 Performance Evaluation of PPDM Algorithms
185(4)
8.3 Data-Mining and Machine-Learning Applications in PPDM
189(13)
8.3.1 Privacy Preservation Association Rules (Table 1.1, A.4)
189(4)
8.3.2 Privacy Preservation Decision Tree (Table 1.1, A.6)
193(1)
8.3.3 Privacy Preservation Bayesian Network (Table 1.1, A.2)
194(3)
8.3.4 Privacy Preservation KNN (Table 1.1, A.7)
197(2)
8.3.5 Privacy Preservation k-Means Clustering (Table 1.1, A.3)
199(2)
8.3.6 Other PPDM Methods
201(1)
8.4 Summary
202(2)
References
204(3)
9 Emerging Challenges in Cybersecurity
207(18)
9.1 Emerging Cyber Threats
208(5)
9.1.1 Threats from Malware
208(1)
9.1.2 Threats from Botnets
209(2)
9.1.3 Threats from Cyber Warfare
211(1)
9.1.4 Threats from Mobile Communication
211(1)
9.1.5 Cyber Crimes
212(1)
9.2 Network Monitoring, Profiling, and Privacy Preservation
213(5)
9.2.1 Privacy Preservation of Original Data
213(1)
9.2.2 Privacy Preservation in the Network Traffic Monitoring and Profiling Algorithms
214(1)
9.2.3 Privacy Preservation of Monitoring and Profiling Data
215(1)
9.2.4 Regulation, Laws, and Privacy Preservation
215(1)
9.2.5 Privacy Preservation, Network Monitoring, and Profiling Example: PRISM
216(2)
9.3 Emerging Challenges in Intrusion Detection
218(4)
9.3.1 Unifying the Current Anomaly Detection Systems
219(1)
9.3.2 Network Traffic Anomaly Detection
219(1)
9.3.3 Imbalanced Learning Problem and Advanced Evaluation Metrics for IDS
220(1)
9.3.4 Reliable Evaluation Data Sets or Data Generation Tools
221(1)
9.3.5 Privacy Issues in Network Anomaly Detection
222(1)
9.4 Summary
222(1)
References
223(2)
Index 225
Dr. Sumeet Dua is currently an upchurch endowed associate professor and the coordinator of IT research at Louisiana Tech University, Ruston, USA. He received his PhD in computer science from Louisiana State University, Baton Rouge, Louisiana.

His areas of expertise include data mining, image processing and computational decision support, pattern recognition, data warehousing, biomedical informatics, and heterogeneous distributed data integration. The National Science Foundation (NSF), the National Institutes of Health (NIH), the Air Force Research Laboratory (AFRL), the Air Force Office of Sponsored Research (AFOSR), the National Aeronautics and Space Administration (NASA), and the Louisiana Board of Regents (LA-BoR) have funded his research with over $2.8 million. He frequently serves as a study section member (expert panelist) for the National Institutes of Health (NIH) and panelist for the National Science Foundation (NSF)/CISE Directorate. Dr. Dua has chaired several conference sessions in the area of data mining and is the program chair for the Fifth International Conference on Information Systems, Technology, and Management (ICISTM-2011). He has given more than 26 invited talks on data mining and its applications at international academic and industry arenas, has advised more than 25 graduate theses, and currently advises several graduate students in the discipline. Dr. Dua is a coinventor of two issued U.S. patents, has (co-)authored more than 50 publications and book chapters, and has authored or edited four books. Dr. Dua has received the Engineering and Science Foundation Award for Faculty Excellence (2006) and the Faculty Research Recognition Award (2007), has been recognized as a distinguished researcher (20042010) by the Louisiana Biomedical Research Network (NIH-sponsored), and has won the Outstanding Poster Award at the NIH/NCI caBIGNCRI Informatics Joint Conference; Biomedical Informatics without Borders: From Collaboration to Implementation. Dr. Dua is a senior member of the IEEE Computer Society, a senior member of the ACM, and a member of SPIE and the American Association for Advancement of Science.

Dr. Xian Du is a research associate and postdoctoral fellow at Louisiana Tech University, Ruston, USA. He worked as a postdoctoral researcher at the Centre National de la Recherche Scientifique (CNRS) in the CREATIS Lab, Lyon, France, from 2007 to 2008 and served as a software engineer in Kikuze Solutions Pte. Ltd., Singapore, in 2006. He received his PhD from the SingaporeMIT Alliance (SMA) Programme at the National University of Singapore in 2006.

Dr. Xian Dus current research focus is on high-performance computing using machine-learning and data-mining technologies, data-mining applications for cybersecurity, software in multiple computer operational environments, and clustering theoretical research. He has broad experience in machine-learning applications in industry and academic research at high-level research institutes. During his work in the CREATIS Lab in France, he developed a 3D smooth active contour technology for knee cartilage MRI image segmentation. He led a small research and development group to develop color control plug-ins for an RGB color printer to connect to the Windows system through image processing GDI functions for Kikuze Solutions.

He helped to build an intelligent e-diagnostics system for reducing mean time to repair wire-bonding machines at National Semiconductor Ltd., Singapore (NSC). During his PhD dissertation research at the SMA, he developed an intelligent color print process control system for color printers. Dr. Dus major research interests are machine-learning and data-mining applications, heterogeneous data integration and visualization, cybersecurity, and clustering theoretical research.
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