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Author Ye, Nong
Title Secure Computer and Network Systems : Modeling, Analysis and Design
Imprint New York : John Wiley & Sons, Incorporated, 2008
©2008
book jacket
Edition 1st ed
Descript 1 online resource (356 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Note Intro -- Secure Computer and Network Systems -- Contents -- Preface -- Part I An Overview of Computer and Network Security -- 1 Assets, Vulnerabilities and Threats of Computer and Network Systems -- 1.1 Risk Assessment -- 1.2 Assets and Asset Attributes -- 1.2.1 Resource, Process and User Assets and their Interactions -- 1.2.2 Cause-Effect Chain of Activity, State and Performance -- 1.2.3 Asset Attributes -- 1.3 Vulnerabilities -- 1.3.1 Boundary Condition Error -- 1.3.2 Access Validation Error and Origin Validation Error -- 1.3.3 Input Validation Error -- 1.3.4 Failure to Handle Exceptional Conditions -- 1.3.5 Synchronization Errors -- 1.3.6 Environment Error -- 1.3.7 Configuration Error -- 1.3.8 Design Error -- 1.3.9 Unknown Error -- 1.4 Threats -- 1.4.1 Objective, Origin, Speed and Means of Threats -- 1.4.2 Attack Stages -- 1.5 Asset Risk Framework -- 1.6 Summary -- References -- 2 Protection of Computer and Network Systems -- 2.1 Cyber Attack Prevention -- 2.1.1 Access and Flow Control -- 2.1.2 Secure Computer and Network Design -- 2.2 Cyber Attack Detection -- 2.2.1 Data, Events and Incidents -- 2.2.2 Detection -- 2.2.3 Assessment -- 2.3 Cyber Attack Response -- 2.4 Summary -- References -- Part II Secure System Architecture and Design -- 3 Asset Protection-Driven, Policy-Based Security Protection Architecture -- 3.1 Limitations of a Threat-Driven Security Protection Paradigm -- 3.2 A new, Asset Protection-Driven Paradigm of Security Protection -- 3.2.1 Data to Monitor: Assets and Asset Attributes -- 3.2.2 Events to Detect: Mismatches of Asset Attributes -- 3.2.3 Incidents to Analyze and Respond: Cause-Effect chains of Mismatch Events -- 3.2.4 Proactive Asset Protection against Vulnerabilities -- 3.3 Digital Security Policies and Policy-Based Security Protection -- 3.3.1 Digital Security Policies -- 3.3.2 Policy-Based Security Protection
3.4 Enabling Architecture and Methodology -- 3.4.1 An Asset Protection Driven Security Architecture (APDSA) -- 3.4.2 An Inside-Out and Outside-In (IOOI) Methodology of Gaining Knowledge about Data, Events and Incidents -- 3.5 Further Research Issues -- 3.5.1 Technologies of Asset Attribute Data Acquisition -- 3.5.2 Quantitative Measures of Asset Attribute Data and Mismatch Events -- 3.5.3 Technologies for Automated Monitoring, Detection, Analysis and Control of Data, Events, Incidents and COA -- 3.6 Summary -- References -- 4 Job Admission Control for Service Stability -- 4.1 A Token Bucket Method of Admission Control in DiffServ and InteServ Models -- 4.2 Batch Scheduled Admission Control (BSAC) for Service Stability -- 4.2.1 Service Stability in Service Reservation for Instantaneous Jobs -- 4.2.2 Description of BSAC -- 4.2.3 Performance Advantage of the BSAC Router Model Over a Regular Router Model -- 4.3 Summary -- References -- 5 Job Scheduling Methods for Service Differentiation and Service Stability -- 5.1 Job Scheduling Methods for Service Differentiation -- 5.1.1 Weighted Shortest Processing Time (WSPT), Earliest Due Date (EDD) and Simplified Apparent Tardiness Cost (SATC) -- 5.1.2 Comparison of WSPT, ATC and EDD with FIFO in the Best Effort Model and in the DiffServ Model in Service Differentiation -- 5.2 Job Scheduling Methods for Service Stability -- 5.2.1 Weighted Shortest Processing Time - Adjusted (WSPT-A) and its Performance in Service Stability -- 5.2.2 Verified Spiral (VS) and Balanced Spiral (BS) Methods for a Single Service Resource and their Performance in Service -- 5.2.3 Dynamics Verified Spiral (DVS) and Dynamic Balanced Spiral (DBS) Methods for Parallel Identical Resources and their per -- 5.3 Summary -- References -- 6 Job Reservation and Service Protocols for End-To-End Delay Guarantee
6.1 Job Reservation and Service in InteServ and RSVP -- 6.2 Job Reservation and Service in I-RSVP -- 6.3 Job Reservation and Service in SI-RSVP -- 6.4 Service Performance of I-RSVP and SI-RSVP in Comparison with the Best Effort Model -- 6.4.1 The Simulation of a Small-Scale Computer Network with I-RSVP, SI-RSVP and the Best Effort Model -- 6.4.2 The Simulation of a Large-Scale Computer Network with I-RSVP, SI-RSVP and the Best Effort Model -- 6.4.3 Service Performance of I-RSVP, SI-RSVP and the Best Effort Model -- 6.5 Summary -- References -- Part III Mathematical/Statistical Features and Characteristics of Attack and Normal Use Data -- 7 Collection of Windows Performance Objects Data Under Attack and Normal Use Conditions -- 7.1 Windows Performance Objects Data -- 7.2 Description of Attacks and Normal Use Activities -- 7.2.1 Apache Resource DoS -- 7.2.2 ARP Poison -- 7.2.3 Distributed DoS -- 7.2.4 Fork Bomb -- 7.2.5 FTP Buffer Overflow -- 7.2.6 Hardware Keylogger -- 7.2.7 Remote Dictionary -- 7.2.8 Rootkit -- 7.2.9 Security Audit -- 7.2.10 Software Keylogger -- 7.2.11 Vulnerability Scan -- 7.2.12 Text Editing -- 7.2.13 Web Browsing -- 7.3 Computer Network Setup for Data Collection -- 7.4 Procedure of Data Collection -- 7.5 Summary -- References -- 8 Mean Shift Characteristics of Attack and Normal Use Data -- 8.1 The Mean Feature of Data and Two-Sample Test of Mean Difference -- 8.2 Data Pre-Processing -- 8.3 Discovering Mean Shift Data Characteristics for Attacks -- 8.4 Mean Shift Attack Characteristics -- 8.4.1 Examples of Mean Shift Attack Characteristics -- 8.4.2 Mean Shift Attack Characteristics by Attacks and Windows Performance Objects -- 8.4.3 Attack Groupings Based on the Same and Opposite Attack Characteristics -- 8.4.4 Unique Attack Characteristics -- 8.5 Summary -- References
9 Probability Distribution Change Characteristics of Attack and Normal Use Data -- 9.1 Observation of Data Patterns -- 9.2 Skewness and Mode Tests to Identify Five Types of Probability Distributions -- 9.3 Procedure for Discovering Probability Distribution Change Data Characteristics for Attacks -- 9.4 Distribution Change Attack Characteristics -- 9.4.1 Percentages of the Probability Distributions Under the Attack and Normal use Conditions -- 9.4.2 Examples of Distribution Change Attack Characteristics -- 9.4.3 Distribution Change Attack Characteristics by Attacks and Windows Performance Objects -- 9.4.4 Attack Groupings Based on the Same and Opposite Attack Characteristics -- 9.4.5 Unique Attack Characteristics -- 9.5 Summary -- References -- 10 Autocorrelation Change Characteristics of Attack and Normal Use Data -- 10.1 The Autocorrelation Feature of Data -- 10.2 Discovering the Autocorrelation Change Characteristics for Attacks -- 10.3 Autocorrelation Change Attack Characteristics -- 10.3.1 Percentages of Variables with Three Autocorrelation Levels Under the Attack and Normal Use Conditions -- 10.3.2 Examples of Autocorrelation Change Attack Characteristics -- 10.3.3 Autocorrelation Change Attack Characteristics by Attacks and Windows Performance Objects -- 10.3.4 Attack Groupings Based on the Same and Opposite Attack Characteristics -- 10.3.5 Unique Attack Characteristics -- 10.4 Summary -- References -- 11 Wavelet Change Characteristics of Attack and Normal Use Data -- 11.1 The Wavelet Feature of Data -- 11.2 Discovering the Wavelet Change Characteristics for Attacks -- 11.3 Wave Change Attack Characteristics -- 11.3.1 Examples of Wavelet Change Attack Characteristics -- 11.3.2 Wavelet Change Attack Characteristics by Attacks and Windows Performance Objects -- 11.3.3 Attack Groupings Based on the Same and Opposite Attack Characteristics
11.3.4 Unique Attack Characteristics -- 11.4 Summary -- References -- Part IV Cyber Attack Detection: Signature Recognition -- 12 Clustering and Classifying Attack and Normal Use Data -- 12.1 Clustering and Classification Algorithm - Supervised (CCAS) -- 12.2 Training and Testing Data -- 12.3 Application of CCAS to Cyber Attack Detection -- 12.4 Detection Performance of CCAS -- 12.5 Summary -- References -- 13 Learning and Recognizing Attack Signatures Using Artificial Neural Networks -- 13.1 The Structure and Back-Propagation Learning Algorithm of Feedforward ANNs -- 13.2 The ANN Application to Cyber Attack Detection -- 13.3 Summary -- References -- Part V Cyber Attack Detection: Anomaly Detection -- 14 Statistical Anomaly Detection with Univariate and Multivariate Data -- 14.1 EWMA Control Charts -- 14.2 Application of the EWMA Control Chart to Cyber Attack Detection -- 14.3 Chi-Square Distance Monitoring (CSDM) Method -- 14.4 Application of the CSDM Method to Cyber Attack Detection -- 14.5 Summary -- References -- 15 Stochastic Anomaly Detection Using the Markov Chain Model of Event Tansitions -- 15.1 The Markov Chain Model of Event Transitions for Cyber Attack Detection -- 15.2 Detection Performance of the Markov Chain Model-Based Anomaly Detection Technique and Performance Degradation with the i -- 15.3 Summary -- References -- Part VI Cyber Attack Detection: Attack Norm Separation -- 16 Mathematical and Statistical Models of Attack Data and Normal Use Data -- 16.1 The Training Data for Data Modeling -- 16.2 Statistical Data Models for the Mean Feature -- 16.3 Statistical Data Models for the Distribution Feature -- 16.4 Time-Series Based Statistical Data Models for the Autocorrelation Feature -- 16.5 The Wavelet-Based Mathematical Model for the Wavelet Feature -- 16.6 Summary -- References -- 17 Cuscore-Based Attack Norm Separation Models
17.1 The Cuscore
Professor Ye received her Ph.D. degree (1991) in Industrial Engineering from Purdue University, West Lafayette, Indiana, and holds MS (1988) and BS (1985) degrees in Computer Science. With her multi-disciplinary educational background, Dr. Ye has devoted her academic career to establishing the scientific and engineering foundation for assuring quality/reliability of information systems and industrial systems
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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Link Print version: Ye, Nong Secure Computer and Network Systems : Modeling, Analysis and Design New York : John Wiley & Sons, Incorporated,c2008 9780470023242
Subject Computer networks -- Security measures.;Computer networks -- Design and construction.;Computer security
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