SecureCyber: An SDN-Enabled SIEM for Enhanced Cybersecurity in the Industrial Internet of Things

The proliferation of smart technologies has undeniably brought forth numerous advantages. However, it has also introduced critical security issues and vulnerabilities that need to be addressed. In response, the development of appropriate and continuously adaptable countermeasures is essential to ensure the uninterrupted operation of critical environments. This paper presents an innovative approach through the introduction of an Software-Defined Networking (SDN)-enabled Security Information and Event Management (SIEM) system. The proposed SIEM solution effectively combines the power of Artificial Intelligence (AI) and SDN to protect Industrial Internet of Things (IIoT) applications. Leveraging AI capabilities, the SDN-enabled SIEM is capable of detecting a wide range of cyberattacks and anomalies that pose potential threats to IIoT environments. On the other hand, SDN plays a crucial role in mitigating identified risks and ensuring the security of IIoT applications. In particular, AI-driven insights and analysis guide the SDN-C in selecting appropriate mitigation actions to neutralize detected threats effectively. The experimental results demonstrate the efficiency of the proposed solution

New Silvicultural Treatments for Conifer Peri-Urban Forests Having Broadleaves in the Understory - The First Application in the Peri-Urban of Xanthi in Northeastern Greece

Background and Purpose: In Greece, forest practice did not develop special silvicultural treatments for planted conifer peri-urban forests where broadleaf trees appear as natural regeneration in the understory. The aims of this study are: a) to analyze the new proposed selective silvicultural treatments for the planted peri-urban forest of Xanthi and for analogous planted conifer forests, where broadleaf trees are naturally established in the understory b) to check the research hypothesis that the new selective silvicultural treatments exhibited higher intensity in terms of the basal area of cut trees, compared to that of traditional treatments in the studied peri-urban forest. Materials and Methods: In the traditional treatments, in the pine overstory cuttings, apart from the dead trees, mainly the malformed, damaged, suppressed and intermediate trees were cut. In the lower stories, the goal of the thinning was the more or less uniform distribution of broadleaf trees. In the proposed selective treatments, the main aim of pine cuttings is to release the broadleaf formations growing in the lower stories, while the treatments of the broadleaf trees will be a form of “positive selection” thinning. Plots were established in areas where the two types of treatments were going to be applied. In each plot, tree measurements and a classification of living trees into crown classes took place. After the application of the treatments the characteristics of cut trees were recorded. Results: In the established plots, before the cuttings (and thinning), total basal area was not statistically significantly different between the two types of treatments. In selective treatments, the basal area of all cut trees was statistically significantly higher than that of the results of traditional treatments. In the broadleaf cut trees there were statistical differences in the ratios of dominant, intermediate and suppressed trees between the two silvicultural approaches. Conclusions: The research hypothesis was verified. The intensity of treatments in terms of the basal area of cut trees was higher in the selective approach, compared to the traditional treatments in the Xanthi peri-urban forest. However, the overstory cutting intensity of the selective treatments depends on the spatial distributions and densities of broadleaved and conifer trees. In the broadleaf trees, the different objectives of the two types of treatments resulted in thinning with different qualitative characteristics. The proposed silvicultural treatments will accelerate the conversion of peri-urban conifer forests having an understory of broadleaf trees into broadleaved forests, or into mixed forests of conifers and broadleaf trees

Detection of Physical Adversarial Attacks on Traffic Signs for Autonomous Vehicles

Current vision-based detection models within Autonomous Vehicles, can be susceptible to changes within the physical environment, which cause unexpected issues. Physical attacks on traffic signs could be malicious or naturally occurring, causing incorrect identification of the traffic sign which can drastically alter the behaviour of the autonomous vehicle. We propose two novel deep learning architectures which can be used as detection and mitigation strategy for environmental attacks. The first is an autoencoder which detects anomalies within a given traffic sign, and the second is a reconstruction model which generates a clean traffic sign without any anomalies. As the anomaly detection model has been trained on normal images, any abnormalities will provide a high reconstruction error value, indicating an abnormal traffic sign. The reconstruction model is a Generative Adversarial Network (GAN) and consists of two networks; a generator and a discriminator. These map the input traffic sign image into a meta representation as the output. By using anomaly detection and reconstruction models as mitigation strategies, we show that the performance of the other models in pipelines such as traffic sign recognition models can be significantly improved. In order to evaluate our models, several types of attack circumstances were designed and on average, the anomaly detection model achieved 0.84 accuracy with a 0.82 F1-score in real datasets whereas the reconstruction model improved performance of traffic sign recognition model from average F1-score 0.41 to 0.641

Ecophysiology of seedlings of three Mediterranean pine species in contrasting light regimes

Seasonal dynamics of net photosynthesis (Anet) in 2-year-old seedlings of Pinus brutia Ten., Pinus pinea L. and Pinus pinaster Ait. were investigated. Seedlings were grown in the field in two light regimes: sun (ambient light) and shade (25% of photosynthetically active radiation (PAR)). Repeated measures analyses over a 12-month period showed that Anet varied significantly among species and from season to season. Maximum Anet in sun-acclimated seedlings was low in winter (yet remained positive) and peaked during summer. Maximum Anet was observed in June in P. pinea (12 μmol m–2 s–1), July in P. pinaster (23 μmol m–2 s–1) and August in P. brutia (20 μmol m–2 s–1). Photosynthetic light response curves saturated at a PAR of 200–300 μmol m–2 s–1 in winter and in shade-acclimated seedlings in summer. Net photosynthesis in sun-acclimated seedlings did not saturate at PAR up to 1900 μmol m–2 s–1 in P. brutia and P. pinaster. Minimum air temperature of the preceding night was apparently one of the main factors controlling Anet during the day. In shade-acclimated seedlings, photosynthetic rates were reduced by 50% in P. brutia and P. pinaster and by 20% in P. pinea compared with those in sun-acclimated seedlings. Stomatal conductance was generally lower in shaded seedlings than in seedlings grown in the sun, except on days with a high vapor pressure deficit. Total chlorophyll concentration per unit leaf area, specific leaf area (SLA) and height significantly increased in P. pinea in response to shade, but not in P. pinaster or P. brutia. In response to shade, P. brutia showed a significant increase in total chlorophyll concentration but not SLA. Photosynthetic and growth data indicate that P. pinaster and P. brutia are more light-demanding than P. pinea

Ecophysiology of seedlings of three Mediterranean pine species in contrasting light regimes

Seasonal dynamics of net photosynthesis (Anet) in 2-year-old seedlings of Pinus brutia Ten., Pinus pinea L. and Pinus pinaster Ait. were investigated. Seedlings were grown in the field in two light regimes: sun (ambient light) and shade (25% of photosynthetically active radiation (PAR)). Repeated measures analyses over a 12-month period showed that Anet varied significantly among species and from season to season. Maximum Anet in sun-acclimated seedlings was low in winter (yet remained positive) and peaked during summer. Maximum Anet was observed in June in P. pinea (12 μmol m–2 s–1), July in P. pinaster (23 μmol m–2 s–1) and August in P. brutia (20 μmol m–2 s–1). Photosynthetic light response curves saturated at a PAR of 200–300 μmol m–2 s–1 in winter and in shade-acclimated seedlings in summer. Net photosynthesis in sun-acclimated seedlings did not saturate at PAR up to 1900 μmol m–2 s–1 in P. brutia and P. pinaster. Minimum air temperature of the preceding night was apparently one of the main factors controlling Anet during the day. In shade-acclimated seedlings, photosynthetic rates were reduced by 50% in P. brutia and P. pinaster and by 20% in P. pinea compared with those in sun-acclimated seedlings. Stomatal conductance was generally lower in shaded seedlings than in seedlings grown in the sun, except on days with a high vapor pressure deficit. Total chlorophyll concentration per unit leaf area, specific leaf area (SLA) and height significantly increased in P. pinea in response to shade, but not in P. pinaster or P. brutia. In response to shade, P. brutia showed a significant increase in total chlorophyll concentration but not SLA. Photosynthetic and growth data indicate that P. pinaster and P. brutia are more light-demanding than P. pinea

Single-entry volume table for Pinus brutia in a planted peri-urban forest

Brutia pine is a Mediterranean tree species of high ecological value, widely planted for soil protection, windbreaks and timber, both in its native area and elsewhere in the Mediterranean region. However, there is not yet enough information relating its growth dynamics and yield. The aim of this study was to evaluate the volume of Pinus brutia in a planted peri-urban forest (reforested area) in Greece. A single-entry, individual tree volume model has been developed using data from 18 permanent experimental plots, in the context of a research project regarding recovery of degraded coniferous forests

Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions

The current study focuses on systematically analyzing the recent advances in the field of Multimodal eXplainable Artificial Intelligence (MXAI). In particular, the relevant primary prediction tasks and publicly available datasets are initially described. Subsequently, a structured presentation of the MXAI methods of the literature is provided, taking into account the following criteria: a) The number of the involved modalities, b) The stage at which explanations are produced, and c) The type of the adopted methodology (i.e. mathematical formalism). Then, the metrics used for MXAI evaluation are discussed. Finally, a comprehensive analysis of current challenges and future research directions is provided.Comment: 26 pages, 11 figure

Explainable AI-based Intrusion Detection in the Internet of Things

The revolution of Artificial Intelligence (AI) has brought about a significant evolution in the landscape of cyberattacks. In particular, with the increasing power and capabilities of AI, cyberattackers can automate tasks, analyze vast amounts of data, and identify vulnerabilities with greater precision. On the other hand, despite the multiple benefits of the Internet of Things (IoT), it raises severe security issues. Therefore, it is evident that the presence of efficient intrusion detection mechanisms is critical. Although Machine Learning (ML) and Deep Learning (DL)-based IDS have already demonstrated their detection efficiency, they still suffer from false alarms and explainability issues that do not allow security administrators to trust them completely compared to conventional signature/specification-based IDS. In light of the aforementioned remarks, in this paper, we introduce an AI-powered IDS with explainability functions for the IoT. The proposed IDS relies on ML and DL methods, while the SHapley Additive exPlanations (SHAP) method is used to explain decision-making. The evaluation results demonstrate the efficiency of the proposed IDS in terms of detection performance and explainable AI (XAI)

Data Protection and Cybersecurity Certification Activities and Schemes in the Energy Sector

Cybersecurity concerns have been at the forefront of regulatory reform in the European Union (EU) recently. One of the outcomes of these reforms is the introduction of certification schemes for information and communication technology (ICT) products, services and processes, as well as for data processing operations concerning personal data. These schemes aim to provide an avenue for consumers to assess the compliance posture of organisations concerning the privacy and security of ICT products, services and processes. They also present manufacturers, providers and data controllers with the opportunity to demonstrate compliance with regulatory requirements through a verifiable third-party assessment. As these certification schemes are being developed, various sectors, including the electrical power and energy sector, will need to access the impact on their operations and plan towards successful implementation. Relying on a doctrinal method, this paper identifies relevant EU legal instruments on data protection and cybersecurity certification and their interpretation in order to examine their potential impact when applying certification schemes within the Electrical Power and Energy System (EPES) domain. The result suggests that the EPES domain employs different technologies and services from diverse areas, which can result in the application of several certification schemes within its environment, including horizontal, technological and sector-specific schemes. This has the potential for creating a complex constellation of implementation models and would require careful design to avoid proliferation and disincentivising of stakeholders. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Hunting IoT Cyberattacks With AI - Powered Intrusion Detection

The rapid progression of the Internet of Things allows the seamless integration of cyber and physical environments, thus creating an overall hyper-connected ecosystem. It is evident that this new reality provides several capabilities and benefits, such as real-time decision-making and increased efficiency and productivity. However, it also raises crucial cybersecurity issues that can lead to disastrous consequences due to the vulnerable nature of the Internet model and the new cyber risks originating from the multiple and heterogeneous technologies involved in the loT. Therefore, intrusion detection and prevention are valuable and necessary mechanisms in the arsenal of the loT security. In light of the aforementioned remarks, in this paper, we introduce an Artificial Intelligence (AI)-powered Intrusion Detection and Prevention System (IDPS) that can detect and mitigate potential loT cyberattacks. For the detection process, Deep Neural Networks (DNNs) are used, while Software Defined Networking (SDN) and Q-Learning are combined for the mitigation procedure. The evaluation analysis demonstrates the detection efficiency of the proposed IDPS, while Q- Learning converges successfully in terms of selecting the appropriate mitigation action