Description
Taylor & Francis Ltd Data Mining And Machine Learning In Cybersecurity 2011 Edition by Sumeet Dua, Xian Du
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 challenges-detailing cutting-edge machine learning and data mining techniques. It also: Unveils cutting-edge techniques for detecting new attacksContains in-depth discussions of machine learning solutions to detection problemsCategorizes methods for detecting, scanning, and profiling intrusions and anomaliesSurveys 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. IntroductionCybersecurityData MiningMachine LearningReview on Cybersecurity SolutionsProactive Security SolutionsReactive Security SolutionsFurther Reading Classical Machine-Learning Paradigms for Data MiningMachine LearningFundamentals of Supervised Machine-Learning MethodsPopular Unsupervised Machine-Learning MethodsImprovements on Machine-Learning MethodsNew Machine-Learning AlgorithmsResamplingFeature Selection MethodsEvaluation MethodsCross ValidationChallengesChallenges in Data MiningChallenges in Machine Learning (Supervised Learning and Unsupervised Learning)Research DirectionsUnderstanding the Fundamental Problems of Machine-Learning Methods in CybersecurityIncremental Learning in CyberinfrastructuresFeature Selection/Extraction for Data with Evolving CharacteristicsPrivacy-Preserving Data MiningSupervised Learning for Misuse/Signature DetectionMisuse/Signature DetectionMachine Learning in Misuse/Signature DetectionMachine-Learning Applications in Misuse DetectionRule-Based Signature AnalysisArtificial Neural NetworkSupport Vector MachineGenetic ProgrammingDecision Tree and CARTBayesian NetworkMachine Learning for Anomaly DetectionIntroductionAnomaly DetectionMachine Learning in Anomaly Detection SystemsMachine-Learning Applications in Anomaly DetectionRule-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 ModelKalman FilterUnsupervised Anomaly DetectionInformation Theoretic (Table 1.3, C.5)Other Machine-Learning Methods Applied in Anomaly Detection (Table 1.3, C.2)Machine Learning for Hybrid DetectionHybrid DetectionMachine Learning in Hybrid Intrusion Detection SystemsMachine-Learning Applications in Hybrid Intrusion DetectionAnomaly-Misuse Sequence Detection SystemAssociation Rules in Audit Data Analysis and Mining (Table 1.4, D.4)Misuse-Anomaly Sequence Detection SystemParallel Detection SystemComplex Mixture Detection SystemOther Hybrid Intrusion SystemsMachine Learning for Scan DetectionScan and Scan DetectionMachine Learning in Scan DetectionMachine-Learning Applications in Scan DetectionOther Scan Techniques with Machine-Learning MethodsMachine Learning for Profiling Network TrafficIntroductionNetwork Traffic Profiling and Related Network Traffic KnowledgeMachine Learning and Network Traffic ProfilingData-Mining and Machine-Learning Applications in Network ProfilingOther Profiling Methods and Applications.Privacy-Preserving Data MiningIntroductionPrivacy Preservation Techniques in PPDMNotationsPrivacy Preservation in Data MiningWorkflow of PPDMIntroduction of the PPDM WorkflowPPDM AlgorithmsPerformance Evaluation of PPDM AlgorithmsData-Mining and Machine-Learning Applications in PPDMPrivacy 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 MethodsEmerging Challenges in CybersecurityEmerging Cyber ThreatsThreats from MalwareThreats from BotnetsThreats from Cyber WarfareThreats from Mobile CommunicationCyber CrimesNetwork Monitoring, Profiling, and Privacy PreservationPrivacy Preservation of Original DataPrivacy Preservation in the Network Traffic Monitoring and Profiling AlgorithmsPrivacy Preservation of Monitoring and Profiling Data Regulation, Laws, and Privacy PreservationPrivacy Preservation, Network Monitoring, and Profiling Example: PRISMEmerging Challenges in Intrusion DetectionUnifying the Current Anomaly Detection SystemsNetwork Traffic Anomaly DetectionImbalanced Learning Problem and Advanced Evaluation Metrics for IDSReliable Evaluation Data Sets or Data Generation ToolsPrivacy Issues in Network Anomaly DetectionIndex Each chapter includes a Summary and References