The upgrade of energy infrastructures by the incorporation of communication and Internet technologies might introduce new risks for the security and for the smooth operation of electricity networks. Exploitation of the potential vulnerabilities of the heterogeneous systems used in smart energy grids (SEGs) may lead to the loss of control of critical electronic devices and, moreover, to the interception of confidential information. This may result in the disruption of essential services or even in total power failures. Addressing security issues that can ensure the confidentiality, the integrity, and availability of energy information is the primary objective for a transition to a new energy shape. This research paper presents an innovative system that can effectively offer SEG cybersecurity. It employs soft computing approaches, fuzzy cognitive maps, and a Mamdani fuzzy inference system in order to model overall security level. Three of the 27 scenarios considered herein have low overall security level, 21 of them have middle overall security, whereas only 3 are characterized as secure. The system automates the strategic planning of high security standards, as it allows a thorough audit of digital systems related to potential infrastructures and it contributes towards accurate decision-making in cases of threats.

Biosafety is defined as a set of preventive measures aimed at reducing the risk of infectious diseases’ spread to crops and animals, by providing quarantine pesticides. Prolonged and sustained overheating of the sea, creates significant habitat losses, resulting in the proliferation and spread of invasive species, which invade foreign areas typically seeking colder climate. This is one of the most important modern threats to marine biosafety. The research effort presented herein, proposes an innovative approach for Marine Species Identification, by employing an advanced intelligent Machine Hearing Framework (MHF). The final target is the identification of invasive alien species (IAS) based on the sounds they produce. This classification attempt, can provide significant aid towards the protection of biodiversity, and can achieve overall regional biosecurity. Hearing recognition is performed by using the Online Sequential Multilayer Graph Regularized Extreme Learning Machine Autoencoder (MIGRATE_ELM). The MIGRATE_ELM uses an innovative Deep Learning algorithm (DELE) that is applied for the first time for the above purpose. The assignment of the corresponding class ‘native’ or ‘invasive’ in its locality, is carried out by an equally innovative approach entitled ‘Geo Location Country Based Service’ that has been proposed by our research team.

Prolonged and sustained warming of the sea, acidification of surface water and rising of sea levels, creates significant habitat losses, resulting in the proliferation and spread of invasive species which immigrate to foreign regions seeking colder climate conditions. This is happening either because their natural habitat does not satisfy the temperature range in which they can survive, or because they are just following their food. This has negative consequences not only for the environment and biodiversity but for the socioeconomic status of the areas and for the human health. This research aims in the development of an advanced Machine Hearing system towards the automated recognition of invasive fish species based on their sounds. The proposed system uses the Spiking Convolutional Neural Network algorithm which cooperates with Geo Location Based Services. It is capable to correctly classify the typical local fish inhabitants from the invasive ones.