In the coming years, the application of paraffinic biofuels, such as Hydrotreated Vegetable Oils (HVO), in the transportation sector is expected to increase. However, as the composition of HVO is different compared to conventional diesel, the engine optimized for conventional fuel cannot take full advantage of the HVO beneficial properties. Suitable adjustment of a number of engine parameters, if not complete engine re-calibration, will enable the full exploitation of such fuels' potential for lower exhaust emissions and reduced fuel consumption. In the present work, the emission characteristics of HVO fuel in a light-duty Euro 5 diesel engine have been studied, under steady-state operation, as well as during the New European Driving Cycle (NEDC). The study was expanded to the investigation of exhaust emissions under modified Main Injection Timing (MIT) and EGR rate. The NEXBTL fuel, produced by Neste, was considered in the study and was compared with conventional market diesel. Emissions of nitric oxides (NO x ), soot, carbon monoxide (CO), carbon dioxide (CO 2 ), and hydrocarbons (HC) were studied. At default MIT and EGR settings the use of HVO resulted in a significant reduction of all regulated emissions. In addition, it was observed that the adjustment of MIT and EGR can enhance the exploitation of HVO potential for emissions reduction, highlighting the differences with the conventional diesel fuel.

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Industry 4.0 adoption demands integrability, interoperability, composability, and security. Currently, integrability, interoperability and composability are addressed by next-generation approaches for enterprise systems integration such as model-based standards, ontology, business process model life cycle management and the context of business processes. Security is addressed by conducting risk management as a first step. Nevertheless, security risks are very much influenced by the assets that the business processes are supported. To this end, this paper proposes an approach for automated risk estimation in smart sensor environments, called ARES, which integrates with the business process model life cycle management. To do so, ARES utilizes standards for platform, vulnerability, weakness, and attack pattern enumeration in conjunction with a well-known vulnerability scoring system. The applicability of ARES is demonstrated with an application example that concerns a typical case of a microSCADA controller and a prototype tool called Business Process Cataloging and Classification System. Moreover, a computer-aided procedure for mapping attack patterns-to-platforms is proposed, and evaluation results are discussed revealing few limitations.

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Traditional attack detection approaches utilize predefined databases of known signatures about already-seen tools and malicious activities observed in past cyber-attacks to detect future attacks. More sophisticated approaches apply machine learning to detect abnormal behavior. Nevertheless, a growing number of successful attacks and the increasing ingenuity of attackers prove that these approaches are insufficient. This paper introduces an approach for digital forensics-based early detection of ongoing cyber-attacks called Fronesis. The approach combines ontological reasoning with the MITRE ATT&CK framework, the Cyber Kill Chain model, and the digital artifacts acquired continuously from the monitored computer system. Fronesis examines the collected digital artifacts by applying rule-based reasoning on the Fronesis cyber-attack detection ontology to identify traces of adversarial techniques. The identified techniques are correlated to tactics, which are then mapped to corresponding phases of the Cyber Kill Chain model, resulting in the detection of an ongoing cyber-attack. Finally, the proposed approach is demonstrated through an email phishing attack scenario.INDEX TERMS Cyber-attack detection, cyber kill chain, cybersecurity, digital artifacts, MITRE ATT&CK, ontology, rule-based reasoning.