Homeland Security Technology Challenges
From Sensing and Encrypting to Mining and Modeling
1st Edition
Published on 1. January 2008
314 pages
978-1-59693-290-6 (ISBN)
Description
Written and edited by leading experts in the field, this timely resource presents a thorough overview of the technical facets of Homeland Security (HS). This practical book offers you expert guidance on sensors and the preprocessing of sensed data, the handling of sensed data with secure and safe procedures, and the design, modeling and simulation of complex HS systems. You learn how to store, encrypt and mine sensitive data. Further, the book shows how data is transmitted and received along wired or wireless networks, operating on electromagnetic channels. Critical topics include embedded wireless sensor networks, tapping the vehicle grid for homeland security, visual detection of humans, mining databases, single-database private information retrieval, the channel model for sensor networks in an urban environment, sensing through walls, and stopping cars and mobiles. This unique, cutting-edge volume includes over 100 illustrations and more than 460 references that support key discussions throughout the book.
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Giorgio Franceshetti | Marina Grossi
Homeland Security Technology Challenges: From Sensing and Encrypting to Mining and Modeling
Book
06/2008
Artech House Publishers
Unfortunately, price unknown
Available (delivery time upon request)
Content
- Homeland Security Technology ChallengesFrom Sensing and Encrypting to Mining and Modeling
- Contents
- Preface
- 1 The Homeland Security Scenario
- 1.1 The Scenario
- 1.2 Composing Expertise and Technologies
- 1.3 System of System General Architecture
- 1.4 The Applications
- 1.5 System of System Engineering
- References
- 2 Embedded Wireless Sensor Networks
- 2.1 Introduction
- 2.2 Sensor Network Design Drivers: Resource Constraints and Autonomy
- 2.3 Resource Constraints
- 2.3.1 In-Network Processing for Computing, Storage, and Communication Energy Trade-Offs
- 2.3.2 Time Is Energy: Sleep Optimization Via Time Uncertainty Management
- 2.4 Network Autonomy
- 2.4.1 Self-Configuration: Localizing Nodes in Space and Time
- 2.4.2 Energy Neutral Operation
- 2.5 Lessons Learned
- 2.5.1 Beyond Smart Dust to Heterogeneous Sensor Ecologies
- 2.5.2 Incorporating the Human Tier
- 2.5.3 To Sense or Not to Sense: Optimizing the Sampling Energy
- 2.5.4 Mobility as a Performance Amplifier
- 2.5.5 Monitoring the Monitors: Sensor Data Integrity
- 2.6 Future Directions: Participatory Sensing
- 2.7 Conclusions
- Acknowledgments
- References
- 3 Visual Detection and Classification of Humans, Their Pose, and Their Motion
- 3.1 Introduction
- 3.2 Detection of Humans
- 3.2.1 Faces
- 3.2.2 Full Body
- 3.3 Tracking Human Motion
- 3.3.1 Tracking as Transition Between Key Frames
- 3.3.2 Continuous Temporal Models
- 3.4 Classification and Recognition of Human Motion
- 3.4.1 Integral Representations
- 3.4.2 Global Measures of Similarity
- 3.4.3 Classification of Models
- 3.4.4 Gait Recognition
- 3.5 Outlook
- References
- 4 Cyber Security Basic Defenses and Attack Trends
- 4.1 Introduction
- 4.2 Basic Concepts
- 4.2.1 Common Security Goals
- 4.2.2 Threat Modeling
- 4.2.3 Security Analysis
- 4.3 Cryptography
- 4.3.1 Hash Functions
- 4.3.2 Secret-Key and Public-Key Cryptography
- 4.3.3 Confidentiality
- 4.3.4 Integrity
- 4.3.5 Authentication
- 4.4 Network Security
- 4.4.1 Firewalls, IDSs, and Honeypots
- 4.5 Software Security
- 4.5.1 Software Vulnerabilities
- 4.5.2 Malicious Software
- 4.5.3 Defenses
- 4.6 Cyber Attack Trends, Threats, and Homeland Security
- 4.6.1 Cyber Crime and Botnets
- 4.6.2 Widely Spread Malware
- 4.6.3 DDoS Attacks, Estonia, and Hacktivism
- 4.6.4 Cyber Espionage and the Athens Affair
- 4.6.5 Critical Infrastructure and Cyber Security
- 4.7 Conclusions
- Acknowledgments
- References
- 5 Mining Databases and Data Streams
- 5.1 Introduction and Historical Perspective
- 5.1.1 Examples of DM Methods and Applications
- 5.2 Data Mining Methods
- 5.2.1 Taxonomy of Data Mining Methods
- 5.2.2 Classification and Prediction
- 5.2.3 Association Rules
- 5.2.4 Clustering Methods
- 5.2.5 Other Mining Techniques
- 5.2.6 The KDD Process
- 5.3 Web Mining and New Applications
- 5.3.1 Web Mining
- 5.3.2 Security Applications
- 5.3.3 Privacy-Preserving Data Mining
- 5.4 Mining Data Streams
- 5.4.1 Intrusion Detection and Network Monitoring
- 5.4.2 Data Mining Systems
- 5.4.3 DSMSs and Online Mining
- 5.4.4 Stream Mill: An Inductive DSMS
- 5.4.5 Data Stream Mining Algorithms
- 5.5 Conclusions
- References
- 6 Private Information Retrieval: Single-Database Techniques and Applications
- 6.1 Introduction
- 6.2 Single-Database Private Information Retrieval
- 6.2.1 Amortizing Database Work in PIR
- 6.2.2 Connections: Single-Database PIR and OT
- 6.2.3 Connections: PIR and Collision-Resistant Hashing
- 6.2.4 Connections: PIR and Function-Hiding PKE
- 6.2.5 Connections: PIR and Complexity Theory
- 6.2.6 Public-Key Encryption That Supports PIR Read and Write
- 6.2.7 Organization of the Rest of the Chapter
- 6.3 Background and Preliminaries
- 6.3. Encryption Schemes
- 6.3.2 Private Information Retrieval
- 6.3.3 Balancing the Communication Between Sender and Receiver
- 6.4 Semantically Secure Homomorphic Encryption Schemes: Examples
- 6.4.1 Encryption Based on Quadratic Residues
- 6.4.2 The ElGamal Cryptosystem
- 6.4.3 The Paillier Cryptosystem
- 6.5 PIR Based on Group-Homomorphic Encryption
- 6.5.1 Basic Protocols from Homomorphic Encryption
- 6.5.2 Optimizing Via an Integer Map
- 6.5.3 Further Improvements: Length-Flexible Cryptosystems
- 6.6 Private Information Retrieval Based on the F-Hiding Assumption
- 6.6.1 Preliminaries
- 6.6.2 A Brief Description of the Protocol
- 6.6.3 Generalizations: Smooth Subgroups
- 6.7 Private Information Retrieval from Any Trapdoor Permutation
- 6.7.1 Preliminaries
- 6.7.2 Outline of the Protocol
- 6.7.3 Protocol Details
- 6.8 Conclusions
- References
- 7 Tapping Vehicle Sensors for Homeland Security
- 7.1 Introduction
- 7.2 State of the Art
- 7.2.1 VANETs
- 7.2.2 Opportunistic Sensor Networking
- 7.3 MobEyes Architecture
- 7.4 MobEyes Diffusion/Harvesting Processor
- 7.4.1 MDHP Protocol Design Principles
- 7.4.2 Summary Diffusion
- 7.4.3 Summary Harvesting
- 7.5 MobEyes Performance Evaluation
- 7.5.1 Simulation Setup
- 7.5.2 Stability Check
- 7.5.3 Tracking Application
- 7.5.4 Border Effects and Turnover
- 7.6 MobEyes Privacy and Security
- 7.7 Conclusions
- References
- 8 Modeling and Analysis of Wireless Networked Systems
- 8.1 Introduction
- 8.2 Wireless Networked Systems for Critical Infrastructure
- 8.2.1 Real-Time Measurement Systems
- 8.2.2 Real-Time Control Systems
- 8.2.3 Theory of Networked Control Systems
- 8.3 Wireless Network Metrics
- 8.3.1 Choice of Network Models
- 8.3.2 Modeling Unicast Path Diversity
- 8.3.3 Modeling Directed Staged Flooding
- 8.3.4 Using the Network Metrics
- 8.3.5 Limitations of the Models and Future Work
- 8.4 Conclusions
- Acknowledgments
- References
- Appendix 8A Proof Sketch for Theorems 8.1 and 8.3
- 9 Large Systems Modeling and Simulation
- 9.1 Introduction
- 9.2 The Challenge of Homeland Protection
- 9.3 Definitions and Background
- 9.4 The Role of Modeling and Simulation
- 9.5 Reductionist Approaches to the Modeling of Large Systems
- 9.5.1 Horizontal Decomposition
- 9.5.2 Vertical Decomposition
- 9.6 Simulation Architecture for Performance Evaluation
- 9.6.1 End-to-End Level
- 9.6.2 Medium Grain Accuracy Level
- 9.6.3 Fine Grain Accuracy Level
- 9.7 Study Cases
- 9.7.1 End-to-End Simulation of a Maritime Border Control System
- 9.7.2 Multisensor Fusion for Naval Threat
- 9.8 Conclusions
- Acknowledgments
- References
- About the Authors
- Index
