Centre for Data Intensive Sciences and Applications

Welcome to the February PhD-seminar 2023


When? Friday February 3rd 14.00-16.00
Where? D1172 or via zoom, the link will be sent out to those who register
Registration? Register via this link https://forms.gle/t1D3iHkC6mocidQF6 no later than February 1st.

14.00 – 14.10 Welcome and practical information from Welf Löwe
14.10 – 14.55 Presentation and discussion: Digital twin development at Volvo CE (VCE) for Wheel loaders (WLO) –Manoranjan Kumar, Volvo
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion – Design and Analysis of Self-Protection: Adaptive Security for Software-Intensive Systems – Charilos Skandylas; LNU
15.50 – 16.00 Sum up and plan for our next seminar on March 3rd


Digital twin development at Volvo CE (VCE) for Wheel loaders (WLO) –Manoranjan Kumar, Volvo CE
In recent years, numerous advancements have been made in technology related to IOT, data visualization, and simulation. Therefore, VCE has decided to develop the digital twin model of Wheel loaders to support and understand the customers need. This decision comes with opportunities and challenges. The success of digital twin depends on a minimum of data being extracted from real machines allowing still estimates of complete vehicle usages using the simulations. Those simulations can be data driven or physics based. VCE’s Digital twin platform supports the developments of hardware as well as software for Digital twins (DT) including:

  • WLO usage DT: Logs of WLO capture the behaviors of drivers and what kind of materials they are handling and classifies the driving styles using machine learning.
  • WLO DT: Complete vehicle simulations of WLOs using the physical properties of different components. The simulation also captures the different controls and path followings. The simulation is “twinned” with the loads measured in real machines. Good correlations are observed for different kinds of driving behaviors.
  • EGR DT: Exhaust gar recirculation (EGR) clogging can be monitored in different types of construction machines using online and offline machine learning approaches.

More information about the research project: https://lnu.se/en/research/research-projects/doctoral-project-digital-twin-developments-within-volvo-ce/

Design and Analysis of Self-Protection: Adaptive Security for Software-Intensive Systems – Charilos Skandylas, LNU
Today’s software landscape features a high degree of complexity, frequent changes in requirements and stakeholder goals, and uncertainty. Therefore, in the corresponding threat landscape cybersecurity attacks are a common occurrence, and their consequences are often severe. Self-adaptive systems have been proposed to mitigate the complexity and frequent degree of change by adapting at run-time to deal with situations not known at design time. They, however, are not immune to attacks, as they themselves suffer from high degrees of complexity and uncertainty. Therefore, suitable software systems that can dynamically defend themselves from adversaries are required. Such systems are called self-protecting systems and aim to identify, analyze and mitigate threats autonomously.

This presentation will discuss approaches with the goal of providing software systems with self-protection capabilities in two parts. The first part aims to enhance the security of architecture-based self-adaptive systems and equip them with self-protection capabilities. Both proactive and reactive self-protection techniques will be discussed. Proactive techniques aim to protect a software system by accurately analyzing its current and future security relevant behaviour and steering the system towards the most secure behavior, minimizing the attack surface. Reactive techniques provide self-protection to an architecture based self-adaptive system via effective countermeasure selection at runtime. In the second part, we extend a classical decentralized information flow control model by incorporating trust and adding adaptation capabilities that allow a full decentralized, open system system to identify security threats and self-organize to maximize the average trust between the system entities while maintaining the security policies of each of the system’s entities.