Software/Hardware And unKnown EnviRonment Interactions (SHAKER) team

Activities like mining exploration, port or coastal surveillance are increasingly carried out by swarms of drones controlled semi-automatically. The complexity of their networks makes them vulnerable to cyberattacks, however.

To secure the data contained and transmitted between drones, the ENSTA Bretagne/Lab-STICC Software/Hardware And unKnown EnviRonment Interactions (SHAKER) team launched the DISPEED* project with the AID in September 2022. Its goal? “To develop an intrusion detection system (IDS) factoring in the resources which each of the drones in the swarm needs in terms of energy and calculating capacity,” explains Camélia Slimani, a post-doctoral student at ENSTA Bretagne and a member of this team. 

The most widespread IDSs leverage machine learning algorithms which require significant memory and computing power. Not all types of drones have the same processing capacities though (processors, memory, storage), which affects their cybersecurity performance. “The challenge is to come up with an execution model which strikes a relevant trade-off between swift detection and energy use depending on the criticality of the attack and state of the system and the mission,” the researcher clarified.

The research team initially conducted an energy use and performance study of several existing IDSs before drawing up an appropriate execution strategy for the missions chosen for a population of drones operating autonomously. 

* Project “Détection d’Intrusion et compromis Sécurité / Performance / Energie, Etude pour les meutes de Drones” (“Intrusion Detection and Security / Performance / Energy tradeoff, a Study for Drone swarms”) financed by the Ministry for the Armed Forces Defense Innovation Agency (AID).

• Objective of this project: to develop methods and tools for modeling data access profiles in an efficient and non-intrusive manner then use the models established to develop strategies for optimizing the energy consumption of compute nodes during the data access stage.
• Financed by: Atos
• Led by: Jalil Boukhobza, research professor at ENSTA Bretagne/Lab-STICC (SHARP department, SHAKER team)

In performance terms, storage systems represent one of the most significant weak links in an IT system, particularly for applications which process large amounts of data such as in the field of high-performance computing (HPC).  

The emergence of new storage technologies is an opportunity to reduce the performance gap between working memory and storage, as well as energy consumption. 
These technologies are deployed at various levels:

- storage memory (e.g. 3DxPoint),
- its interface (e.g. NVMe),
- or its software management (e.g. object store).

These technologies imply significant growth in the complexity of storage management in order to meet the service quality requirements of applications. 
 
 

Objective: outline a model and strategies for running intrusion detection systems embedded on drones equipped with heterogeneous architectures and striking a relevant compromise according to the attack criticality and state of the system and mission, between detection speed/energy consumption.

• Financed by: AID
• Partners: FORTH Greece, Naval Group, UBO
• Led by: Jalil Boukhobza, research professor at ENSTA Bretagne/Lab-STICC (SHARP department, SHAKER team)

The distributed operation of fleets of drones during missions makes them vulnerable to diverse attacks that it is crucial to detect. Embedded in these drones are hardware components (computing and storage) with heterogenous computing power and energy consumption for performing the various tasks necessary for their mission.

The project sets out to develop models, strategies and tools for optimizing the energy cost of intrusion detection on a fleet of drones or any other cooperative system with major energy or hardware capacity constraints. These systems operate in cooperation to accomplish a joint mission. The network load therefore varies enormously depending on the context of the mission, which means that the intrusion detection system does not need to be run continuously on equipment requiring significant hardware capacity or consuming a considerable share of the system’s energy.

The aim of the project is thus to study and analyze how the performance of the IDS can be adapted using various hardware components depending on this network load and the context of the mission.

4 challenges underpin the project:

• Challenge 1: modeling the execution environment
• Challenge 2: setting up an assessment platform
• Challenge 3: designing a configuration selection strategy based on multi-objective optimization tooling
• Challenge 4: implementing an inter-drone balancing/offset computing system for reducing energy consumption.

Objective: design effective data positioning systems on multi-tiered storage architectures in the field of high-performance computing.

• Financed by: CEA
• Led by: Jalil Boukhobza, research professor at ENSTA Bretagne/Lab-STICC (SHARP department, SHAKER team)

In performance terms, storage systems represent one of the most significant weak links in an IT system, particularly for applications which process large amounts of data. The emergence of new storage technologies is an opportunity to reduce the performance gap between working memory and storage. These technologies, deployed at the level of storage memory (e.g. 3DxPoint), its interface (e.g. NVMe) and even its software management (e.g. object store), imply significant growth in the complexity of storage management. In addition, amid the "big Data" boom, more and more applications are processing huge amounts of data, and present different levels of criticality.

Against this backdrop, we intend to study and come up with new data positioning strategies with different levels of criticality on heterogenous, geo-distributed storage systems. As part of this project, we will explore several techniques including machine learning, reinforcement learning and optimization methods, to guarantee effective online data positioning.