Bachelor thesis: Derivation of probabilities for bayesian networks (gn)
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Bachelor thesis: Derivation of probabilities for bayesian networks (gn)
Standort: Ottobrunn bei München
Beschreibung der Stelle: IABG Innovation Centre is a development incubator for its IABG's portfolio, which, among others, includes data analysis, automatisation, and optimisation in the mobility and security domain. Natural Language Processing (NLP) has become one of our key competences and we would like to expand on this with you by applying it to one of our main projects in the domain of cyber security.

As part of the ongoing IMMUNE research project, IABG develops a risk assessment tool for industrial networks. Sensor information is interpreted to assess the current state of the network in terms of an exploitation of system vulnerabilities to derive guidance for risk minimisation. Vulnerabilities hereby include software/hardware flaws or configuration errors, which an attacker can employ to gain access. Thus, an attacker may propagate through the system via lateral movement and eventually reach a targeted machine. The sensors mainly perceive information coming from network based intrusion detection systems such as traffic load, connection geometry, and network topology. This state information is used to infer countermeasures defined on the action space upon the software-defined network configurations and the virtualized network function deployments.
  • Develop an algorithm to collect all vulnerabilities within the external sources by which the IMMUNE industrial network may be affected.
  • Apply CVSS to the identified vulnerabilities and exposures.
  • Derive a sensitive mapping between the CVSS and a probability measure.
  • Derive a senseful correlation between the vulnerabilities and generalize.
  • Good programming skills (essential)
  • Experience with natural language processing (desirable)
  • Good communication skills in English
  • Ability to work both independently and collaboratively
Ziele: The goal of this Bachelor thesis is to implement an interface to the databases of (such as CVE) and an interface to the vulnerability scoring system (CVSS) in order to automatically compute the score of a given vulnerability. As these scores are being used as a measurement for the likeliness of the exploitability, a sensitive mapping to probability measures needs to be derived. The second aspect of the project is the correlation between the vulnerabilities and its probability measure.

The result will be implemented into our IMMUNE Risk Assessment tool, which will be used within our company for consulting purposes.

This thesis expands on the current IMMUNE Risk Assessment tool, which currently incorporates a semiautomatic solution to the problem described above.

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