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The paper analyzes the effect of the thermal rehabilitation works for a 2 room's apartment, built between the years 1975 - 1985, upon heating thermal energy consumption. Thus, it is determined the percent of thermal energy reducing, concerning the thermal isolations measurements of the buildings elements and those for the heat losses reducing of the heating installations elements in unheated spaces, as well as the optimizing of thermal energy consumption according to the new values of heat losses for thermal enveloped buildings. As conclusion of this study on can observe that it are not enough only the construction works made on glazed and opaque elements, they must be correlated with the rehabilitation measures of the heating installations so that their heat contribution will be in balance with reduced values for the heat necessity. [ABSTRACT FROM AUTHOR]

The existence of many cyber-attacks targeted to online environment, make eLearning platforms security a major concern. To secure an eLearning platform there are three interconnected strategies: prevention (the actions taken before an attack), detection (the action taken during an attack) and response (the action taken after an attack). This paper focuses on detection, providing different strategies to detect if eLearning platform security was compromised: intrusion detection, malware detection and suspicious activities detection. An attack tree is developed to simulate and to observe the impact of cyber-attacks on eLearning platforms. The attack tree lists and develops methods by which an attacker can cause a security incident on platforms. The attack tree is useful to explore certain attack paths in depth and to generate intrusion scenarios on a website. To conduct a cyber-attack to an eLearning platform, each edge to the internal node structure of the attack tree must be traversed. The internal nodes of the attack tree represent the seven stages of the intrusion model Kill Chain, which was defined by researchers from Lockheed Martin. This model consists of seven stages: reconnaissance, weaponization, delivery, exploitation, installation, command and control and action on objectives. The external edges of the tree that connect the leaf nodes, represent optional attack vectors. The results from the simulation attacks are used to presents the management of eLearning platforms security against cyber-attacks. An eLearning platform security is affected when the integrity or availability of the platform's files are compromised or additional malicious activity has been detected; for example malware infections, redirections to malicious websites or other suspicious activities like phishing or spamming. While there are no solutions to guarantee the security of eLearning platforms, this paper describes the attack vectors and presents various solutions to detect indicators of compromise. [ABSTRACT FROM AUTHOR]

The paper introduces a way of improving the survivability of an e-learning system by installing it on a virtual machine. The survivability can be defined as the capability of a system to fulfil its mission in the presence of the attacks, incidents or failure. It is presented the concept of e-learning system and the database conceptual structure. The e-learning survivability is computed through the variation of the defence and the cyber-attack levels. For arrival rate of the attacks it was used a stochastic point process model, where incidents were events or attacks that occur at a random moment. The incidents reported from the e-learning system where used to analyse survivability. The interest is to compute the e-learning system's survivability, when it represents the target of incidents. The survivability will depend on the levels of attacks and system security. The stronger the security level is, the better survivability system is and the system can remain in the same state. The simulation was made for different types of attacks and for different relationships between the cost of the security mechanisms and the probability for the system to end in different states. The results were compared with the survivability of a regular system. The curve between survivability and cost of the defence mechanisms was made in order to observe the survivability variation with the costs of these mechanisms. The data analysed can be classified by the level of the incidents and can be used in a model to compute the survivability of this concept. [ABSTRACT FROM AUTHOR]