Computational intelligence in information assurance and security

This volume provides the academic and industrial community with a medium for presenting original research and applications related to information assurance and security using computational intelligence techniques. It details current research on information assurance and security regarding both the theoretical and methodological aspects, as well as various applications in solving real world problems using computational intelligence.

E.C. Laskari and G.C. Meletiou and Y.C. Stamatiou and M.N. VrahatisShiguo LianNadia Nedjah and Luiza de Macedo MourellePaolo Mercorelli and Alexander FrickKi-Won YeomLuc Longpré and Vladik KreinovichChengfeng Wang and Yuan Luo and Marina L. Gavrilova and Jon RokneKostas Markantonakis and Keith Mayes and Michael Tunstall and Damien Sauveron Fred PiperSangkyun Kim

1 Cryptography and Cryptanalysis Through Computational Intelligence	p. 1
1.1 Introduction	p. 2
1.1.1 Block ciphers	p. 2
1.1.2 Public key cryptographic schemes	p. 5
1.1.3 Elliptic Curve based cryptosystems	p. 7
1.2 Computational Intelligence Background and Methods	p. 8
1.2.1 Evolutionary Computation	p. 8
1.2.2 Artificial Neural Networks	p. 13
1.2.3 Fuzzy systems	p. 15
1.3 Review of Cryptography and Cryptanalysis Through Computational Intelligence	p. 16
1.4 Applying Computational Intelligence in Cryptanalysis	p. 18
1.4.1 Cryptanalysis as Discrete Optimization Task	p. 18
1.4.2 Cryptanalysis of Feistel Ciphers through Evolutionary Computation Methods	p. 23
1.4.3 Utilizing Artificial Neural Networks to Address Cryptographic Problems	p. 31
1.4.4 Artificial Neural Networks Applied on Problems Related to Elliptic Curve Cryptography	p. 34
1.5 Ridge Polynomial Networks for Cryptography	p. 37
1.6 Summary	p. 42
References	p. 43
2 Multimedia Content Protection Based on Chaotic Neural Networks	p. 51
2.1 Introduction	p. 52
2.2 Chaotic neural networks' generation and properties	p. 54
2.2.1 Chaotic neural network's generation	p. 54
2.2.2 Chaotic neural network's properties suitable for data encryption	p. 55
2.3 Multimedia content encryption based on chaotic neural networks	p. 59
2.3.1 Introduction to multimedia content encryption	p. 59
2.3.2 The cipher based on chaotic neural network	p. 60
2.3.3 Selective video encryption based on Advanced Video Coding	p. 64
2.4 Multimedia content authentication based on chaotic neural networks	p. 66
2.4.1 Introduction to multimedia content authentication	p. 66
2.4.2 The hash function based on chaotic neural network	p. 67
2.4.3 The proposed image authentication scheme	p. 69
2.4.4 Performance analysis	p. 70
2.5 Future work and discussions	p. 73
2.6 Conclusions	p. 74
2.7 Acknowledgements	p. 75
References	p. 75
3 Evolutionary Regular Substitution Boxes	p. 79
3.1 Introduction	p. 79
3.2 Preliminaries for Substitution Boxes	p. 80
3.3 Nash Equilibrium-based Evolutionary Algorithms	p. 82
3.4 Evolving Resilient S-Boxes	p. 82
3.4.1 S-Box encoding and genetic operators	p. 83
3.4.2 S-Box evaluation	p. 84
3.5 Performance Results	p. 87
3.6 Conclusion	p. 88
References	p. 88
4 Industrial Applications Using Wavelet Packets for Gross Error Detection	p. 89
4.1 Introduction	p. 90
4.1.1 Modules	p. 91
4.1.2 Gross Error Types and Examples	p. 93
4.2 Problem Specification	p. 95
4.2.1 Mathematical Preliminary	p. 95
4.2.2 Noise Level Detection Problem (NLDP) and Algorithm (NLDA)	p. 97
4.2.3 Some Remarks Regarding Wavelet Based Algorithms	p. 97
4.3 Wavelet Based Noise Level Determination	p. 97
4.3.1 Background and State of the Art	p. 98
4.3.2 Noise Level Estimation: State of the Art	p. 99
4.3.3 The Proposed New Procedure for Peak-Noise Level Detection	p. 100
4.3.4 Validation of Peak Noise Level Estimation	p. 103
4.4 The Wavelet Algorithm for GEDR	p. 106
4.4.1 Validation and Simulations	p. 109
4.4.2 Outlier Detection Algorithm: MAD Algorithm	p. 110
4.5 Results	p. 111
4.5.1 Algorithm Parameterization	p. 114
4.6 Experimental Data Sources	p. 116
4.6.1 Dryer, Distillation and Mining Data with Outliers	p. 118
4.6.2 Artificially Contaminated Data and Off-line, On-line Mode	p. 122
4.7 Summary, Conclusions and Outlook	p. 125
References	p. 126
5 Immune-inspired Algorithm for Anomaly Detection	p. 129
5.1 Introduction	p. 129
5.2 Background	p. 131
5.2.1 The Danger Theory	p. 131
5.2.2 Dendritic Cells as Initiator of Primary Immune Response	p. 133
5.3 IDS based on Danger Theory and DCs Properties	p. 136
5.3.1 Properties of DCs for IDS	p. 136
5.3.2 Abstraction of Anomaly Detection Algorithm	p. 138
5.4 DCs based Implementation of Practical Applications	p. 141
5.4.1 A Detection of DoM Attack	p. 142
5.4.2 Experiments and Results	p. 145
5.4.3 A Detection of Port Scan Attack	p. 147
5.4.4 Experiments and Results	p. 149
5.5 Conclusion	p. 153
References	p. 153
6 How to Efficiently Process Uncertainty within a Cyberinfrastructure without Sacrificing Privacy and Confidentiality	p. 155
6.1 Cyberinfrastructure and Web Services	p. 155
6.1.1 Practical Problem	p. 155
6.1.2 Centralization of Computational Resources	p. 156
6.1.3 Cyberinfrastructure	p. 156
6.1.4 What Is Cyberinfrastructure: The Official NSF Definition	p. 157
6.1.5 Web Services: What They Do - A Brief Summary	p. 157
6.2 Processing Uncertainty Within a Cyberinfrastructure	p. 158
6.2.1 Formulation of the problem	p. 158
6.2.2 Description of uncertainty: general formulas	p. 160
6.2.3 Error Estimation for the Results of Data Processing	p. 162
6.2.4 How This Problem Is Solved Now	p. 162
6.3 Need for Privacy Makes the Problem More Complex	p. 162
6.4 Solution for Statistical Setting: Monte-Carlo Simulations	p. 164
6.5 Solution for Interval and Fuzzy Setting	p. 165
6.6 Summary	p. 169
References	p. 170
7 Fingerprint Recognition Using a Hierarchical Approach	p. 175
7.1 Introduction	p. 175
7.2 Coarse Fingerprint Matching	p. 179
7.2.1 Fingerprint Foreground Segmentation	p. 180
7.2.2 Singular Points Extraction	p. 181
7.2.3 Singular Points Matching	p. 185
7.3 Topology-based Fine Matching	p. 185
7.3.1 Delaunay Triangulation of Minutiae Set	p. 188
7.3.2 Modeling Fingerprint Deformation	p. 190
7.3.3 Maximum Bipartite Matching	p. 192
7.4 Experimental Results	p. 194
7.5 Conclusions	p. 197
References	p. 198
8 Smart Card Security	p. 201
8.1 Introduction	p. 201
8.2 Smart Card Specific Attacks	p. 203
8.2.1 Side Channel Attacks	p. 203
8.2.2 Fault Attacks	p. 209
8.3 Smart Card Platform Security	p. 214
8.3.1 The Evolution of Smart Card Platforms	p. 214
8.3.2 The Different Multi-application smart card Platforms	p. 215
8.3.3 Java Card	p. 217
8.3.4 Java Card Security	p. 219
8.4 GSM and 3G Security	p. 221
8.4.1 1G - TACS	p. 222
8.4.2 2G - GSM	p. 222
8.4.3 3G - UMTS	p. 226
8.5 Summary	p. 228
References	p. 229
9 Governance of Information Security: New Paradigm of Security Management	p. 235
9.1 Introduction	p. 236
9.2 Rise of the Governance	p. 237
9.2.1 Definitions of the Governance	p. 237
9.2.2 Implications of the Governance	p. 238
9.2.3 Success Factors of the Governance	p. 239
9.3 Why the Security Management Fails	p. 240
9.3.1 What the Security Management Can Do	p. 240
9.3.2 What the Security Management Cannot Do	p. 242
9.4 Governance of Corporate Security	p. 244
9.4.1 General Frameworks for the Governance	p. 244
9.4.2 Integrated Framework for the Governance of Corporate Security	p. 244
9.5 Summary	p. 251
References	p. 252
Author Index	p. 255

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