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.

With the escalating frequency of cybersecurity threats in public cloud computing environments, there is a pressing need for robust security measures to safeguard sensitive data and applications. This research addresses growing security concerns in the cloud by proposing an innovative security information and event management system (SIEM) that offers automated visibility of cloud resources. Our implementation includes a virtual network comprising virtual machines, load balancers, Microsoft Defender for Cloud, and an application gateway that functions as a web application firewall (WAF). This WAF scans incoming Internet traffic and provides centralized protection against common exploits and vulnerabilities, securing web applications within the cloud environment. We deployed the SIEM system to automate visibility and incident response for cloud resources. By harnessing the power of this employed SIEM, the developed system can continuously monitor, detect security incidents, and proactively mitigate potential security threats. Microsoft Defender for Cloud consistently assesses the configuration of cloud resources against industry standards, regulations, and benchmarks to ensure compliance requirements are met. Our findings highlight the practicality and effectiveness of deploying such solutions to safeguard cloud resources, offering valuable insights to organizations and security professionals seeking sustainable and resilient security measures in the cloud computing environment.

Smart agriculture based on new types of sensors, data analytics and automation, is an important enabler for optimizing yields and maximizing efficiency to feed the world’s growing population while limiting environmental pollution. The aim of this paper is to describe a multi-sensor Internet of Things (IoT) system for agriculture consisting of a soil probe, an air probe and a smart data logger. The implementation details will focus of the integration element and the innovative Artificial Intelligence based gas identification sensor. Furthermore, the paper focuses on the analytics and decision support system implementation that provides farming recommendations and is enhanced with a feedback loop from farmers and a social trust index that will increase the reliability of the system.

Renewable energy is an essential solution for addressing climate change, providing sustainable options that are vital for a more environmentally friendly future. Integrating information technology (IT) into renewable energy systems has driven remarkable progress, enhanced efficiency, and enabled remote monitoring. Nevertheless, integrating IT into these systems dramatically increases their vulnerability to cyber threats and potential attacks. This study thoroughly investigates the enhancement of security measures in an IoT-based solar energy remote monitoring system. The research integrates advanced technologies, including Advanced Encryption Standard (AES), myRIO board, and NI’s SystemLink Cloud platform, to enhance data security in smart solar energy monitoring systems. Emphasizing AES encryption ensures secure information exchange between the myRIO board and the computer. NI’s SystemLink Cloud offers a user-friendly interface for real-time monitoring of critical solar system parameters, supported by robust security measures such as HTTPS encryption and access control. This study sets higher data protection standards in smart energy systems by promoting advanced encryption and secure cloud infrastructures. The approach involves seamlessly integrating renewable energy sources with IT innovations while prioritizing proactive measures to strengthen solar energy system security.

The actual trade-off among engine emissions and performance requires detailed investigations into exhaust system configurations. Correlations among engine data acquired by sensors are susceptible to artificial intelligence (AI)-driven performance assessment. The influence of exhaust back pressure (EBP) on engine performance, mainly on effective power, was investigated on a turbocharged diesel engine tested on an instrumented dynamometric test-bench. The EBP was externally applied at steady state operation modes defined by speed and load. A complete dataset was collected to supply the statistical analysis and machine learning phases—the training and testing of all the AI solutions developed in order to predict the effective power. By extending the cloud-/edge-computing model with the cloud AI/edge AI paradigm, comprehensive research was conducted on the algorithms and software frameworks most suited to vehicular smart devices. A selection of artificial neural networks (ANNs) and regressors was implemented and evaluated. Two proof-of concept smart devices were built using state-of-the-art technology—one with hardware acceleration for “complete cycle” AI and the other with a compact code and size (“AI in a nut-shell”) with ANN coefficients embedded in the code and occasionally offline “statistical re-calibration”.

The integration of Internet of Things (IoT) technologies into solar energy systems has transformed them into smart solar energy systems, enabling advanced real-time monitoring, control, and optimization. However, this connectivity also expands the attack surface, exposing critical components to cybersecurity threats that could compromise system reliability and long-term sustainability. This study presents a comprehensive cybersecurity threat modeling analysis for IoT-based smart solar energy systems using the STRIDE threat model to systematically identify, categorize, and assess potential security risks. These risks, if unmitigated, could disrupt operations and hinder large-scale adoption of solar energy. The methodology begins with a system use case outlining the architecture and key components, including sensors, PV modules, IoT nodes, gateways, cloud infrastructure, and remote-access interfaces. A Data Flow Diagram (DFD) was developed to visualize the data flow and identify the critical trust boundaries. The STRIDE model was applied to classify threats, such as spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege across components and their interactions. The DREAD risk assessment model was then used to prioritize threats based on the Damage Potential, Reproducibility, Exploitability, Affected Users, and Disability. The results indicate that most threats fall into the high-risk category, with scores ranging from 2.6 to 2.8, emphasizing the need for targeted mitigation. This study proposes security recommendations to address the identified threats and enhance the resilience of IoT-enabled solar energy systems. By securing these infrastructures, this research supports the transition to sustainable energy by ensuring system integrity and protection against cyber threats. The combined use of STRIDE and DREAD provides a robust framework for identifying, categorizing, and prioritizing risks, enabling effective resource allocation and targeted security measures. These findings offer critical insights into safeguarding renewable energy systems against evolving cyber threats, contributing to global energy sustainability goals in an increasingly interconnected world.