As cybercrime is becoming increasingly sophisticated, effective cybersecurity is crucial to safeguard digital assets and protect critical infrastructures from emerging threats. Several security applications exploit recent advances in (Big) data analysis and Artificial Intelligence (AI) to prevent and respond to malicious activities. Towards this direction, supervised and unsupervised Machine Learning (ML) methods are used to detect anomalies or reveal patterns that may indicate potential threats. However, the successful implementation of these technologies requires security practitioners to undergo specialized training to fully understand and use AI-driven tools and data analytics. On the other hand, AI models themselves are vulnerable to a variety of cyber threats, which can compromise their training data and learning processes. To ensure the safe operation of these systems, especially when deployed in adversarial environments, it is crucial to create novel AI adversarial algorithms and models that are resilient against diverse security threats. This work presents a conceptual framework based on Large Language Models (LLMs) supported by a Multi-Agent layer for training of security practitioners in various advanced technologies and enhance ML models ability to detect and respond to emerging cyber threats effectively.

Green, sustainable and energy-aware computing terms are gaining more and more attention during the last years. The increasing complexity of Internet of Things (IoT) applications makes energy efficiency an important requirement, imposing new challenges to software developers. Software tools capable of providing energy consumption estimations and identifying optimization opportunities are critical during all the phases of application development. This work proposes a novel framework that targets the energy efficiency at application development level. The proposed framework is implemented as a single user-friendly tool-flow, providing a variety of useful features, such as the estimation of the energy consumption without the need of executing the application on the targeted IoT devices and the estimation of potential gains by GPU acceleration on modern heterogeneous IoT architectures. The proposed methodology provides several novel contributions, such as the combination of static analysis and dynamic instrumentation approaches in order to exploit the advantages of both. The framework is evaluated on widely used benchmarks, achieving increased estimation accuracy (more than 90% for similar architectures and more than 72% for the potential use of the GPU). The effectiveness of the framework is further demonstrated using two industrial use-cases achieving an energy reduction from 91% up to 98%.

Developing modern secure and low-energy applications in a short time imposes new challenges and creates the need of designing new software tools to assist developers in all phases of application development. The design of such tools cannot be considered a trivial task, as they should be able to provide optimization of multiple quality requirements. In this paper, we introduce the SDK4ED platform, which incorporates advanced methods and tools for measuring and optimizing maintainability, dependability and energy. The presented solution offers a com-plete tool-flow for providing indicators and optimization meth-ods with emphasis on embedded software. Effective forecasting models and decision-making solutions are also implemented to improve the quality of the software, respecting the constraints imposed on maintenance standards, energy consumption limits and security vulnerabilities. The use of the SDK4ED platform is demonstrated in a healthcare embedded application.

Energy management in buildings equipped with renewable energy is vital for reducing electricity costs and maximizing occupant comfort. Despite several studies on the scheduling of appliances, a battery, and heating, ventilating, and air-conditioning (HVAC), there is a lack of a comprehensive and time-scalable approach that integrates predictive information such as renewable generation and thermal comfort. In this paper, we propose an online energy management framework to incorporate the optimal energy scheduling and prediction model of PV generation and thermal comfort by the model predictive control (MPC) approach. The energy management problem is formulated as coordinated three optimization problems covering a fast and slow time-scale.This reduces the time complexity without a significant negative impact on the global nature and quality of the result. Experimental results show that the proposed framework achieves optimal energy management that takes into account the trade-off between the electricity bill and thermal comfort.

Lately, more and more applications are deployed on heterogeneous, power-constrained edge-computing devices. Bringing computation closer to the data, contributes both to latency and energy consumption reduction due to the elimination of excessive data transfers. However, while the main concern in such environments is the minimization of energy consumption, the heterogeneity in compute resources found at the edge may lead to Quality of Service (QoS) violations. At the same time, Serverless computing, the next frontier of Cloud computing has emerged to offer unprecedented elasticity by utilizing fine-grained, stateless functions. The reduction in the execution time and the modest memory footprint of such decomposed applications, allow for fine-grained resource multiplexing. In this work, we propose a methodology for application decomposition into fine-grained functions and energy-aware function placement on a cluster of edge devices subject to user-specified QoS guarantees.

Source code refactorings and transformations are extensively used by embedded system developers to improve the quality of applications, often supported by various open source and proprietary tools. They either aim at improving the design time quality such as the maintainability and reusability of software artifacts, or the runtime quality such as performance and energy efficiency. However, an inherent trade-off between design- and run-time qualities is often present posing challenges to embedded software development. This work is a first step towards the investigation of the impact of transformations for improving the performance and the energy efficiency on software quality metrics and the impact of refactorings for increasing the design time quality on the execution time, the memory and the energy consumption. Based on a set of embedded applications from widely used benchmark suites and typical transformations and refactorings, we identify interrelations and trade-offs between the aforementioned metrics.