Data Mining and Machine Learning in Cybersecurity

Data Mining and Machine Learning in Cybersecurity

by Sumeet Dua, Xian Du

Hardcover(New Edition)

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Product Details

ISBN-13: 9781439839423
Publisher: Taylor & Francis
Publication date: 05/16/2011
Edition description: New Edition
Pages: 256
Product dimensions: 6.00(w) x 9.30(h) x 0.80(d)

About the Author

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 (2004–2010) by the Louisiana Biomedical Research Network (NIH-sponsored), and has won the Outstanding Poster Award at the NIH/NCI caBIG—NCRI 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 Singapore–MIT Alliance (SMA) Programme at the National University of Singapore in 2006.

Dr. Xian Du’s 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. Du’s major research interests are machine-learning and data-mining applications, heterogeneous data integration and visualization, cybersecurity, and clustering theoretical research.

Table of Contents

Data Mining
Machine Learning
Review on Cybersecurity Solutions
Proactive Security Solutions
Reactive Security Solutions
Further Reading

Classical Machine-Learning Paradigms for Data Mining
Machine Learning
Fundamentals of Supervised Machine-Learning Methods
Popular Unsupervised Machine-Learning Methods
Improvements on Machine-Learning Methods
New Machine-Learning Algorithms
Feature Selection Methods
Evaluation Methods
Cross Validation
Challenges in Data Mining
Challenges in Machine Learning (Supervised Learning and Unsupervised Learning)
Research Directions
Understanding the Fundamental Problems of Machine-Learning Methods in Cybersecurity
Incremental Learning in Cyberinfrastructures
Feature Selection/Extraction for Data with Evolving Characteristics
Privacy-Preserving Data Mining

Supervised Learning for Misuse/Signature Detection
Misuse/Signature Detection
Machine Learning in Misuse/Signature Detection
Machine-Learning Applications in Misuse Detection
Rule-Based Signature Analysis
Artificial Neural Network
Support Vector Machine
Genetic Programming
Decision Tree and CART
Bayesian Network

Machine Learning for Anomaly Detection
Anomaly Detection
Machine Learning in Anomaly Detection Systems
Machine-Learning Applications in Anomaly Detection
Rule-Based Anomaly Detection (Table 1.3, C.6)
Fuzzy Rule-Based (Table 1.3, C.6)
ANN (Table 1.3, C.9)
Support Vector Machines (Table 1.3, C.12)
Nearest Neighbor-Based Learning (Table 1.3, C.11)
Hidden Markov Model
Kalman Filter
Unsupervised Anomaly Detection
Information Theoretic (Table 1.3, C.5)
Other Machine-Learning Methods Applied in Anomaly Detection (Table 1.3, C.2)

Machine Learning for Hybrid Detection
Hybrid Detection
Machine Learning in Hybrid Intrusion Detection Systems
Machine-Learning Applications in Hybrid Intrusion Detection
Anomaly–Misuse Sequence Detection System
Association Rules in Audit Data Analysis and Mining (Table 1.4, D.4)
Misuse–Anomaly Sequence Detection System
Parallel Detection System
Complex Mixture Detection System
Other Hybrid Intrusion Systems

Machine Learning for Scan Detection
Scan and Scan Detection
Machine Learning in Scan Detection
Machine-Learning Applications in Scan Detection
Other Scan Techniques with Machine-Learning Methods

Machine Learning for Profiling Network Traffic
Network Traffic Profiling and Related Network Traffic Knowledge
Machine Learning and Network Traffic Profiling
Data-Mining and Machine-Learning Applications in Network Profiling
Other Profiling Methods and Applications.

Privacy-Preserving Data Mining
Privacy Preservation Techniques in PPDM
Privacy Preservation in Data Mining
Workflow of PPDM
Introduction of the PPDM Workflow
PPDM Algorithms
Performance Evaluation of PPDM Algorithms
Data-Mining and Machine-Learning Applications in PPDM
Privacy Preservation Association Rules (Table 1.1, A.4)
Privacy Preservation Decision Tree (Table 1.1, A.6)
Privacy Preservation Bayesian Network (Table 1.1, A.2)
Privacy Preservation KNN (Table 1.1, A.7)
Privacy Preservation k-Means Clustering (Table 1.1, A.3)
Other PPDM Methods

Emerging Challenges in Cybersecurity
Emerging Cyber Threats
Threats from Malware
Threats from Botnets
Threats from Cyber Warfare
Threats from Mobile Communication
Cyber Crimes
Network Monitoring, Profiling, and Privacy Preservation
Privacy Preservation of Original Data
Privacy Preservation in the Network Traffic Monitoring and Profiling Algorithms
Privacy Preservation of Monitoring and Profiling Data
Regulation, Laws, and Privacy Preservation
Privacy Preservation, Network Monitoring, and Profiling Example: PRISM
Emerging Challenges in Intrusion Detection
Unifying the Current Anomaly Detection Systems
Network Traffic Anomaly Detection
Imbalanced Learning Problem and Advanced Evaluation Metrics for IDS
Reliable Evaluation Data Sets or Data Generation Tools
Privacy Issues in Network Anomaly Detection


Each chapter includes a Summary and References

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