Jean-François Lalande

TL;DR: Experimental results show the feasibility and effectiveness of the proposed approach to detect the hidden data exchange between colluding applications, based on artificial intelligence tools, such as neural networks and decision trees.

TL;DR: This paper presents an extended access control model based on attributes associated with objects and subjects that incorporates trust and privacy issues in order to make access control decisions sensitive to the cross-organizational collaboration context.

TL;DR: This paper developed a malware that slowly leaks collected private information and sends it synchronously based on four covert channel techniques based on a minimized footprint to achieve a high covertness.

TL;DR: This paper proposes to automatically detect when an application accesses private data and to log this access in a third-party application and shows that the proposed methodology can be implemented as an IPS, in order to prevent such accesses.

TL;DR: This article proposes a method to enhance the execution of the malicious code of unknown malware that has triggering protections, for example branching conditions that wait for an event or expect a specific value for a variable before triggering malicious execution, and proposes an enhanced monkey called GroddDroid, that stimulates the GUI of an application and forces the Execution of some branching conditions if needed.