In this study, the methodology of cyber-resilience in small and medium-sized organizations (SMEs) is investigated, and a comprehensive solution utilizing prescriptive malware analysis, detection and response using open-source solutions is proposed for detecting new emerging threats. By leveraging open-source solutions and software, a system specifically designed for SMEs with up to 250 employees is developed, focusing on the detection of new threats. Through extensive testing and validation, as well as efficient algorithms and techniques for anomaly detection, safety, and security, the effectiveness of the approach in enhancing SMEs’ cyber-defense capabilities and bolstering their overall cyber-resilience is demonstrated. The findings highlight the practicality and scalability of utilizing open-source resources to address the unique cybersecurity challenges faced by SMEs. The proposed system combines advanced malware analysis techniques with real-time threat intelligence feeds to identify and analyze malicious activities within SME networks. By employing machine-learning algorithms and behavior-based analysis, the system can effectively detect and classify sophisticated malware strains, including those previously unseen. To evaluate the system’s effectiveness, extensive testing and validation were conducted using real-world datasets and scenarios. The results demonstrate significant improvements in malware detection rates, with the system successfully identifying emerging threats that traditional security measures often miss. The proposed system represents a practical and scalable solution using containerized applications that can be readily deployed by SMEs seeking to enhance their cyber-defense capabilities.

Principal Component Analysis (PCA) is a method based on statistics and linear algebra techniques, used in hyperspectral satellite imagery for data dimensionality reduction required in order to speed up and increase the performance of subsequent hyperspectral image processing algorithms. This paper introduces the PCA approximation method based on a geometric construction approach (gaPCA) method, an alternative algorithm for computing the principal components based on a geometrical constructed approximation of the standard PCA and presents its application to remote sensing hyperspectral images. gaPCA has the potential of yielding better land classification results by preserving a higher degree of information related to the smaller objects of the scene (or to the rare spectral objects) than the standard PCA, being focused not on maximizing the variance of the data, but the range. The paper validates gaPCA on four distinct datasets and performs comparative evaluations and metrics with the standard PCA method. A comparative land classification benchmark of gaPCA and the standard PCA using statistical-based tools is also described. The results show gaPCA is an effective dimensionality-reduction tool, with performance similar to, and in several cases, even higher than standard PCA on specific image classification tasks. gaPCA was shown to be more suitable for hyperspectral images with small structures or objects that need to be detected or where preponderantly spectral classes or spectrally similar classes are present.

This paper proposes a power-system protection device designed to be integrated in smart environments based on Internet-of-Things technologies. The proposed system enhances electrical safety by fast disconnection of the power supply in case of fault events like leakage current, electrical arc, overcurrent or overvoltage and has been designed with the goal to be integrated in smart environments like smart homes or smart cities for protecting the electrical equipment. The system also enables real-time monitoring and notification events through an advanced communication interface using a data concentrator architecture. This paper provides an extended description of the proposed system’s design and implementation, as well as the experimental validation results.

Constrained random stimulus generation is no longer sufficient to fully simulate the functionality of a digital design. The increasing complexity of today’s hardware devices must be supported by powerful development and simulation environments, powerful computational mechanisms, and appropriate software to exploit them. Reinforcement learning, a powerful technique belonging to the field of artificial intelligence, provides the means to efficiently exploit computational resources to find even the least obvious correlations between configuration parameters, stimuli applied to digital design inputs, and their functional states. This paper, in which a novel software system is used to simplify the analysis of simulation outputs and the generation of input stimuli through reinforcement learning methods, provides important details about the setup of the proposed method to automate the verification process. By understanding how to properly configure a reinforcement algorithm to fit the specifics of a digital design, verification engineers can more quickly adopt this automated and efficient stimulus generation method (compared with classical verification) to bring the digital design to a desired functional state. The results obtained are most promising, with even 52 times fewer steps needed to reach a target state using reinforcement learning than when constrained random stimulus generation was used.

Digital integrated circuits play an important role in the development of new information technologies and support Industry 4.0 from a hardware point of view. There is great pressure on electronics companies to reduce the time-to-market for product development as much as possible. The most time-consuming stage in hardware development is functional verification. As a result, many industry and academic stakeholders are investing in automating this crucial step in electronics production. The present work aims to automate the functional verification process by means of genetic algorithms that are used for generating the relevant input stimuli for full simulation of digital design behavior. Two important aspects are pursued throughout the current work: the implementation of genetic algorithms must be time-worthy compared to the application of the classical constrained-driven generation and the verification process must be implemented using tools accessible to a wide range of practitioners. It is demonstrated that for complex designs, functional verification powered by the use of genetic algorithms can go beyond the classical method of performing verification, which is based on constrained-random stimulus generation. The currently proposed methods were able to generate several sets of highly performing stimuli compared to the constraint-random stimulus generation method, in a ratio ranging from 57:1 to 205:1. The performance of the proposed approaches is comparable to that of the well-known NSGA-II and SPEA2 algorithms.