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PhD Defence: A Software Vulnerabilities Odysseus: Analysis, Detection, and Mitigation

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Speaker: Timothée Riom (SerVal group)
Event date: Wednesday, 28 September 2022 10:00 am - 01:00 pm

You are all cordially invited to attend the PhD Defence of Timothée Riom on Wednesday, 28 September 2022 at 10:00.

The defence will take place physically in room D17 (Main building of the Campus Kirchberg).

Members of the defense committee:

  • Dr. Jacques KLEIN, Dissertation Supervisor, Associate Professor, University of Luxembourg, Luxembourg
  • Dr. Tegawendé Francois d’Assise BISSYANDE, Chairman, Associate Professor, University of Luxembourg, Luxembourg
  • Dr. Yves LE TRAON, Vice-Chairman, Full Professor, University of Luxembourg, Luxembourg
  • Dr. Olivier BARAIS, Full Professor, Université de Rennes 1, France
  • Dr. Pierre GRAUX, Associate Professor, University of Lille, France

 

Abstract:

Programming has become central in the development of human activities while not being immune to defaults, or bugs. Developers have developed specific methods and sequences of tests that they implement to prevent these bugs from being deployed in releases. Nonetheless, not all cases can be thought through beforehand, and automation presents limits the community attempts to overcome. As a consequence, not all bugs can be caught.

These defaults are causing particular concerns in case bugs can be exploited to breach the program’s security policy. They are then called vulnerabilities and provide specific actors with undesired access to the resources a program manages. It damages the trust in the program and in its developers, and may eventually impact the adoption of the program. Hence, to attribute a specific attention to vulnerabilities appears as a natural outcome. In this regard, this PhD work targets the following three challenges:

(1) The research community references those vulnerabilities, categorises them, reports and ranks their impact. As a result, analysts can learn from past vulnerabilities in specific programs and figure out new ideas to counter them. Nonetheless, the resulting quality of the lessons and the usefulness of ensuing solutions depend on the quality and the consistency of the information provided in the reports.

(2) New methods to detect vulnerabilities can emerge among the teachings this monitoring provides. With responsible reporting, these detection methods can provide hardening of the programs we rely on. Additionally, in a context of computer performance gain, machine learning algorithms are increasingly adopted, providing engaging promises.

(3) If some of these promises can be fulfilled, not all are not reachable today. Therefore a complementary strategy needs to be adopted while vulnerabilities evade detection up to public releases. Instead of preventing their introduction, programs can be hardened to scale down their exploitability. Increasing the complexity to exploit or lowering the impact below specific thresholds makes the presence of vulnerabilities an affordable risk for the feature provided. The history of programming development encloses the experimentation and the adoption of so-called defence mechanisms. Their goals and performances can be diverse, but their implementation in worldwide adopted programs and systems (such as the Android Open Source Project) acknowledges their pivotal position.

To face these challenges, we provide the following contributions:

• We provide a manual categorisation of the vulnerabilities of the worldwide adopted Android Open Source Project up to June 2020. Clarifying to adopt a vulnerability analysis provides consistency in the resulting data set. It facilitates the explainability of the analyses and sets up for the updatability of the resulting set of vulnerabilities. Based on this analysis, we study the evolution of AOSP’s vulnerabilities. We explore the different temporal evolutions of the vulnerabilities affecting the system for their severity, the type of vulnerability, and we provide a focus on memory corruption-related vulnerabilities.

• We undertake the replication of a machine-learning based detection algorithms that, besides being part of the state-of-the-art and referenced to by ensuing works, was not available. Named VCCFinder, this algorithm implements a Support-Vector Machine and bases its training on Vulnerability-Contributing Commits and related patches for C and C++ code. Not in capacity to achieve analogous performances to the original article, we explore parameters and algorithms, and attempt to overcome the challenge provided by the over-population of unlabeled
entries in the data set. We provide the community with our code and results as a replicable baseline for further improvement.

• We eventually list the defence mechanisms that the Android Open Source Project incrementally implements, and we discuss how it sometimes answers comments the community addressed to the project’s developers. We further verify the extent to which specific memory corruption defence mechanisms were implemented in the binaries of different versions of Android (from API-level 10 to 28). We eventually confront the evolution of memory corruption-related vulnerabilities with the implementation timeline of related defence mechanisms.