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Data Mining and Machine Learning in Cybersecurity

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ISBN-10: 1439839425

ISBN-13: 9781439839423

Edition: 2011

Authors: Sumeet Dua, Xian Du

List price: $75.99
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Book details

List price: $75.99
Copyright year: 2011
Publisher: Taylor & Francis Group
Publication date: 5/20/2011
Binding: Hardcover
Pages: 256
Size: 6.00" wide x 9.00" long x 0.75" tall
Weight: 1.100
Language: English

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…    

List of Figures
List of Tables
Data Mining
Machine Learning
Review of Cybersecurity Solutions
Proactive Security Solutions
Reactive Security Solutions
Misuse/Signature Detection
Anomaly Detection
Hybrid Detection
Scan Detection
Profiling Modules
Further Reading
Classical Machine-Learning Paradigms for Data Mining
Machine Learning
Fundamentals of Supervised Machine-Learning Methods
Association Rule Classification
Artificial Neural Network
Support Vector Machines
Decision Trees
Bayesian Network
Hidden Markov Model
Kalman Filter
Bootstrap, Bagging, and AdaBoost
Random Forest
Popular Unsupervised Machine-Learning Methods
k-Means Clustering
Expectation Maximum
k-Nearest Neighbor
Principal Components Analysis
Subspace Clustering
Improvements on Machine-Learning Methods
New Machine-Learning Algorithms
Feature Selection Methods
Evaluation Methods
Cross Validation
Challenges in Data Mining
Modeling Large-Scale Networks
Discovery of Threats
Network Dynamics and Cyber Attacks
Privacy Preservation in Data Mining
Challenges in Machine Learning (Supervised Learning and Unsupervised Learning)
Online Learning Methods for Dynamic Modeling of Network Data
Modeling Data with Skewed Class Distributions to Handle Rare Event Detection
Feature Extraction for Data with Evolving Characteristics
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
Classification Using Association Rules
Artificial Neural Network
Support Vector Machine
Genetic Programming
Decision Tree and CART
Decision-Tree Techniques
Application of a Decision Tree in Misuse Detection
Bayesian Network
Bayesian Network Classifier
Na�ve Bayes
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.ll)
Hidden Markov Model
Kalman Filter
Unsupervised Anomaly Detection
Clustering-Based Anomaly Detection
Random Forests
Principal Component Analysis/Subspace
One-Class Supervised Vector Machine
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