![]() Modern critical infrastructures are characterized by a high degree of complexity, in terms of vulnerabilities, threats, and interdependencies that characterize them. 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. The MIGRATE_ELM uses an innovative Deep Learning algorithm (DELE) that is applied for the first time for the above purpose. Hearing recognition is performed by using the Online Sequential Multilayer Graph Regularized Extreme Learning Machine Autoencoder (MIGRATE_ELM). This classification attempt, can provide significant aid towards the protection of biodiversity, and can achieve overall regional biosecurity. The target is the identification of invasive alien species (IAS), based on the sounds they produce. The research effort presented herein, proposes an innovative approach for Marine Species Identification, by employing an advanced intelligent Machine Hearing Framework (MHF). This is one of the most important modern threats to marine biosafety. 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. The system also forecasts future air pollutant values and their risk level for the urban environment, based on the temperature and rainfall variation as derived from sixteen CMIP5 climate models for the period 2020–2099.īiosafety 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. Specifically, Self-Organizing Maps are used to extract hidden knowledge in the raw data of atmospheric recordings and Fuzzy Cognitive Maps are employed to study the conditions and to analyze the factors associated with the problem. ![]() This paper introduces an innovative hybrid system of predicting air pollutant values (IHAP) using Soft computing techniques. In addition, the topography of an area in conjunction with the recording of meteorological conditions conducive to atmospheric pollution, act as catalytic factors in increasing the concentrations of primary or secondary pollutants. During recent years the economic crisis has led to the burning of timber products for domestic heating, which adds to the burden of the atmosphere with dangerous pollutants. Air pollution in modern urban centers such as Athens has a significant impact on human activities such as industry and transport. Prolonged climate change contributes to an increase in the local concentrations of O3 and PMx in the atmosphere, influencing the seasonality and duration of air pollution incidents. It is an effective and accurate Ensemble Machine Learning forensics tool to Network Traffic Analysis, Demystification of Malware Traffic and Encrypted Traffic Identification. This paper proposes a novel intelligence driven Network Flow Forensics Framework (NF3) which uses low utilization of computing power and resources, for the Next Generation Cognitive Computing SOC (NGC2SOC) that rely solely on advanced fully automated intelligence methods. For all the reasons above, in most cases, the traditional software fails completely to recognize unidentified vulnerabilities and zero-day exploitations. In addition, an additional significant inability of these software packages is they create high false positive rates because they are deprived of accurate predicting mechanisms. A serious potential disadvantage of the traditional software solutions used today for computer network monitoring, and specifically for the instances of effective categorization of the encrypted or obfuscated network flow, which enforces the rebuilding of messages packets in sophisticated underlying protocols, is the requirements of computational resources. The supervision and categorization of network flow is an essential process not only for the scheduling, management, and regulation of the network's services, but also for attacks identification and for the consequent forensics' investigations. ![]() The fundamental aspects of an effective SOC is related to the ability to examine and analyze the vast number of data flows and to correlate several other types of events from a cybersecurity perception. ![]() A Security Operations Center (SOC) can be defined as an organized and highly skilled team that uses advanced computer forensics tools to prevent, detect and respond to cybersecurity incidents of an organization. ![]()
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