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Intrusion detection networks : a key to collaborative security

Author: Carol Fung; Raouf Boutaba
Publisher: Boca Raton : CRC Press, [2014]
Edition/Format:   Print book : EnglishView all editions and formats

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Document Type: Book
All Authors / Contributors: Carol Fung; Raouf Boutaba
ISBN: 9781466564121 1466564121
OCLC Number: 793224635
Description: xxii, 239 pages ; 24 cm
Contents: INTRODUCTION CYBER INTRUSIONS AND INTRUSION DETECTION Cyber IntrusionsIntroductionOverview of Cyber Intrusions Malware Vulnerabilities Exploitation Denial-of-Service Attack Web-Based Attacks DNS Attack Organized Attacks and Botnets Spam and Phishing Mobile Device Security Cyber Crime and Cyber WarfareA Taxonomy of Cyber IntrusionsSummaryIntrusion DetectionIntrusion Detection Systems Signature-Based and Anomaly-Based IDSs Host-Based and Network-Based IDSs Other Types of IDSs Strength and Limitations of IDSsCollaborative Intrusion Detection Networks Motivation for IDS Collaboration Challenges of IDS CollaborationOverview of Existing Intrusion Detection Networks Cooperation Topology Cooperation Scope Collaboration Type Specialization Cooperation Technologies and Algorithms Data Correlation Trust Management Load Balancing TaxonomySelected Intrusion Detection Networks Indra DOMINO DShield NetShield CIDS Gossip Worminator ABDIAS CRIM ALPACAS CDDHT SmartScreen Filter CloudAV FFCIDN CMDASummaryDESIGN OF AN INTRUSION DETECTION NETWORK Collaborative Intrusion Detection Networks Architecture DesignIntroductionCollaboration Framework Network Join Process Consultation Requests Test Messages Communication Overlay Mediator Trust Management Acquaintance Management Resource Management Feedback AggregationDiscussion Privacy Issues Insider AttacksSummaryTrust ManagementIntroductionBackgroundTrust Management Model Satisfaction Mapping Dirichlet-Based Model Evaluating the Trustworthiness of a PeerTest Message Exchange Rate and Scalability of Our SystemRobustness against Common Threats Newcomer Attacks Betrayal Attacks Collusion Attacks Inconsistency AttacksSimulations and Experimental Results Simulation Setting Modeling the Expertise Level of a Peer Deception Models Trust Values and Confidence Levels for Honest Peers Trust Values for Dishonest Peers Robustness of Our Trust Model Scalability of Our Trust Model Efficiency of Our Trust ModelConclusions and Future Work Collaborative DecisionIntroductionBackgroundCollaborative Decision Model Modeling of Acquaintances Collaborative DecisionSequential Hypothesis Testing Threshold ApproximationPerformance Evaluation Simulation Simple Average Model Weighted Average Model Bayesian Decision Model Modeling of a Single IDS Detection Accuracy and Cost Cost under Homogeneous Environment Cost under Heterogeneous Environment Cost and the Number of Acquaintances Sequential Consultation Robustness and Scalability of the SystemConclusionResource ManagementIntroductionBackgroundResource Management and Incentive Design Modeling of Resource Allocation Characterization of Nash Equilibrium Incentive PropertiesPrimal / Dual Iterative AlgorithmExperiments and Evaluation Nash Equilibrium Computation Nash Equilibrium Using Distributed Computation Robustness Evaluation Free-Riding Denial-of-Service (DoS) Attacks Dishonest Insiders Large-Scale Simulation Conclusion Collaborators Selection and ManagementIntroductionBackgroundIDS Identification and Feedback Aggregation Detection Accuracy for a Single IDS Feedback AggregationAcquaintance Management Problem Statement Acquaintance Selection Algorithm Acquaintance Management AlgorithmEvaluation Simulation Setting Determining the Test Message Rate Efficiency of Our Feedback Aggregation Cost and the Number of Collaborators Efficiency of Acquaintance Selection Algorithms Evaluation of Acquaintance Management Algorithm Convergence Stability Incentive Compatibility RobustnessConclusion and Future WorkOTHER TYPES OF IDN DESIGNKnowledge-Based Intrusion Detection Networks and Knowledge PropagationIntroductionBackgroundKnowledge Sharing IDN Architecture Network Topology Communication Framework Snort Rules Authenticated Network Join Operation Feedback Collector Trust Evaluation and Acquaintance Management Knowledge Propagation Control An ExampleKnowledge Sharing and Propagation Model Lower Level - Public Utility Optimization Upper Level - Private Utility Optimization Tuning Parameter Rij Nash Equilibrium Price of Anarchy Analysis Knowledge PropagationBayesian Learning and Dynamic Algorithms Bayesian Learning Model for Trust Dirichlet Learning Model for Knowledge Quality Credible-Bound Estimation of Trust Dynamic Algorithm to Find the Prime NE at NodeEvaluation Simulation Setup Trust Value Learning Convergence of Distributed Dynamic Algorithm Scalability and Quality of Information (QoI) Incentive Compatibility and Fairness Robustness of the SystemConclusionCollaborative Malware Detection NetworksIntroductionBackground Collaborative Malware Detection Decision Models for Collaborative Malware Detection Static Threshold Weighted Average Decision Tree Bayesian DecisionCollaboration Framework Architecture Design Communication Overhead and Privacy Issue Adversaries and Free-RidingCollaborative Decision Model Problem Statement and RevMatch Model Feedback Relaxation Labeled History UpdateEvaluation Data Sets Experiment Setting Ranking of AVs Static Threshold Weighted Average Decision Tree Bayesian Decision RevMatch Comparison between Different Decision Models Robustness against Insider Attacks Acquaintance List Length and EfficiencyDiscussion Runtime Efficiency on Decision Partial Feedback Tuning Flexibility Comparison Zero-Day Malware Detection History Poison Flooding AttackConclusion and Future WorkCONCLUSION APPENDICESExamples of Intrusion Detection Rules and AlertsExamples of Snort RulesExample of an Intrusion Alert in IDMEF FormatProofsProof of Proposition 9.4.3Proof of Theorem 9.2Proof of Proposition 9.4.4Proof of Proposition 9.4.5Proof of Proposition 9.4.6ReferencesIndex
Responsibility: Carol Fung and Raouf Boutaba.


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