### 8.1.3 Post-quantum cryptography (see 3.1.3)

Building a universal (as opposed to special purpose) quantum computer is widely believed to become feasible in the next decades. Therefore, it is important to think now about quantum-resistant cryptography, as some information that is encrypted today may still be sensitive in, say, 50 years. Most asymmetric cryptography used today is based either on the hardness of factoring or computing discrete logarithms, these problems are both known to be efficiently solvable by a quantum computer. Hence, there is a need for alternatives: lattice-based, code-based, and multivariate-based primitives are the most prominent candidates. It is urgent to perform an in-depth security analysis of these new schemes.

### 8.1.4 Computing on encrypted data (see 3.2.2)

The need for computing on encrypted data has emerged, in particular, with the appearance of the cloud and outsourced computation. In cryptography, this problem can be solved using homomorphic or functional encryption. Gentry showed in 2009 in his breakthrough paper that it was indeed possible to construct a fully homomorphic encryption (FHE) scheme. However, this construction remained theoretical and was completely impractical due to its poor performance. Since then, significant progress has been made on FHE schemes, achieving approximately a still very low speed of 50 logical gates per second. Significant progress will have extremely useful applications for privacy preserving cloud computing, where any technical advance may quickly be exploited as an economical advantage.

### 8.1.5 End-to-end formally verified cryptographic protocols (see 3.3.4)

As the security of cryptographic protocols is extremely difficult to ensure (pencil and paper proofs regularly contain errors), the use of rigorous, formal methods appears increasingly as the only way to achieve the expected security level for this class of systems. Therefore, the area of computer-aided security proofs is an increasingly important topic and must include all aspects from the specification down to the implementation. Recent work, in particular around TLS 1.3, have shown that this is now achievable.

However, such proofs still require carefully crafted code and a very high level of expertise. Leveraging the proof techniques to make them applicable to more general code and usable by a wider audience is now the main challenge. Different protocols often ensure different security properties, but existing tools for verifying certain properties, such as anonymity, do not yet have the same maturity as tools for verifying authentication properties. Yet another challenge is to consider stronger adversary models, e.g., an adversary that may control part of the computer through malware.
### 8.1.6 Intrusion detection for encrypted networks (see 4.4.1)

Nowadays, intrusion detection is essentially realized at the network level. If, as expected in the near future, the traffic were more systematically encrypted, which would of course be a good practice for security and privacy, the analysis of the network packets would become *de facto* inoperative, apart from the header analysis. Therefore, it becomes important to study and design new mechanisms for monitoring information systems and producing alerts, at the application, middleware, operating system, and even firmware or hardware levels.

### 8.1.7 Understanding privacy and deriving practical tools (see 5.1.6)

Understanding privacy principles and regulations is the foundation of any activity in privacy. Although this is not a new domain (e.g., the “Loi Informatique et Libertés” was adopted in 1978), this area has recently experienced major evolutions with the new GDPR European regulation and at the same time new opportunities to collect personal data. As a consequence, understanding the concepts and the regulation is a first necessity. Being able to derive practical tools is another one: even though the GDPR promotes several concepts and goals, it provides little guidance about the effective implementation of these new regulatory provisions.

In particular the GDPR introduced the right to data portability whereby a user can retrieve their data in a human readable and machine portable format. This right opens new research areas around individualized management and control over one's personal data. The goal is to empower citizens to leverage their personal data for their own good, which calls for secure, extensible, and sovereign personal cloud platforms, three conflicting goals that open new research challenges (see e.g., §5.2.4).

### 8.1.8 Open data and anonymization (see 5.2.3)

Open data initiatives may sometimes mean releasing databases that contain sensitive, personal information. To ensure privacy of the individuals, data need to be anonymized. Robust anonymization, that effectively resists de-anonymization attacks, is an active and hot research topic. If differential privacy has become a key scientific tool to achieve provable anonymization guarantees, challenges remain on its application, for instance in order to improve the privacy/utility trade-off.

### 8.1.9 Towards a privacy preserving smart connected world (see 5.3.6)

Our connected world experiences an unprecedented growth in terms of personal data collection, with practices that are increasingly intrusive for the citizen's intimacy. Surfing the web, using smartphones and other smart devices, driving a connected—and soon to be autonomous— car are activities that generate personal data leaks. The lack of transparency (many services and devices behave as black boxes) and lack of user control (how to express consent or opposition when there is no information, nor user interface) are major issues.
Identification of such hidden behavior is hindered by the number and complexity of underlying technologies specific to each domain. For instance, identification of tracking practices in a web page requires advanced JavaScript execution analyzes, while monitoring of smartphone applications needs dedicated frameworks, and monitoring of certain wireless communication technologies remains mostly unsolved. The analysis of these data flows is required to assess potential privacy leaks, e.g., in a smart home.

Such challenging and diverse research activities are essential to bring transparency, highlight good and bad practices, and enable regulators to enforce data protection laws. As such, this research directly helps in the shaping of our future smart connected world.

### 8.1.10 Securing the Internet of Things (IoT) (see 6.2.1)

Security in IoT is a major challenge: attacks are still relatively easy (many devices have not been designed with security in mind), invasive (e.g., pervasive in our lives), and potentially with a major impact due to the multiplication factor made possible by the large number of devices available and to the direct implications some of them have in the physical world (e.g., connected cars). The research directions are manifold, with for instance the ability to securely update embedded devices' software, the design of lightweight cryptographic primitives adapted to limited resources, the analysis of the security of new low-power wide area wireless technologies, the detection and mitigation of intrusions or misbehaving devices, and the need for secure-by-design frameworks, protocols, and operating systems.

### 8.1.11 Secure Industrial Systems (see 6.2.2)

Industrial systems rely more and more on software mechanisms that can be attacked. Their security has thus become a major issue, especially since the consequences of an attack against such systems can be dramatic. Although traditional security approaches seem applicable to the case of industrial systems, their specificities require reviewing the traditional security mechanisms to adapt them to this new context. In particular, the communication protocols used in this context cannot be modified overnight. There must be a transition during which legacy communications should be embedded in secure protocols. In addition, real-time control of the system is usually required. Security must thus also be applicable in real time. Finally, it is often impossible to modify industrial devices. Therefore, preventive security mechanisms cannot be used. It is then mandatory to use reactive security and thus extremely important to study how effective attack detection mechanisms could be deployed in this context.
## 8.2 General recommendations

We give here a few general recommendations related to the interaction between researchers in cybersecurity and society, the organization of research in cybersecurity, and to conclude, the importance of cyber-resilience.

### 8.2.1 Society should profit more from academic scientific expertise

For most topics of cybersecurity, there are academics with very specific technical expertise who can provide useful scientific advice and who occasionally already do so. However, scientists are often underrepresented in national or industrial advisory committees, in comparison to industrial members. Moreover, some positions or decisions taken, at different levels, show a lack of scientific advice.

### 8.2.2 Transfer of expertise between cybersecurity and other domains

The need for cybersecurity expertise is striking in most of its application domains, e.g., industrial systems, medical systems, robotics, and, perhaps even more crucially, IoT (see §6 for the complete list). Unfortunately, cybersecurity is not yet sufficiently identified as a priority in these domains, and thus not taken into account in early design stage of applications, failing to uphold the security-by-design principle. Conversely, research in cybersecurity sometimes needs more expertise in formal methods or artificial intelligence for instance. A transfer of expertise between research teams in cybersecurity and these different domains is needed to turn the tide.

### 8.2.3 Promoting security also as an experimental science

System security and network security appear to suffer from a lack of prestige in the academic world, at least in France. This may be a cultural issue, partly due to the fact that research in these domains is more experimental and technological. Another concern is the lack of real-world datasets. For instance, intrusion detection and alert correlation suffer from a lack of real data on which it is possible to test and compare new mechanisms proposed by researchers. Helping researchers to access or generate such data appears crucial.

### 8.2.4 Education

Education is essential to security (see §1.3 and §2.3.3) and major dissemination efforts should be made for all audiences: teachers, faculty members, researchers, industrial actors, and specialists, to everyday citizens including young children. Concerning awareness, there is need for scientific mediation in cybersecurity, targeting the society in general, and with a special focus on scholars and teachers in primary and secondary schools. On the professional side, Ph.D training in cybersecurity areas is of course a natural education tool. But dedicated professional master degrees or final-year programs of engineering schools could already bring
to students a clear vision of the possible future of information security (beyond the
industrial state of the art). In this context, MOOCs could be an efficient dissemination
and education vector, as well as a major communication tool, as they potentially
reach several thousand users during each MOOC session and require an active
participation from users.

8.2.5 Cyber-resilience by design

Many recent reports on the analysis of cybersecurity threats claim that states
will have to face massive cyber attacks. Are we well-prepared to resist a cyber
tornado? The resilience of critical infrastructures and, in particular, of operators
of vital importance (OVI) is a real issue. A large part of this challenge is actually
organizational and engineering-related, rather than academic—and the ANSSI has
already spent many efforts increasing our resilience. Nevertheless, research can
help: the security-by-design principle should also apply to cyber-resilience. This
means that cyber-resilience should not be considered after the fact, but taken
into account from the initial design when constructing digital systems, networks,
and digital infrastructure.

Moreover, many subdomains of cybersecurity are also indirectly implied in
this challenge: reactive security or malware detection are of utmost importance;
preventive security to ensure state-of-the-art protection levels is also relevant;
formal methods including proofs of protocols, their implementations, as well as
critical parts of operating systems can have a significant long-term impact on
communication system resilience.

Final remarks

In recent years, a major national effort has been made to strengthen the
security of the state and vital operators' information systems. In particular, the
ANSSI (Agence Nationale pour la Sécurité des Systèmes d'Information) and the
DGA (Délégation Générale de l'Armement)—to give only two examples—have
recruited many top talents to adapt their human resources to identified needs.

Nevertheless, even if the awareness of the importance of cybersecurity
research and training has increased, no such recruitment effort to enroll more
faculty members and researchers has been made. In the area of cyberresilience,
human resources are essential: we therefore call for a continuation of the efforts
undertaken by some national institutions, and that these efforts be extended to
research institutions, universities, and schools.

Similar to the countries making a significant effort to improve their cyber-
security capabilities, France should maintain or even reinforce its academic forces
and all its innovative ecosystem in this field.
<figure><img src="image_34.png" /></figure>

# Appendix A

<figure><img src="image_35.png" /></figure>
# List of Teams

**LEGEND:** Teams whose name is in capital letters have their main activity in cybersecurity. Other teams whose name is just capitalized in straight font have some significant activity on cybersecurity, while teams whose name is capitalized and slanted have only a marginal contribution to cybersecurity. Teams websites and 2017 annual reports can be found at the URLs https://www.inria.fr/en/research/research-teams and https://raweb.inria.fr/rapportsactivite/RA2017/.

* **Almanach**, Automatic Language Modelling and ANALysis & Computational Humanities, 46

* **Antique**, Static Analysis by Abstract Interpretation, 132, 137

* **ARIC**, Arithmetic and Computing, 56, 63

* **Avalon**, Algorithms and Software Architectures for Distributed and HPC Platforms, 127

* **Aviz**, Analysis and Visualization, 132

* **Cairn**, Energy Efficient Computing Architectures with Embedded Reconfigurable Resources, 36, 137

* **CARAMBA**, Cryptology, arithmetic: algebraic methods for better algorithms, 56, 63, 70

* **CASCADE**, Construction and Analysis of Systems for Confidentiality and Authenticity of Data and Entities, 56, 63, 71, 127, 132

* **Cedar**, Rich Data Exploration at Cloud Scale, 46

* **CELTIQUE**, Software certification with semantic analysis, 83, 137

* **CIDRE**, Confidentiality, Integrity, Availability, and Repartition, 36, 42, 47, 83, 88, 89, 93, 103, 121, 127, 132, 141, 146, 157

* **Coast**, Web Scale Trustworthy Collaborative Service Systems, 132

* **Coati**, Combinatorics, Optimization and Algorithms for Telecommunications, 129

* **COMETE**, Concurrency, Mobility and Transactions, 83, 110, 121, 148

* **Ctrl-a**, Control for safe Autonomic computing systems, 94, 141

* **Dante**, Dynamic Networks: Temporal and Structural Capture Approach, 46

* **Datasphere**, Economie des données et des plateformes, 39

* **Delys**, Distributed Algorithms and Systems, 132

* **Diana**, Design, Implementation and Analysis of Networking Architectures, 110, 129

* **Diverse**, Diversity-centric Software Engineering, 47, 122

* **Fun**, self-organizing Future Ubiquitous Network, 83, 137

* **GRACE**, Geometry, arithmetic, algorithms, codes and encryption, 53, 56, 64, 132, 138

* **Graphik**, GRAPHS for Inferences and Knowledge representation, 46

* **Ilda**, Interacting with Large Data, 46

* **INDES**, Secure Diffuse Programming, 83, 103, 122, 138

* **Infine**, Information Networks, 138
↗ **Kairos**, Logical Time for Formal Embedded System Design, 138

↗ **Lacodam**, Large Scale Collaborative Data Mining, 89, 148

↗ **LFANT**, Lith and fast algorithmic number theory, 64

↗ **Linkmedia**, Creating and exploiting explicit links between multimedia fragments, 46, 78, 148

↗ **Magnet**, Machine Learning in Information Networks, 148

↗ **MARELLE**, Mathematical, Reasoning and Software, 64, 83

↗ **Multispeech**, Speech Modelling for Facilitating Oral-Based Communication, 78, 148

↗ **Myriads**, Design and Implementation of Autonomous Distributed Systems, 89, 127

↗ **Orpailleur**, Knowledge representation, reasoning, 149

↗ **Ouragan**, OUtils de Résolution Algébriques pour la Géométrie et ses ApplicatioNs, 53, 64

↗ **Pacap**, Pushing Architecture and Compilation for Application Performance, 36

↗ **PESTO**, Proof techniques for security protocols, 70, 71, 78, 83, 110, 122

↗ **PETRUS**, Personal and Trusted cloud, 83, 85, 103, 110, 122, 138, 143

↗ **POLSYS**, Polynomial Systems, 56, 64

↗ **PRIVATICS**, Privacy Models, Architectures and Tools for the Information Society, 46, 47, 103, 110, 122, 138, 143, 149

↗ **PROSECCO**, Programming securely with cryptography, 53, 71, 78, 84, 132, 138

↗ **RESIST**, Management of dynamic networks and services, 41, 83, 89, 94, 129, 132, 153

↗ **Rits**, Robotics and Intelligent Transportation Systems, 146

↗ **SECRET**, Security, Cryptology and Transmissions, 53, 56, 64

↗ **Sequel**, Sequential Learning, 149

↗ **Specfun**, Symbolic Special Functions: Fast and Certified, 132

↗ **Spirals**, Self-adaptation for distributed services and large software systems, 122

↗ **Stack**, Software Stack for Massively Geo-Distributed Infrastructures, 127

↗ **TAMIS**, Threat Analysis and Mitigation for Information Security, 36, 47

↗ **Toccata**, Certified Programs, Certified Tools, Certified Floating-Point Computations, 132

↗ **Tyrex**, Types and Reasoning for the Web, 143, 149

↗ **Valda**, Value from Data, 84, 110

↗ **Veridis**, Modeling and Verification of Distributed Algorithms and Systems, 129, 133

↗ **Wide**, the World Is Distributed Exploring the tension between scale and coordination, 110
## EDITORS

Steve Kremer - Ludovic Mé - Didier Rémy - Vincent Roca.

## LIST OF CONTRIBUTORS

Laurent Amsaleg - Nicolas Anciaux - Daniel Augot - Frédéric Besson - Nataliia Bielova - Luc Bouganim - Anne Canteaut - Claude Castelluccia - André Chailloux - Konstantinos Chatzikokolakis - Mathieu Cunche - Jérôme François - Teddy Furon - Georges Gonthier - Guillaume Gravier - Gilles Guette - Hélène Kirchner - Jean-Louis Lanet - Cédric Lauradoux - Arnaud Legout - Gérard Le Lann - Daniel Le Métayer - Gaëtan Leurent - Anthony Leverrier - Stéphane Mocanu - Christine Morin - Maria Naya Plasencia - Catuscia Palamidessi - David Pointcheval - Tamara Rezk - Michaël Rusinowitch - Kavé Salamatian - Guillaume Scerri - Nicolas Sendrier - Olivier Sentieys - Éric Totel - Valérie Viet Triem Tong.

## LIST OF PEOPLE WHO PROVIDED USEFUL COMMENTS

Gildas Avoine - Emmanuel Baccelli - Hugues Berry - Karthikeyan Bhargavan - Bruno Blanchet - Bertrand Braunschweig - Isabelle Chrisment - Hervé Debar - Claude Kirchner - Philippe Pucheral - Emmanuel Thomé - Frédéric Tronel - Damien Vergnaud - Emmanuel Vincent.
<figure><img src="image_36.png" /></figure>

Inria

Domaine de Voluceau, Rocquencourt BP 105

78153 Le Chesnay Cedex, France

Tel.: +33 (0)1 39 63 55 11

www.inria.fr