1,998 research outputs found

    Stochastic model checking for predicting component failures and service availability

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    When a component fails in a critical communications service, how urgent is a repair? If we repair within 1 hour, 2 hours, or n hours, how does this affect the likelihood of service failure? Can a formal model support assessing the impact, prioritisation, and scheduling of repairs in the event of component failures, and forecasting of maintenance costs? These are some of the questions posed to us by a large organisation and here we report on our experience of developing a stochastic framework based on a discrete space model and temporal logic to answer them. We define and explore both standard steady-state and transient temporal logic properties concerning the likelihood of service failure within certain time bounds, forecasting maintenance costs, and we introduce a new concept of envelopes of behaviour that quantify the effect of the status of lower level components on service availability. The resulting model is highly parameterised and user interaction for experimentation is supported by a lightweight, web-based interface

    Modelling Security of Critical Infrastructures: A Survivability Assessment

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    Critical infrastructures, usually designed to handle disruptions caused by human errors or random acts of nature, define assets whose normal operation must be guaranteed to maintain its essential services for human daily living. Malicious intended attacks to these targets need to be considered during system design. To face these situations, defence plans must be developed in advance. In this paper, we present a Unified Modelling Language profile, named SecAM, that enables the modelling and security specification for critical infrastructures during the early phases (requirements, design) of system development life cycle. SecAM enables security assessment, through survivability analysis, of different security solutions before system deployment. As a case study, we evaluate the survivability of the Saudi Arabia crude-oil network under two different attack scenarios. The stochastic analysis, carried out with Generalized Stochastic Petri nets, quantitatively estimates the minimization of attack damages on the crude-oil network

    Cyber resilience meta-modelling: The railway communication case study

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    Recent times have demonstrated how much the modern critical infrastructures (e.g., energy, essential services, people and goods transportation) depend from the global communication networks. However, in the current Cyber-Physical World convergence, sophisticated attacks to the cyber layer can provoke severe damages to both physical structures and the operations of infrastructure affecting not only its functionality and safety, but also triggering cascade effects in other systems because of the tight interdependence of the systems that characterises the modern society. Hence, critical infrastructure must integrate the current cyber-security approach based on risk avoidance with a broader perspective provided by the emerging cyber-resilience paradigm. Cyber resilience is aimed as a way absorb the consequences of these attacks and to recover the functionality quickly and safely through adaptation. Several high-level frameworks and conceptualisations have been proposed but a formal definition capable of translating cyber resilience into an operational tool for decision makers considering all aspects of such a multifaceted concept is still missing. To this end, the present paper aims at providing an operational formalisation for cyber resilience starting from the Cyber Resilience Ontology presented in a previous work using model-driven principles. A domain model is defined to cope with the different aspects and “resilience-assurance” processes that it can be valid in various application domains. In this respect, an application case based on critical transportation communications systems, namely the railway communication system, is provided to prove the feasibility of the proposed approach and to identify future improvements

    Nature-inspired survivability: Prey-inspired survivability countermeasures for cloud computing security challenges

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    As cloud computing environments become complex, adversaries have become highly sophisticated and unpredictable. Moreover, they can easily increase attack power and persist longer before detection. Uncertain malicious actions, latent risks, Unobserved or Unobservable risks (UUURs) characterise this new threat domain. This thesis proposes prey-inspired survivability to address unpredictable security challenges borne out of UUURs. While survivability is a well-addressed phenomenon in non-extinct prey animals, applying prey survivability to cloud computing directly is challenging due to contradicting end goals. How to manage evolving survivability goals and requirements under contradicting environmental conditions adds to the challenges. To address these challenges, this thesis proposes a holistic taxonomy which integrate multiple and disparate perspectives of cloud security challenges. In addition, it proposes the TRIZ (Teorija Rezbenija Izobretatelskib Zadach) to derive prey-inspired solutions through resolving contradiction. First, it develops a 3-step process to facilitate interdomain transfer of concepts from nature to cloud. Moreover, TRIZ’s generic approach suggests specific solutions for cloud computing survivability. Then, the thesis presents the conceptual prey-inspired cloud computing survivability framework (Pi-CCSF), built upon TRIZ derived solutions. The framework run-time is pushed to the user-space to support evolving survivability design goals. Furthermore, a target-based decision-making technique (TBDM) is proposed to manage survivability decisions. To evaluate the prey-inspired survivability concept, Pi-CCSF simulator is developed and implemented. Evaluation results shows that escalating survivability actions improve the vitality of vulnerable and compromised virtual machines (VMs) by 5% and dramatically improve their overall survivability. Hypothesis testing conclusively supports the hypothesis that the escalation mechanisms can be applied to enhance the survivability of cloud computing systems. Numeric analysis of TBDM shows that by considering survivability preferences and attitudes (these directly impacts survivability actions), the TBDM method brings unpredictable survivability information closer to decision processes. This enables efficient execution of variable escalating survivability actions, which enables the Pi-CCSF’s decision system (DS) to focus upon decisions that achieve survivability outcomes under unpredictability imposed by UUUR

    False data injection attack detection in smart grid

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    Smart grid is a distributed and autonomous energy delivery infrastructure that constantly monitors the operational state of its overall network using smart techniques and state estimation. State estimation is a powerful technique that is used to determine the overall operational state of the system based on a limited set of measurements collected through metering systems. Cyber-attacks pose serious risks to a smart grid state estimation that can cause disruptions and power outages resulting in huge economical losses and are therefore a big concern to a reliable national grid operation. False data injection attacks (FDIAs), engineered on the basis of the knowledge of the network configuration, are difficult to detect using the traditional data detection mechanisms. These detection schemes have been found vulnerable and failed to detect these FDIAs. FDIAs specifically target the state data and can manipulate the state measurements in such a way that these false measurements appear real to the main control systems. This research work explores the possibility of FDIA detection using state estimation in a distributed and partitioned smart grid. In order to detect FDIAs we use measurements for residual-based testing which creates an objective function; and the probability of erroneous data is determined from this residual test. In this test, a preset threshold is determined based on the prior history of the state data. FDIA cases are simulated within a smart grid considering that the Chi-square detection state estimator fails in identifying such attacks. We compute the objective function using the standard weighted least problem and then test the objective function against the value in the Chi-square table. The gain matrix and the Jacobian matrix are computed. The state variables are computed in the form of a voltage magnitude. The state variables are computed after the inception of an attack to assess these state magnitude results. Different sizes of partitioning are used to improve the overall sensitivity of the Chi-square results. Our additional estimator is based on a Kalman estimation that consists of the state prediction and state correction steps. In the first step, it obtains the state and matrix covariance prediction, and in the second step, it calculates the Kalman gain and the state and matrix covariance update steps. The set of points is created for the state vector x at a time instant t. The initial vector and covariance matrix are based on a priori knowledge of the historical estimates. A set of sigma points is estimated by the state update function. Sigma points refer to the minimal set of sampling points that are selected and transformed using nonlinear function, and the new mean and the covariance are formed out of these transformed points. The idea behind this is that it is easier to compute a Gaussian distribution than an arbitrary nonlinear function. The filter gain, the mean and the covariance are used to estimate the next state. Our simulation results show that the combination of Kalman estimation and distributed state estimation improves the overall stability index and vulnerability assessment score of the smart grid. We built a stability index table for a smart grid based on the state estimates value after the inception of an FDIA. The vulnerability assessment score of the smart grid is based on common vulnerability scoring system (CVSS) and state estimates under the influence of an FDIA. The simulations are conducted in the MATPOWER program and different electrical bus systems such as IEEE 14, 30, 39, 118 and 300 are tested. All the contributions have been published in reputable journals and conferences.Doctor of Philosoph

    Hybrid Cloud Model Checking Using the Interaction Layer of HARMS for Ambient Intelligent Systems

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    Soon, humans will be co-living and taking advantage of the help of multi-agent systems in a broader way than the present. Such systems will involve machines or devices of any variety, including robots. These kind of solutions will adapt to the special needs of each individual. However, to the concern of this research effort, systems like the ones mentioned above might encounter situations that will not be seen before execution time. It is understood that there are two possible outcomes that could materialize; either keep working without corrective measures, which could lead to an entirely different end or completely stop working. Both results should be avoided, specially in cases where the end user will depend on a high level guidance provided by the system, such as in ambient intelligence applications. This dissertation worked towards two specific goals. First, to assure that the system will always work, independently of which of the agents performs the different tasks needed to accomplish a bigger objective. Second, to provide initial steps towards autonomous survivable systems which can change their future actions in order to achieve the original final goals. Therefore, the use of the third layer of the HARMS model was proposed to insure the indistinguishability of the actors accomplishing each task and sub-task without regard of the intrinsic complexity of the activity. Additionally, a framework was proposed using model checking methodology during run-time for providing possible solutions to issues encountered in execution time, as a part of the survivability feature of the systems final goals

    Resilient cooling pathway for extremely hot climates in southern Asia

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    Global warming is increasing extreme heat conditions, with existing energy efficiency policies showing trade-offs between mitigation objectives and adaptation to climate change. This research aims to identify the best resilient cooling solutions that should be promoted in the built environment of extremely hot countries to increase their heat resilience capacity. The impact of climate change on climate zones, cooling thermal demand (kWh/m2), and indoor heat discomfort hours (DHh, hours) in buildings is evaluated in different extremely hot dry climates of southern Asia through a parametric analysis for 2020, 2050 and 2080 under the A2 (medium–high) emission scenario. Then, cooling alternatives with higher synergies and trade-offs between energy efficiency (energy consumption) and resiliency to extreme heat (passive survivability) are highlighted. TRNSYS simulation software and ASHRAE criteria were used to characterise climate zones and calculate buildings' cooling needs and discomfort hours. Pakistan, in southern Asia, was selected as a hot reference region characterised by various climatic regions. The simulated scenario shows how Pakistan's extremely hot dry climate surface may increase from 36.9 % to 78.1 % by 2080, increasing annual cooling needs ranging from 20.56 to 66.96 kWh/m2 and indoor discomfort hours ranging from 423 to 1267 h. The results demonstrate how the passive solutions with higher synergies between energy savings and indoor comfort hours are, in decreasing order, ventilative cooling, reflective and ventilated roofs, shading in windows, and roof insulation. They can provide energy savings ranging from 13.1 to 7.1 kWh/m2 while reducing indoor discomfort by 320 to 131 h for extremely hot climates. Moreover, the sufficiency action related to higher thermostat settings, from 24 to 25 °C to 25–26.5 °C, was the most effective strategy to decrease energy demand. Additionally, there are trade-offs between energy-saving and heat resilience with highly insulated alternatives when ventilation is not adequately addressed. Despite increasing energy savings by 14.4 kWh/m2, discomfort hours are increased by 256 hours when air conditioning is unavailable, increasing building overheating by 5.1 %

    Modelling and Design of Resilient Networks under Challenges

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    Communication networks, in particular the Internet, face a variety of challenges that can disrupt our daily lives resulting in the loss of human lives and significant financial costs in the worst cases. We define challenges as external events that trigger faults that eventually result in service failures. Understanding these challenges accordingly is essential for improvement of the current networks and for designing Future Internet architectures. This dissertation presents a taxonomy of challenges that can help evaluate design choices for the current and Future Internet. Graph models to analyse critical infrastructures are examined and a multilevel graph model is developed to study interdependencies between different networks. Furthermore, graph-theoretic heuristic optimisation algorithms are developed. These heuristic algorithms add links to increase the resilience of networks in the least costly manner and they are computationally less expensive than an exhaustive search algorithm. The performance of networks under random failures, targeted attacks, and correlated area-based challenges are evaluated by the challenge simulation module that we developed. The GpENI Future Internet testbed is used to conduct experiments to evaluate the performance of the heuristic algorithms developed
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