FUNDED PROJECTS
Part of CISPA's research is covered by third-party funding. Our researchers recruit these funds in competitive procedures - alone or in cooperation with other applicants.
CySec4Psych
Cybersecurity 4 Psychology
Traditionally, cybersecurity has been viewed as a technical problem, for which software and hardware solutions were key. However, in recent years, the focus has moved from the technical to the human aspect of cyber security. People are more and more considered ‘the weakest link’, or light-heartedly referred to as PEBCAK (problem exists between chair and keyboard). With human error and cyber-attacks aimed at individuals rather than machines becoming every-day occurrences, there is a strong need to solve cybersecurity issues on this level. Coming from a programming background, computer scientists usually aim to solve these weaknesses in the architecture of software. However, a piece of software can ask for a strong password, but if the employee who needs to create the strong password, writes it down on a post-it that is left on his desk, the ‘improved’ software security is easily becoming obsolete. Instead of trying to solve human problems with technological solutions, or reinventing the wheel, a better solution is to look at existing scientific knowledge and work with experts on human behaviour. Knowledge in the field of psychology can create more effective awareness campaigns, improve compliance with security policies through tried and tested behavioural change interventions, and train people in detecting social cyber-attacks through the use of existing knowledge in the cognitive psychology domain. These collaborations lead to improved individual cybersecurity, safer organisations, and a better functioning (international) society. To achieve this, working with psychologists is key as they are trained to describe, understand and solve human behaviour issues. By bringing psychologists into the cybersecurity field, they can apply existing psychological theories and best practices to cybersecurity problems, as well as develop new psychological theories on the specifics of cyberattacks and cyber resilience.
Management
Founded
2020
Duration
01.09.2020-31.08.2023
Funding Code
ID: 2020-1-DE01-KA203-005726
IMPACT
ERC Synergy Grant imPACT – Privacy, Accountability, Compliance, and Trust in Tomorrow’s Internet
The Internet has evolved from a mere communication network used by tens of millions of people two decades ago, to a global multimedia platform for communication, social networking, entertainment, education, trade and political activism with more than two billion users. This transformation has brought tremendous benefits to society, but has also created entirely new threats to privacy, safety, law enforcement, freedom of information and freedom of speech. In today’s Internet, principals are amorphous, identities can be fluid, users participate and exchange information as peers, and data is processed on global third-party platforms. Existing models and techniques for security and privacy, which assume trusted infrastructure and well-defined policies, principals and roles, fail to fully address this challenge.
The imPACT project addresses the challenge of providing privacy, accountability, compliance and trust (PACT) in tomorrow’s Internet, using a cross-disciplinary and synergistic approach to understanding and mastering the different roles, interactions and relationships of users and their joint effect on the four PACT properties. The focus is on principles and methodologies that are relevant to the needs of individual Internet users, have a strong potential to lead to practical solutions and address the funda-mental long-term needs of the future Internet. We take on this challenge with a team of researchers from relevant subdisciplines within computer science, and with input from outside experts in law, so-cial sciences, economics and business. The team of PIs consists of international leaders in privacy and security, experimental distributed systems, formal methods, program analysis and verification, and database systems. By teaming up and committing ourselves to this joint research, we are in a unique position to meet the grand challenge of unifying the PACT properties and laying a new foundation for their holistic treatment.
Funding Body
Partner
Management
Duration
01.02.2015-31.01.2021
Funding Code
Grant agreement ID: 610150
Research Area
Funding Body
Partner
Incubator
CISPA founding incubator for start-ups in the IT security sector
CISPA's research topics contain an enormous potential for technology transfer into industrial application. CISPA has already been in active exchange with partners from industry and business for several years. Under the assumption that utilization in newly founded companies is the most direct form of knowledge and technology transfer, the expansion of the start-up incubator provides the opportunity to expand specialized structures for the explicit support of spin-offs.
The aim of the project is therefore to expand these initiatives conceptually and anchor them structurally in order to create a highly creative environment in the immediate vicinity of CISPA and in the vicinity of the Saarland Informatics Campus.
The BMBF's funding of measures primarily provides for the following areas: Raising awareness, project initiation, project funding, scaling and the overall management of the incubator.
Foundations of Perspicuous Software Systems – Enabling Comprehension in a Cyber-Physical World
DFG TRR248/1
The goal of this project is to develop programming abstractions for CPS applications, together with reasoning and verification techniques for correctness, synthesis techniques across continuous and discrete phenomena, and visualisation and immersive interaction tools for better programmer productivity. The basis for the project will be Language integrated transduction (LinT), a programming model for CPS. We shall develop an implementation and runtime system, and use it to develop sample CPS applications in home automation and cyber-physical production case studies. Our long-term vision is an integrated development environment for programming, verifying, and understanding high-performance and scalable applications involving perception of, planning for, and acting on the physical world, and to significantly raise the state of the art in development effort and quality of such systems. Abstractions and algorithmic analysis techniques developed in this project should form the core of a provably correct design methodology for complex CPS applications.
Funding Body
Management
- Bernd Finkbeiner
- Prof. Rupak Majumdar, Ph.D. (MPI für Softwaresysteme)
- Prof. Dr. Stefan Gumhold (Technische Universit t Dresden)
Duration
01.08.2020 bis 31.12.2022
Funding Code
TRR248
Funding Body
Knowledge-based Synthesis
GIF Research Grant Regular Program
We propose to bring together two historically disjoint lines of research: the epistemic analysis of distributed systems on the one hand, which aims at understanding the evolution of the knowledge of the components of a distributed system; and reactive synthesis, which aims at constructing such systems automatically from a formal specification given as a formula of a temporal logic.
Reactive synthesis has the potential to revolutionize the development of distributed systems. From a given logical specification, the synthesis algorithm automatically constructs an implementation that is correct-by-design. This allows the developer to focus on “what” the system should do instead of “how” it should be done. There has been a lot of success in the last years in synthesizing individual components of a distributed system. However, the complete synthesis of distributed protocols is, with currently available methods, far too expensive for practical applications.
Recent advances in the study of knowledge in distributed systems, such as the Knowledge of Preconditions principle, offer a path to significantly improve the situation. Our vision is a new class of synthesis algorithms that gainfully use this potential by constructing the distributed protocol in terms of the evolving knowledge of the components rather than the low-level evolution of the states.
We bring to the project complementing skills and expertise in the two respective fields. The proposed project will begin by carrying out a study on epistemic arguments for the correctness of existing distributed protocols. The goal is to develop a formalization of these arguments in the form of a diagrammatic proof that can be verified automatically. We will then develop systematic methods for the construction of such proofs, based on insights like the Knowledge of Preconditions principle. Finally, we will integrate our formalization of epistemic proofs into a synthesis algorithm that automatically constructs such a proof for a given specification, and then translates the proof into an actual implementation.
Funding Body
Management
Duration
01.04.2020-01.03.2023
Members
- Prof. Moses Yoram, Electrical Engineering Andrew and Erna Faculty of Electrical Engineering Technion, Israel
Funding Code
I-1513-407./2019
Research Area
Funding Body
OSARES
Output-Sensitive Algorithms for Reactive Synthesis
Reactive synthesis has the potential to revolutionize the development of distributed embedded systems. From a given logical specification, the synthesis algorithm automatically constructs an implementation that is correct-by-design. The vision is that a designer analyzes the design objectives with a synthesis tool, automatically identifies competing or contradictory requirements and obtains an error-free prototype implementation. Coding and testing, the most expensive stages of development, are eliminated from the development process. Recent case studies from robotic control and from hardware design, such as the automatic synthesis of the AMBA AHB bus controller, demonstrate that this vision is in principle feasible. So far, however, synthesis does not scale to large systems. Even if successful, it produces code that is much larger and much more complicated than the code produced by human programmers for the same specification. Our goal is to address both of these fundamental shortcomings at the same time. We will develop output-sensitive synthesis algorithms, i.e. algorithms that, in addition to optimal performance in the size of the specification, also perform optimally in the size and structural complexity of the implementation. Target applications for our algorithms come from both the classic areas of reactive synthesis, such as hardware circuits, and from new and much more challenging application areas such as the distributed control and coordination of autonomous vehicles and manufacturing robots, which are far beyond the reach of the currently available synthesis algorithms.
Funding Body
Funding Body
VoloSTreAM
Reliable runtime monitoring by means of traceable status analysis for highly critical eVOTL operation
The focus of the project is the development of a monitoring system for the highly critical VTOL operation. Advances in electromobility and automation technology enable the commercial use of highly automated aircraft with distributed electric propulsion systems.
Safety is an important success factor for such aircraft. To achieve this, the inherent complexity of the overall system must be identified in the form of precise requirements and consistently monitored during operation. In addition, development, operating and maintenance costs must be kept low in order to ensure economical operation of increasingly automated aircraft. The aim of the project is the automatic monitoring of parameters that are important for the safe commercial operation of an autonomous system. To increase the confidence in safety monitoring, the executable monitor is automatically generated from a formal specification of the desired behavior. The resulting transparency promises advantages for certification and economical operation. Analysis of the feedback for certification by secure, independent monitoring components is an essential topic.
The formal specification is separate from the control code and easier to understand, thus saving development and maintenance costs. Furthermore, conventional centralized monitoring procedures require the availability of all relevant data. In highly distributed avionics like that of the Volocopter it is necessary to execute the monitoring process at different system nodes, for which algorithms for monitoring have to be developed. In the project, the system monitoring approach is integrated on the basis of a formal specification for a Volocopter. This promises substantial improvements both in terms of security and from an economic point of view.
Pro-Gene-Gen
Protecting Genetic Data with Synthetic Cohorts from Deep Generative Models
Genetic data is highly privacy sensitive information and therefore is protected under stringent legal regulations, making them burdensome to share. However, leveraging genetic information bears great potential in diagnosis and treatment of diseases and is essential for personalized medicine to become a reality. While privacy preserving mechanisms have been introduced, they either pose significant overheads or fail to fully protect the privacy of sensitive patient data. This reduces the ability to share data with the research community which hinders scientific discovery as well as reproducibility of results. Hence, we propose a different approach using synthetic data sets that share the properties of patient data sets while respecting the privacy. We achieve this by leveraging the latest advances in generative modeling to synthesize virtual cohorts. Such synthetic data can be analyzed with established tool chains, repeated access does not affect the privacy budget and can even be shared openly with the research community. While generative modeling of high dimensional data like genetic data has been prohibitive, latest developments in deep generative models have shown a series of success stories on a wide range of domains. The project will provide tools for generative modeling of genetic data as well as insights into the long-term perspective of this technology to address open domain problems. The approaches will be validated against existing analysis that are not privacy preserving. We will closely collaborate with the scientific community and propose guidelines how to deploy and experiment with approaches that are practical in the overall process of scientific discovery. This unique project will be the first to allow the generation of synthetic high-dimensional genomic information to boost privacy compliant data sharing in the medical community.
Management
Duration
1.8.2020-31.07.2023
Members
Funding Code
ZT-1-PF-5-23
Website
TFDA
Trustworthy Federated Data Analytics
To solve future grand challenges, data, computational power and analytics expertise need to be brought together at unprecedented scale. The need for data has become even larger in the context of recent advances in machine learning. Therefore, data-centric digital systems commonly exhibit a strong tendency towards centralized structures. While data centralization can greatly facilitate analy-sis, it also comes with several intrinsic disadvantages and threats not only from a technical but more importantly also from a legal, political and ethical perspective. Rooting in sophisticated security or trust requirements, overcoming these issues is cumbersome and time consuming. As a consequence, many research projects are substantially hindered, fail or are simply not addressed. In this interdisci-plinary project we aim at facilitating the implementation of decentralized, cooperative data analytics architectures within and beyond Helmholtz by addressing the most relevant issues in such scenarios.
Trustworthy Federated Data Analytics (TFDA) will facilitate bringing the algorithms to the data in a trustworthy and regulatory compliant way instead of going a data-centric way. TFDA will address the technical, methodical and legal aspects when ensuring trustworthiness of analysis and transparency regarding the analysis in- and outputs without violating privacy constraints. To demonstrate applica-bility and to ensure the adaptability of the methodological concepts, we will validate our develop-ments in the use case “Federated radiation therapy study” (Health) before disseminating the results.
Management
Duration
01.12.2019–30.11.2022
Members
Funding Code
ZT-I-0014
Research Area
Website
KMU-Fuzz
Automatische Programmanalyse für Schwachstellenerkennung in Netzwerkapplikationen mittels Fuzzing
In KMU-Fuzz werden neue Konzepte erforscht und umgesetzt, um Fuzz-Testing - eine besonders vielversprechende Form von automatischem Softwaretesting - entscheidend zu verbessern. Dabei Iiegt der Fokus vor allem auf dem effizienten Testen von Netzwerkschnittstellen von Applikationen, da existierende Fuzzing-Tools in diesem Bereich momentan noch eine unzureichende Testabdeckung bieten. Durch die Erforschung von neuartigen Methoden, z.B. zustandsbasiertes Netzwerkfuzzing, Fuzz-Testingbasierend auf effizienten Checkpunktmechanismen und effizientes Protokollfuzzing, werden neuartigen Methoden entwickelt, umkomplexe Softwaresysteme automatisiert und effizient testen zu können. Der Fokus dieses Teilprojekts liegt auf der effizientenVerwendung von verschiedenen Methoden aus dem Bereich der Programmanalyse, um Fuzzing effizienter durchführen zu können.
TELLUS
Domänenübergreifende Föderierung und Vernetzung für kritische Anwendungen
Die Vision von GAIA-X ist die Schaffung einer sicheren, vernetzten, föderierten Dateninfrastruktur, um in Datenökosystemen Datensouveränität herzustellen. Das TELLUS-Vorhaben erweitert diese Dateninfrastruktur der verschiedenen Cloud-Ökosysteme um eine leistungsfähige Anbindung und Integration von heterogener Netzwerkinfrastruktur. Es existieren verschiedene Use Cases, die nicht nur hohe Anforderungen an Cloud-Dienste stellen, sondern insbesondere auch an Netzwerke in Bezug auf Latenz, Bandbreite, Sicherheit, Resilienz und Dynamik.
TELLUS entwickelt basierend auf solchen Use Cases ein Overlay über Kaskaden von Cloud-Anbietern, Vernetzungsdienstleistern und Cloud-Anwendern, um unter Berücksichtigung kritischer Anforderungen eine Ende-zu-Ende Vernetzung mit Garantien für Hybrid-Cloud-Szenarien zu ermöglichen. Dem GAIA-X Gedanken folgend werden durch Integration auf Basis von Standards/Schnittstellen und Systemen, Domänengrenzen überbrückt, Interoperabilität und Portabilität sichergestellt und somit dynamische Netzwerke mit variablen Bandbreiten, geringeren Latenzen, erhöhter Sicherheit und Kontrolle über den Datenfluss im Netzwerk geschaffen.
Im Rahmen dieses Teilprojekts untersucht CISPA vorrangig Sicherheits- und Compliance-Aspekte des Gesamtsystems. Dazu wird eine umfassende Risiko- und Bedrohungsanalyse durchgeführt und basierend auf den Ergebnissen werden entsprechende Schutzkonzepte entwickelt und umgesetzt. Darüber hinaus ist CISPA an einigen anderen Arbeitspaketen beteiligt und bringt dort vor allem ebenfalls Expertise im Bereiche Security mit ein.
AlmaCrypt
Algorithmic and Mathematical Cryptology
Cryptology is a foundation of information security in the digital world. Today's internet is protected by a form of cryptography based on complexity theoretic hardness assumptions. Ideally, they should be strong to ensure security and versatile to offer a wide range of functionalities and allow efficient implementations. However, these assumptions are largely untested and internet security could be built on sand. The main ambition of Almacrypt is to remedy this issue by challenging the assumptions through an advanced algorithmic analysis.
In particular, this proposal questions the two pillars of public-key encryption: factoring and discrete logarithms. Recently, the PI contributed to show that in some cases, the discrete logarithm problem is considerably weaker than previously assumed. A main objective is to ponder the security of other cases of the discrete logarithm problem, including elliptic curves, and of factoring. We will study the generalization of the recent techniques and search for new algorithmic options with comparable or better efficiency. We will also study hardness assumptions based on codes and subset-sum, two candidates for post-quantum cryptography. We will consider the applicability of recent algorithmic and mathematical techniques to the resolution of the corresponding putative hard problems, refine the analysis of the algorithms and design new algorithm tools. Cryptology is not limited to the above assumptions: other hard problems have been proposed to aim at post-quantum security and/or to offer extra functionalities. Should the security of these other assumptions become critical, they would be added to Almacrypt's scope. They could also serve to demonstrate other applications of our algorithmic progress. In addition to its scientific goal, Almacrypt also aims at seeding a strengthened research community dedicated to algorithmic and mathematical cryptology.
Management
Duration
01/01/2016-31/21/2021
Funding Code
ERC Advanced Grants 669891
Research Area
Systematicgraph
Systematic mapping of the complexity landscape of hard algorith-mic graph problems
The goal of the SYSTEMATICGRAPH project is to put the search for tractable algorithmic graph prob-lems into a systematic and methodological framework: instead of focusing on specific sporadic prob-lems, we intend to obtain a unified algorithmic understanding by mapping the entire complexity landscape of a particular problem domain. A dichotomy theorem is a complete classification result that characterizes the complexity of each member of a family of problems: it identifies all the cases that admit efficient algorithms and proves that all the other cases are computationally hard. The pro-ject will demonstrate that such a complete classification is feasible for a wide range of graph prob-lems coming from areas such as finding patterns, routing, and survivable network design, and novel algorithmic results and new levels of algorithmic understanding can be achieved even for classic and well-studied problems.
Management
Duration
01.07.2017-30.06.2022
Funding Code
Grant agreement ID: 725978
Research Area
Yuri
Semantic Models and Agents for Security Testing of Web Applications
Detecting vulnerabilities in web applications is a daunting problem that does not have a general solution yet. Existing ad-hoc solutions can only identify simple forms of vulnerabilities that are present on the web application surface. In this project, we propose Yuri, a goal-oriented security testing agent that can synthesize semantic models and program representations closer to the way humans perceive and understand the program behaviors. Yuri can use these models to drive the attack surface exploration and execute security testing tasks, greatly expanding modern web-based application software coverage.
Funding Body
Funding Body
Kamaeleon
Development of an inherently safe control unit
Kamaeleon deals with the adaptation of light electric vehicles through software, so that the vehicles are able to adapt automatically to different areas driven over or to means of transport. Different requirements of the currently still firmly prescribed admission requirements have to be fulfilled by adaptation. The safety is primarily ensured by the speed driven in relation to the respective traffic area, but also due to the proximity to other road users. However, the maximum speed and continuous output is also a criterion for the approval of vehicles for a specific place of use (e.g. pedelec sidewalk, e-bike street). Technically, the maximum speed that can be driven is regulated first and foremost. This will create a completely new class of vehicles that are not defined by fixed characteristics, such as performance, maximum speed, equipment, etc., but whose functions are controllable by software.
Funding Body
Partner
Funding Body
Partner
AMR-XAI
Crushing Antimicrobial Resistance using Explainable AI
Antimicrobial Resistance (AMR) is perhaps the most urgent threat to human health. Since their discovery over a century ago, antibiotics have greatly improved human life expectancy and quality: many diseases went from life-threatening to mild inconveniences. Miss- and over-usage of these drugs, however, has caused microbes to develop resistance to even the most advanced drugs; diseases once considered conquered are becoming devastating again. While individual resistance mutations are well-researched, knowing which new mutations can cause antimicrobial resistance is key to developing drugs that reliably sidestep microbial defenses. In this project we propose to gain this knowledge via explainable artificial intelligence, by developing and applying novel methods for discovering easily interpretable local patterns that are significant with regard to one or multiple classes of resistance. That is, we propose to learn a small set of easily interpretable models that together explain the resistance mechanisms in the data, using statistically robust methods for discovering significant subgroups, as well as information theoretic approaches to discovering succinct sets of noise-robust rules. Key to our success will be the tight integration of domain expertise into the development of the new algorithms, early evaluation on real-world data, and the potential available in the host institute to evaluate particularly promising results in the lab.
Fuzzing
Efficient Detection of Software Vulnerabilities using Fuzzing
Fuzzing – testing software through randomly generated inputs – is one of the premier methods to discover software vulnerabilities. Fuzzing is easy to deploy; once set up, it can be left running for days and weeks, continuously testing the system with one input after another. Fuzzing also has no false positives: any input that crashes the program triggers a real vulnerability that can be exploited by attackers, if only for a denial of service attack.
Fuzzing is slow, though. The overwhelming majority of randomly generated inputs is invalid and will thus be rejected by the program under test. This can still detect errors, notably in the routines for parsing and rejecting inputs. In order to reach deeper functionality after input processing, though, it is necessary to have inputs that are syntactically valid.
The traditional means to produce valid inputs is to formally specify the input language using formal languages such as regular expressions and grammars – well-established and well-understood formal-isms with a sound and detailed theoretical foundation and plenty of applications in practice. Specifying an input language, however, is a huge manual effort, ranging from days for simple data formats to months for complex input languages.
In the past years, the group of PI Zeller has developed a number of techniques that can automatically extract grammars from a given program and a set of sample inputs and shown how to construct extremely efficient fuzzers from these grammars. These techniques are so mature they are even avail-able as open source in a recently published textbook. Yet, the grammar learners still depend on a comprehensive set of samples that cover every feature of the input space.
Therefore the aim of the project is to create test generators that specifically target input processors – that is, lexers (tools that compose input characters into words) as well as parsers (tools that compose sequences of words into syntactical structures, such as sentences in natural language). His approach is to feed some trivial invalid input (say, the string “x”) into a program and then to dynamically track the comparisons this program undertakes before rejecting the input as invalid.
Management
Duration
01.06.2019 – 30.11.2021
Members
Research Area
EMPEROR
Learning Causes of Program Behavior
All program behavior is triggered by some program input. Which parts of the input do trigger program behaviors, and how? In the EMPEROR project, we aim to automatically produce explanations for program behaviors—notably program failures. To this end, we (1) use grammars that separate inputs into individual elements; (2) learn statistical relations between features of input elements and program behavior; and (3) use systematic tests to strengthen or refute inferred associations, including internal features of the execution. As a result, we obtain an approach that (1) automatically infers the (input) conditions under which a specific behavior occurs: “The program fails whenever the mail address contains a quote character”; (2) automatically (re)produces behaviors of interest via generated test inputs: “andr'e@foo.com”; and (3) refines and produces cause-effect relationships via generated test cases, involving execution features: “The input ''''''''@bar.com” causes a recursion depth of more than 128, leading to a crash”. EMPEROR is the successor to EMPRESS, in which we showed that statistical relations between input elements and program behavior exist, and how prototypical implementations would exploit them for testing and debugging. EMPEROR takes the approaches from EMPRESS and unifies and extends them in a single modular approach, going way beyond simple statistical relations. By learning and refining predictive and generative models, EMPEROR will be able to infer and refine relationships involving arbitrary input features and thus boost our understanding of how and why software behaves as it does.
Funding Body
Management
Duration
1.10.2021-30.09.2024
Members
- Prof. Dr. Lars Grunske, Humboldt-Universität zu Berlin
- Marius Smytzek
Funding Code
ZE 509/7-2
Research Area
