In earlier works we presented a computational infrastructure that allows an analyst to estimate the security of a system in terms of the loss that each stakeholder stands to sustain as a result of security breakdowns. In this paper we illustrate this infrastructure by means of an e-commerce application.
Real-time estimation and short-term prediction of traffic conditions is one of major concern of traffic managers and ITS-oriented systems. Model-based methods appear now as very promising ways in order to reach this purpose. Such methods are already used in process control (Kalman filtering, Luenberger observers). In the application presented in this paper, due to the high non linearity of the traffic models, particle filter (PF) approach is applied in combination with the well-known first order macroscopic traffic model. Not only shall we show that travel time prediction is successfully realized, but also that we are able to estimate, in real time, the motorway traffic conditions, even on points with no measurement facilities, having, in a way, designed a virtual sensor.
Addressing Cybersecurity within e-Learning systems becomes empowered to make online information more secure. Certain competences need to be identified as necessary skills to manage security online such the ability to assess sources and architectural components, understanding the privacy, confidentiality and user authentication. Security management approaches quantifying security threats in e-learning are common with other e-services. It is of our need to adopt a quantitative security risk management process in order to determine the worthiest attack and the ignored one, based on financial business risk measure which is the measure of the mean failure cost.This paper proposes a cyber security measure called the Mean Failure Cost (MFC) suitable for e-Learning systems. It is based on the identification of system’s architecture, the well-defined classes of stakeholders, the list of possible threats and vulnerabilities and the specific security requirements related to e-Learning systems and applications. In the mean time, security requirements are considered as appropriate mechanisms for preventing, detecting and recovering security attacks, for this reason an extension of the MFC measure is presented in order to detect the most critical security requirements. Also this paper highlights the security measures and guidelines for controlling e-Learning security policies regarding the most critical security requirements.
Abstract-The Supervisory Control and Data Acquisition (SCADA) system discussed in this work manages a distributed control network for the Tunisian Electric & Gas Utility. The network is dispersed over a large geographic area that monitors and controls the flow of electricity/gas from both remote and centralized locations. The availability of the SCADA system in this context is critical to ensuring the uninterrupted delivery of energy, including safety, security, continuity of operations and revenue. Such SCADA systems are the backbone of national critical cyber-physical infrastructures. Herein, we propose adapting the Mean Failure Cost (MFC) metric for quantifying the cost of unavailability. This new metric combines the classic availability formulation with MFC. The resulting metric, socalled Econometric Availability (EA), offers a computational basis to evaluate a system in terms of the gain/loss ($/hour of operation) that affects each stakeholder due to unavailability.
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