Biblio

As an extension of cloud computing, fog computing is proving itself more and more potentially useful nowadays. Fog computing is introduced to overcome the shortcomings of cloud computing paradigm in handling the massive amount of traffic caused by the enormous number of Internet of Things devices being increasingly connected to the Internet on daily basis. Despite its advantages, fog architecture introduces new security and privacy threats that need to be studied and solved as soon as possible. In this work, we explore two privacy issues posed by the fog computing architecture and we define privacy challenges according to them. The first challenge is related to the fog's design purposes of reducing the latency and improving the bandwidth, where the existing privacy-preserving methods violate these design purposed. The other challenge is related to the proximity of fog nodes to the end-users or IoT devices. We discuss the importance of addressing these challenges by putting them in the context of real-life scenarios. Finally, we propose a privacy-preserving fog computing paradigm that solves these challenges and we assess the security and efficiency of our solution.

Organizations are exposed to various cyber-attacks. When a component is exploited, the overall computed damage is impacted by the number of components the network includes. This work is focuses on estimating the Target Distribution characteristic of an attacked network. According existing security assessment models, Target Distribution is assessed by using ordinal values based on users' intuitive knowledge. This work is aimed at defining a formula which enables measuring quantitatively the attacked components' distribution. The proposed formula is based on the real-time configuration of the system. Using the proposed measure, firms can quantify damages, allocate appropriate budgets to actual real risks and build their configuration while taking in consideration the risks impacted by components' distribution. The formula is demonstrated as part of a security continuous monitoring system.

Timely and adequate response on the computer security incidents depends on the accurate monitoring of the security situation. The paper investigates the task of refinement of the attack models in the form of attack graphs. It considers some challenges of attack graph generation and possible solutions, including: inaccuracies in specifying the pre- and postconditions of attack actions, processing of cycles in graphs to apply the Bayesian methods for attack graph analysis, mapping of incidents on attack graph nodes, and automatic countermeasure selection for the nodes under the risk. The software prototype that implements suggested solutions is briefly specified. The influence of the modifications on the security monitoring is shown on a case study, and the results of experiments are described.

In this paper1 is proposed a graph model, designed to solve security challenges of information systems (IS). The model allows to describe information systems at two levels. The first is the transport layer, represented by the graph, and the second is functional level, represented by the semantic network. Proposed model uses "subject-object" terms to establish a security policy. Based on the proposed model, one can define information system security features location, and choose their deployment in the best way. In addition, it is possible to observe data access control security features inadequacy and calculate security value for the each IS node. Novelty of this paper is that one can get numerical evaluation of IS security according to its nodes communications and network structure.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

It is difficult to assess the security of modern enterprise networks because they are usually dynamic with configuration changes (such as changes in topology, firewall rules, etc). Graphical security models (e.g., Attack Graphs and Attack Trees) and security metrics (e.g., attack cost, shortest attack path) are widely used to systematically analyse the security posture of network systems. However, there are problems using them to assess the security of dynamic networks. First, the existing graphical security models are unable to capture dynamic changes occurring in the networks over time. Second, the existing security metrics are not designed for dynamic networks such that their effectiveness to the dynamic changes in the network is still unknown. In this paper, we conduct a comprehensive analysis via simulations to evaluate the effectiveness of security metrics using a Temporal Hierarchical Attack Representation Model. Further, we investigate the varying effects of security metrics when changes are observed in the dynamic networks. Our experimental analysis shows that different security metrics have varying security posture changes with respect to changes in the network.

We present AVAMAT: AntiVirus and Malware Analysis Tool - a tool for analysing the malware detection capabilities of AntiVirus (AV) products running on different operating system (OS) platforms. Even though similar tools are available, such as VirusTotal and MetaDefender, they have several limitations, which motivated the creation of our own tool. With AVAMAT we are able to analyse not only whether an AV detects a malware, but also at what stage of inspection does it detect it and on what OS. AVAMAT enables experimental campaigns to answer various research questions, ranging from the detection capabilities of AVs on OSs, to optimal ways in which AVs could be combined to improve malware detection capabilities.

The use of multi-terminal HVDC to integrate wind power coming from the North Sea opens de door for a new transmission system model, the DC-Independent System Operator (DC-ISO). DC-ISO will face highly stressed and varying conditions that requires new risk assessment tools to ensure security of supply. This paper proposes a novel risk-based static security assessment methodology named risk-based DC security assessment (RB-DCSA). It combines a probabilistic approach to include uncertainties and a fuzzy inference system to quantify the systemic and individual component risk associated with operational scenarios considering uncertainties. The proposed methodology is illustrated using a multi-terminal HVDC system where the variability of wind speed at the offshore wind is included.

Moving Target Defense (MTD) changes the attack surface of a system that confuses intruders to thwart attacks. Various MTD techniques are developed to enhance the security of a networked system, but the effectiveness of these techniques is not well assessed. Security models (e.g., Attack Graphs (AGs)) provide formal methods of assessing security, but modeling the MTD techniques in security models has not been studied. In this paper, we incorporate the MTD techniques in security modeling and analysis using a scalable security model, namely Hierarchical Attack Representation Models (HARMs), to assess the effectiveness of the MTD techniques. In addition, we use importance measures (IMs) for scalable security analysis and deploying the MTD techniques in an effective manner. The performance comparison between the HARM and the AG is given. Also, we compare the performance of using the IMs and the exhaustive search method in simulations.

This paper presents an overview of the research project “High-Performance Hybrid Simulation/Measurement-Based Tools for Proactive Operator Decision-Support”, performed under the auspices of the U.S. Department of Energy grant DE-OE0000628. The objective of this project is to develop software tools to provide enhanced real-time situational awareness to support the decision making and system control actions of transmission operators. The integrated tool will combine high-performance dynamic simulation with synchrophasor measurement data to assess in real time system dynamic performance and operation security risk. The project includes: (i) The development of high-performance dynamic simulation software; (ii) the development of new computationally effective measurement-based tools to estimate operating margins of a power system in real time using measurement data from synchrophasors and SCADA; (iii) the development a hybrid framework integrating measurement-based and simulation-based approaches, and (iv) the use of cutting-edge visualization technology to display various system quantities and to visually process the results of the hybrid measurement-base/simulation-based security-assessment tool. Parallelization and high performance computing are utilized to enable ultrafast transient stability analysis that can be used in a real-time environment to quickly perform “what-if” simulations involving system dynamics phenomena. EPRI's Extended Transient Midterm Simulation Program (ETMSP) is modified and enhanced for this work. The contingency analysis is scaled for large-scale contingency analysis using MPI-based parallelization. Simulations of thousands of contingencies on a high performance computing machine are performed, and results show that parallelization over contingencies with MPI provides good scalability and computational gains. Different ways to reduce the I/O bottleneck have been also exprored. Thread-parallelization of the sparse linear solve is explored also through use of the SuperLU_MT library. Based on performance profiling results for the implicit method, the majority of CPU time is spent on the integration steps. Hence, in order to further improve the ETMSP performance, a variable time step control scheme for the original trapezoidal integration method has been developed and implemented. The Adams-Bashforth-Moulton predictor-corrector method was introduced and designed for ETMSP. Test results show superior performance with this method.