Foreword: Sharing Complexity: An Acclaim for Complex Thinking ix
Philippe Kourilsky
Preface xiii
Jean-Pierre Briffaut
Chapter 1 The Complexity of Cybersecurity 1
Thierry Berthier and Thomas Anglade
1.1 Formal approach to the complexity of cybersecurity 1
1.1.1 Cybersecurity and theoretical computing 1
1.1.2 Malware and computer virology 7
1.1.3 Cyber-risk 17
1.1.4 Cognitive attacks and immersive fictitious data architectures 23
1.2 Cybersecurity in real life: Advanced persistent threats, computer networks, defense teams and complex log data 27
1.2.1 What is an APT? 27
1.2.2 What is the network that companies need to protect? Who protects it? Why are “Situational Crime Prevention” (SCP) systems complex systems? 30
1.2.3 What kind of anomalies need to be raised in order to detect a multi-stage APT attack? 36
1.3 User and entity behavior analysis as a way of reducing complexity 39
1.3.1 Presentation of the method 39
1.3.2 Data used and details of the method 42
1.3.3 Visual results and interpretation 44
1.4 Conclusion and future work 47
1.5 References 48
Chapter 2 Complexity and Biology: When Historical Perspectives Intersect with Epistemological Analyses 51
Celine Cherici
2.1 Complexity throughout the history of thoughs on living 53
2.1.1 The roots of thinking on complexity 53
2.1.2 From machinules to cells: An ordered complexity? 55
2.1.3 The organism: An autonomous complexity 57
2.1.4 The emergence of complexity between comparative anatomy and embryology 59
2.2 The living: Between potentialities and actualizations 64
2.2.1 Teratology to better understand the links between actualization and potentiality in the living 64
2.2.2 Time, a key concept for understanding the interactions between the possible and the actualized 70
2.3 Reductionist biotechnologies? 73
2.3.1 From physics to biotechnology 74
2.3.2 When the living extend beyond the experimental framework 78
2.4 References 82
Chapter 3 Two Complexities: Information and Structure Content 87
Jean-Paul Delahaye
3.1 The simple, the random and the structured: A triangle of concepts key to a complete understanding 87
3.2 Calculation, the key to the solution 88
3.3 Thought experiment 89
3.4 Mathematical definition 90
3.5 Random complexity and structural complexity 91
3.6 Recent progress 92
3.7 Less undecidability 93
3.8 Experimentation 94
3.9 Appendices 95
3.9.1 Complexification 95
3.9.2 Random and structural complexity 96
3.9.3 Incalculable but approximate 97
3.9.4 The law of slow growth 98
3.9.5 Experimental evaluation of K(s) and P(s) 98
3.10 References 99
Chapter 4 Leveraging Complexity in Oncology – A Data Narrative 101
Xose M. Fernandez
4.1 Large collaborative research initiatives – the Human Genome Project 104
4.2 Human cell atlas – unraveling complexity 105
4.3 From bench to bedside 107
4.4 The battle with cancer 109
4.5 Health economics – cost is another matter 112
4.6 From molecules to medicine 115
4.7 Artificial intelligence 117
4.8 The fourth paradigm 120
4.9 Modeling the complexity of cancer 121
4.10 References 124
Chapter 5 Complexity or Complexities of Information: The Dimensions of Complexity 129
Jacques Printz
5.1 Introduction 129
5.2 A brief historical overview 130
5.3 The phenomenology of complexity in systems engineering 131
5.3.1 Measuring the complexity of an assembly through the integration process and tests 134
5.4 The four dimensions of complexity 138
5.5 The term “simplexity”: A remark on Richard Feynman’s Nobel lecture 142
5.6 Computational volume: Remarks on the first quantification of complexity 144
5.6.1 Quantifying interactions and functional dependencies 145
5.7 References 151
List of Authors 153
Index 155
