Paper Presentation at the EDCC 2021

Carsten Thomas from the University of Applied Sciences Berlin (HTW) presented a paper at 17th European Dependable Computing Conference on 13-16 September 2021 in Munich, Germany. The paper titled “Service-Oriented Reconfiguration in Systems of Systems Assured by Dynamic Modular Safety Cases” was presented during the Workshop on Dynamic Risk managEment for AutonoMous Systems (DREAMS).

Access to the full text via the conference proceedings.

Authors: Carsten Thomas, Elham Mirzaei, Björn Wudka, Lennart Siefke, Volker Sommer


The drive for automation in industry and transport results in an increasing demand for cooperative systems that form cyber-physical systems of systems. One of the characteristic features of such systems is dynamic reconfiguration, which facilitates emergent behavior to respond to internal variations as well as to environmental changes. By means of cooperation, systems of systems can achieve greater efficiency regarding fulfillment of their goals. These goals are not limited to performance, but must also include safety aspects to assure a system of systems to operate safely in various configurations. In this paper, we present a reconfiguration approach which includes consideration of dynamic modular safety cases. During operation, configuration of system of systems will adapt to changes, selecting the most appropriate service composition from the set of possible compositions derived from blueprints. Variations of service compositions lead to changes in the associated safety cases, which are evaluated at run-time and taken into account during configuration selection. With this approach, safe operation of cyber-physical systems of systems with run-time reconfiguration can be guaranteed.


CyberFactory#1 at the ESM 2021: Invited Talks

The CyberFactory#1 consortium organised a second workshop at the 35th European Simulation and Modelling Conference, which took place on October 27th-29th. Similar to last year’s CyberFactory#1 workshop it consisted of invited talks and a paper session. This year, four speakers from our partners gave the keynotes at the conference, presenting one of our use-cases and covering different aspects of the factory of the future that increase the security and optimization of production. Check out the presentations below.


Invited Talks:


1. CyberFactory#1 – Protecting the Factory of the Future with CyberRanges and Digital Twins: the Roboshave Use-Case

Speaker: Adrien Bécue (Head of Innovation Airbus Cybersecurity, Elancourt, France)

2. Holistic Correlation of Events from increased Security and Safety of Factories of the Future

Speaker: Isabel Praça (Professor at ISEP and Researcher at GECAD, Porto, Portugal)

3. Realistic Simulation-based Fleet of cobots for FoF Optimization in Complex Scenarios

Speaker: Sergi Garcia (PAL Robotics, Barcelona, Spain)

4. CyberFactory#1 – Increasing the FoF Resilience with Modelling and Simulation Tools

Speaker: Jarno Salonen (Industrial Cybersecurity, VTT Technical Research Centre of Finland, Tampere, Finland)

Further information on the conference, the speakers and their topics can also be found here.



New CyberFactory#1 Showcase Video!

Watch this new showcase video to learn more about the work we do in our project and how our Portuguese partners SISTRADE, ISEP and IDEPA work together to create an efficient and secure Factory of the Future!


Join Us For Our Integration Workshop!

This workshop will provide insights into the CyberFactory#1 Use-cases. CyberFactory#1 aims at designing, developing, integrating and demonstrating a set of key enabling capabilities to foster virtualization, optimization and resilience of the Factories of the Future (FoF). It addresses the needs of 10 pilot users from Transportation, Textile, Electronics and Machine manufacturing industries around use cases such as AI-based process monitoring/optimization, continuous quality control, collaborative robotics, robot fleet optimizations or distributed manufacturing. It will also propose preventive and reactive capabilities to address cyber and physical threats and safety concerns in Factories of the Future.

The goal of this workshop is to assess the project demonstrators against user requirements including:
•           Demonstration objectives
•           Capabilities involved
•           Proposed architecture
•           Demonstration platform
•           Demonstration scenario

Date: 03. May 2021

Time: 14.00-17.15 CEST


14.00:             Use Cases 1-5

      • Airbus Defence and Space “Roboshave” – Real time rivet shaving control for aircraft manufacturing
      • Airbus Defence and Space “Autoclave” – Data-driven process optimization for aircraft parts forming
      • Airbus Defence and Space “Gap Gun” – Real time gaps and steps measurement data collection and analysis
      • S21Sec – Secure Manufacturing CPS monitoring on auxiliary automotive industry
      • Bittium – Cyber secure networked supply chain and information architecture

15.30:            Coffee Break

15.45:            Use- Cases 5-10

      • High Metal – Cheese making, IoT process lines and machinery
      • IDEPA – Digitized Textile production with cognitive ERP
      • Vestel – Optimizing Material Handling in PCB assembly lines
      • Alstom – Enabling robotics involvement in large system integration
      • ASTI Mobile Robotics GmbH – Simulation-based Robot fleet task allocation and optimization

17.15:            End of Workshop



Call for papers for our second CyberFactory#1 Workshop at the ESM2021!

Call for Papers to be presented at the 35th European Simulation and Modelling Conference

October 27 – October 29, 2021, Rome, Italy

2. Workshop: CyberFactory – Optimization & Resilience of Factories of the Future

This workshop focuses on the development and application of methods for modeling and simulation of CPS for the factory of the future (FoF). With the advent of Industry 4.0, digitalization and automation processes have moved into the focus of industry. The primary goal is not the optimization of a single production plant, but of the factory as a whole by the marriage of physical assets and advanced digital technologies, such as the internet of things (IoT), artificial intelligence (AI) and robots. From a modeling perspective, the individual components of the factory thus become cyber-physical systems (CPS) that communicate, analyze, and act upon information, enabling more flexible and responsive production. This track focuses on the development and application of methods for modeling and simulation of CPS for the factory of the future (FoF).

The organizers invite contributions with a focus towards CPS in the FoF that describe problem statements, trends, and emerging ideas in the engineering and application of CPS in industrial production.

Topics include, but are not limited to:

  • Requirements on CPS modeling for optimization and resilience of the FoF
  • Architectures for the FoF
  • Application of existing CPS models to industry: benefits and gaps
  • Usage of digital twins for optimization and resilience in the FoF
  • Data lake exploitation for the FoF
  • Models & Simulations for the identification of threats on safety and security in the FoF
  • Tool support for modeling & simulation of the FoF
  • Uncertainties and predictions in the FoF models
  • Modeling of human-machine-interaction in the FoF
  • Distributed manufacturing
  • Cyber resilience modeling for the FoF

Paper format:

Participants may submit a 5 page full paper or an 8 page extended paper (single spaced, double column) in PDF format. Paper formatting guidelines and templates can be found at All accepted papers will be published in the ESM’2020 Conference Proceedings.

Workshop format:

The workshop will be held as part of the European Simulation and Modeling Conference (ESM) 2021 to take place in Rome, Italy on October 27-29, 2021. It will feature peer-reviewed paper presentations organized according to the topics defined above. Papers not exceeding 8 pages must be submitted electronically via email in PDF format and must be conform to the submission guidelines.

Each submission will be reviewed by at least three members of the Program Committee and will be evaluated on the basis of originality, importance of contribution, soundness, evaluation, quality of presentation and appropriate comparison to related work. The program committee as a whole will make final decisions about which submissions to accept for presentation at the conference.

Important Dates:

Paper Submission deadline:                           Jun 25th, 2021
Notification of acceptance/rejection:      Aug 21th, 2021
Camera ready paper:                                          Sep 27th, 2021
Workshop:                                                                Oct 27th-29th, 2021


Adrien Bécue (Airbus Cybersecurity)
Frank Oppenheimer (OFFIS e.V.)
Ilhan Kaya (Vestel)
Ingo Stierand (OFFIS e.V.)
Isabel Praça (Instituto Superior de Engenharia do Porto)
Jarno Salonen (VTT Technical Research Centre of Finland Ltd)
Linda Feeken (OFFIS e.V.)

Linda Feeken,

Call for Papers: Symposium on Security and Privacy in Speech Communication

Call for papers to be presented at the

1st Symposium on Security and Privacy in Speech Communication

Virtual, November 10-12, 2021


The first edition of the SPSC Symposium aims at laying the first building blocks required to address the question how researchers and practitioners might bridge the gap between social perceptions and their technical counterparts with respect to what it means for our voices and speech to be secure and private.

The symposium brings together researchers and practitioners across multiple disciplines – more specifically: signal processing, cryptography, security, human-computer interaction, law, and anthropology. By integrating different disciplinary perspectives on speech-enabled technology and applications, the SPSC Symposium opens opportunities to collect and merge input regarding technical and social practices, as well as a deeper understanding of the situated ethics at play.The SPSC Symposium addresses interdisciplinary topics.

For more details, see CFP.

Topics of Interest:
Topics regarding the technical perspective include but are not limited to:
  • Speech Communication
  • Cyber security
  • Machine Learning
  • Natural Language Processing
Topics regarding the societal view include but are not limited to:
  • Human-Computer Interfaces (Speech as Medium)
  • Ethics & Law
  • Digital Humanities
We welcome contributions on related topics, as well as progress reports, project disseminations, or theoretical discussions and “work in progress”.  There also is a dedicated PhD track. In addition, guests from academia, industry and public institutions as well as interested students are welcome to attend the conference without having to make their own contribution. All accepted submissions will appear in the conference proceedings published in ISCA Archive.

Papers intended for the SPSC Symposium should be up to four pages of text. An optional fifth page can be used for references only. Paper submissions must conform to the format defined in the paper preparation guidelines and as detailed in the author’s kit. Papers must be submitted via the online paper submission system. The working language of the conference is English, and papers must be written in English.

All submissions share the same registration deadline (with one week of submission updates afterwards). At least three single-blind reviews are provided, we aim to get feedback from interdisciplinary experts for each submission.

Important dates:
Paper submission opens:           April 10, 2021
Paper submission deadline:     June 30, 2021
Author notification:                      September 5, 2021
Final paper submission:              October 5, 2021
SPSC Symposium:                          November 10-12, 2021

For further details contact!

Webinar: Resilience Capabilities for the Factory of the Future


The webinar will provide insights to one of the key capabilities of CyberFactory#1: Resilience. The keynote speech is given by Sauli Eloranta, Professor of Practice at VTT, on “Industry challenge to resilience in the factory of the future”. Afterwards, experts from a number of project partners will discuss the different aspects that need to be considered for a resilient Factory of the Future. The first half focuses on access management approaches and protection of AIs. After a short Q&A, presentations are given on monitoring of the FoF and dealing with cyberattacks, followed by another Q&A.




14.00:             Welcome

Jarno Salonen, VTT

Keynote: Industry challenge to resilience in the factory of the future

Sauli Eloranta, VTT

14.20:             How to create trust with comprehensive identity and access management

Markku Korkiakoski, Netox

Don’t make me think: an intuitive access management approach

Diogo Santos, Sistrade

14.40:             How to protect AI from manipulation attempts

Ching-Yu Kao, Fraunhofer AISEC

Aspects of preventing AI manipulation

Seppo Heikura, Houston Analytics

15.00:              Q&A

15.10:             How to enhance resilience by monitoring the FoF

Mario Brauer, Airbus CyberSecurity Germany

Monitoring different aspects of human behaviour on the shop-floor

Jorge Oliveira, ISEP

15.30:             Architectural approach to effectively detect cyberattacks

Murat Lostar, Lostar

How to remediate and recover from a cyberattack

Jari Partanen, Bittium

15.50:              Q&A

16.00              Wrap Up

Jarno Salonen, VTT


Keynote Speaker:

Sauli Eloranta (Professor of Practice at VTT Technical Research Centre of Finland)

Sauli Eloranta, M. Sc. (Tech.), began working as Professor of Practice at VTT on 1 January 2020. Eloranta, elected the CTO of the Year in Finland in 2019, came to VTT with a long experience of promoting technology and digitisation in industry and maritime transport.

Before VTT, Eloranta acted as Head of Innovation and Technology at Rolls-Royce Marine, later Kongsberg Maritime. Eloranta earned the CTO of the Year title granted by the Federation of Finnish Technology Industries for his merits as an active influencer in the Finnish innovation scene and promotor of autonomous marine traffic. He chaired the One Sea Autonomous Maritime Ecosystem in 2016-2019. Sauli has chaired the Business Finland digital advisory board and is a member of the transport sector growth programme. In addition, he has been involved in supporting the collaboration of the private sector and societal actors.

In his role as Professor of Practice, Eloranta focuses on the overall resilience of the Finnish society. His area also covers cyber security, autonomous systems and smart transport & mobility. Recently, Sauli has given program management support to Finland´s Ministry of Economics & Employment (TEM) in establishing domestic production of face masks for public health care.

CyberFactory#1 Welcomes LISA to the Team


We are proud to announce that the CyberFactory#1 Consortium was joined by LISA Deutschland GmbH in February 2021. LISA Group is an internationally known company for Intelligent Systems and learning algorithms, and has extensive experinece in developing Systems for Aircraft and Space Operations.

Within the project LISA will provide an autonomous anomaly bot aimed at detecting cybersecurity anomalies to enhance production and manufacturing in the factory of the future. The bot will be used within the use cases of Airbus Defense and Space (Spain) but it can be applied to detect cybersecurity anomalies in any environment. You can read more about their addition to the project here.



Poster Presentation at Machine Learning in Certified Systems Workshop

Members of the CyberFactory#1 project consortium participated in the Machine Learning in Certified Systems Workshop organised by the DEEL project. Ana Pereira from the University of Applied Sciences Berlin (HTW) presented a poster on “Safety Hazards Analysis and Mitigation Strategies for Machine Learning-Based Safety-Critical Systems”.


Machine Learning (ML) is increasingly applied for the control of safety-critical Cyber-Physical Systems (CPS). As a consequence, the safety of machine learning became a focus area for research in recent years. Applying a classic technique of safety engineering, our work provides a methodological analysis of the safety hazards that could be introduced along the ML lifecycle, and that could compromise the safe operation of ML-based CPS. The comprehensive analysis presented here intends to be used as a basis for holistic approaches for safety engineering of ML-based CPS in safety-critical applications, and aims to support the use of ML-based control systems in highly safety-critical applications and their certification.

The poster was created by Ana Pereira and Carsten Thomas from the University of Applied Sciences Berlin (HTW).

You can download the poster here.

Paper presentations at four conferences

We congratulate our colleagues from Fraunhofer AISEC for four paper presentations at academic conferences within the past months! Click on the titles below for more information on each paper.

This paper was presented at the DYNAMICS workshop on the 7th of December 2020 at the Annual Computer Security Applications Conference (ACSAC). The paper proposes a novel method to make deep learning models robust, which can be applied on different data sets, such as images, audios, languages. The results show this method is comparable to adversarial training method.

The paper is available to download here.

Authors: Philip Sperl and Konstantin Böttinger

Abstract: Neural Networks (NNs) are vulnerable to adversarial examples. Such inputs differ only slightly from their benign counterparts yet provoke misclassifications of the attacked NNs. The required perturbations to craft the examples are often negligible and even human imperceptible. To protect deep learning-based systems from such attacks, several countermeasures have been proposed with adversarial training still being considered the most effective. Here, NNs are iteratively retrained using adversarial examples forming a computational expensive and time consuming process often leading to a performance decrease. To overcome the downsides of adversarial training while still providing a high level of security, we present a new training approach we call \textit{entropic retraining}. Based on an information-theoretic-inspired analysis, entropic retraining mimics the effects of adversarial training without the need of the laborious generation of adversarial examples. We empirically show that entropic retraining leads to a significant increase in NNs’ security and robustness while only relying on the given original data. With our prototype implementation we validate and show the effectiveness of our approach for various NN architectures and data sets.

The second paper was also presented at the Annual Computer Security Applications Conference (ACSAC) 2020. The authors apply two visualization techniques to the ASR system Deepspeech and show significant visual differences between benign data and adversarial examples.

Authors: Karla Markert, Romain Parracone, Philip Sperl and Konstantin Böttinger.

Abstract: Security of automatic speech recognition (ASR) is becoming ever more important as such systems increasingly influence our daily life, notably through virtual assistants. Most of today’s ASR systems are based on neural networks and their vulnerability to adversarial examples has become a great matter of research interest. In parallel, the research for neural networks in the image domain has progressed, including methods for explaining their predictions. New concepts, referred to as attribution methods, have been developed to visualize regions in the input domain that strongly influence the image’s classification.  In this paper, we apply two visualization techniques to the ASR system Deepspeech and show significant visual differences between benign data and adversarial examples. With our approach we make first steps towards explaining ASR systems, enabling the understanding of their decision process.

The third paper was presented at the 4th ACM Computer Science in Cars Symposium (ACM CSCS 2020). This paper provides a short overview on recent literature to discuss the language bias towards English in current research. The preliminary findings underline that there are differences in the vulnerability of a German and an English ASR system.

Authors: Karla Markert, Donika Mirdita and Konstantin Böttinger

Abstract: Voice control systems in vehicles offer great advantages for drivers, in particular more comfort and increased safety while driving.  Being continuously enhanced, they are planned to comfortably allow access to the networked home via external interfaces. At the same time, this far-reaching control enables new attack vectors and opens doors for cyber criminals. Any attacks on the voice control systems concern the safety of the car as well as the confidentiality and integrity of the user’s private data. For this reason, the analysis of targeted attacks on automatic speech recognition (ASR) systems, which extract the information necessary for voice control systems, is of great interest. The literature so far has only dealt with attacks on English ASR systems. Since most drivers interact with the voice control system in their mother tongue, it is important to study language-specific characteristics in the generation of so-called adversarial examples: manipulated audio data that trick ASR systems. In this paper, we provide a short overview on recent literature to discuss the language bias towards English in current research. Our preliminary findings underline that there are differences in the vulnerability of a German and an English ASR system.

This paper was already presented at the IEEE European Symposium on Security and Privacy 2020 in September. It proposes an adversarial example detector by analysing dense layer activations of deep learning models.

The paper is available to download here.

Authors: Philip Sperl, Ching-Yu Kao, Peng Chen, Xiao Lei, and Konstantin Boettinger

Abstract: In this paper, we present a novel end-to-end framework to detect such attacks during classification without influencing the target model’s performance. Inspired by recent research in neuron-coverage guided testing we show that dense layers of DNNs carry security-sensitive information. With a secondary DNN we analyze the activation patterns of the dense layers during classification runtime, which enables effective and real-time detection of adversarial examples. This approach has the advantage of leaving the already trained target model and its classification accuracy unchanged. Protecting vulnerable DNNs with such detection capabilities significantly improves robustness against state-of-the-art attacks.Our prototype implementation successfully detects adversarial examples in image, natural language, and audio processing. Thereby, we cover a variety of target DNNs, including Long Short Term Memory (LSTM) architectures. In addition to effectively defend against state-of-the-art attacks, our approach generalizes between different sets of adversarial examples. Thus, our method most likely enables us to detect even future, yet unknown attacks.