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Maxim Finkelstein - One of the best experts on this subject based on the ideXlab platform.
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optimal mission abort policy for systems operating in a random environment
Risk Analysis, 2018Co-Authors: Gregory Levitin, Maxim FinkelsteinAbstract:Many real-world critical systems, e.g., aircrafts, manned space flight systems, and submarines, utilize mission aborts to enhance their Survivability. Specifically, a mission can be aborted when a certain malfunction condition is met and a rescue or recovery procedure is then initiated. For systems exposed to external impacts, the malfunctions are often caused by the consequences of these impacts. Traditional system reliability models typically cannot address a possibility of mission aborts. Therefore, in this article, we first develop the corresponding methodology for modeling and evaluation of the mission success probability and Survivability of systems experiencing both internal failures and external shocks. We consider a policy when a mission is aborted and a rescue procedure is activated upon occurrence of the mth shock. We demonstrate the tradeoff between the system Survivability and the mission success probability that should be balanced by the proper choice of the decision variable m. A detailed illustrative example of a mission performed by an unmanned aerial vehicle is presented.
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optimal mission abort policy for systems in a random environment with variable shock rate
Reliability Engineering & System Safety, 2018Co-Authors: Gregory Levitin, Maxim FinkelsteinAbstract:Abstract To enhance Survivability of many real-world critical systems (e.g., aircrafts and human space flight systems), mission abort procedures are often utilized in practice. Specifically, the mission objectives of these systems can be aborted in cases where a certain malfunction condition is met or some obstacles/ hazards occur. Then a rescue or recovery procedure is initiated to enhance Survivability. Traditional system reliability models typically cannot address the effects of mission aborts, and thus are not applicable to analyzing systems subject to mission abort requirements. In this paper, we first develop a methodology to model and evaluate mission success probability (MSP) and Survivability of systems experiencing both internal failures and external shocks. We consider a policy when a mission is aborted and a rescue procedure is activated if the m-th shock occurs before time ξ since the start of a mission. We demonstrate the tradeoff between system Survivability and MSP that should be balanced by the proper choice of the decision variables m and ξ. An illustrative example of a mission performed by an unmanned aerial vehicle is presented.
Gregory Levitin - One of the best experts on this subject based on the ideXlab platform.
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optimal mission abort policy for systems operating in a random environment
Risk Analysis, 2018Co-Authors: Gregory Levitin, Maxim FinkelsteinAbstract:Many real-world critical systems, e.g., aircrafts, manned space flight systems, and submarines, utilize mission aborts to enhance their Survivability. Specifically, a mission can be aborted when a certain malfunction condition is met and a rescue or recovery procedure is then initiated. For systems exposed to external impacts, the malfunctions are often caused by the consequences of these impacts. Traditional system reliability models typically cannot address a possibility of mission aborts. Therefore, in this article, we first develop the corresponding methodology for modeling and evaluation of the mission success probability and Survivability of systems experiencing both internal failures and external shocks. We consider a policy when a mission is aborted and a rescue procedure is activated upon occurrence of the mth shock. We demonstrate the tradeoff between the system Survivability and the mission success probability that should be balanced by the proper choice of the decision variable m. A detailed illustrative example of a mission performed by an unmanned aerial vehicle is presented.
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optimal mission abort policy for systems in a random environment with variable shock rate
Reliability Engineering & System Safety, 2018Co-Authors: Gregory Levitin, Maxim FinkelsteinAbstract:Abstract To enhance Survivability of many real-world critical systems (e.g., aircrafts and human space flight systems), mission abort procedures are often utilized in practice. Specifically, the mission objectives of these systems can be aborted in cases where a certain malfunction condition is met or some obstacles/ hazards occur. Then a rescue or recovery procedure is initiated to enhance Survivability. Traditional system reliability models typically cannot address the effects of mission aborts, and thus are not applicable to analyzing systems subject to mission abort requirements. In this paper, we first develop a methodology to model and evaluate mission success probability (MSP) and Survivability of systems experiencing both internal failures and external shocks. We consider a policy when a mission is aborted and a rescue procedure is activated if the m-th shock occurs before time ξ since the start of a mission. We demonstrate the tradeoff between system Survivability and MSP that should be balanced by the proper choice of the decision variables m and ξ. An illustrative example of a mission performed by an unmanned aerial vehicle is presented.
Kishor S. Trivedi - One of the best experts on this subject based on the ideXlab platform.
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Survivability model for security and dependability analysis of a vulnerable critical system
International Conference on Computer Communications and Networks, 2018Co-Authors: Xiaolin Chang, Ricardo J. Rodríguez, Shaohua Lv, Kishor S. TrivediAbstract:This paper aims to analyze transient security and dependability of a vulnerable critical system, under vulnerability-related attack and two reactive defense strategies, from a severe vulnerability announcement until the vulnerability is fully removed from the system. By severe, we mean that the vulnerability-based malware could cause significant damage to the infected system in terms of security and dependability while infecting more and more new vulnerable computer systems. We propose a Markov chain-based Survivability model for capturing the vulnerable critical system behaviors during the vulnerability elimination process. A high-level formalism based on Stochastic Reward Nets is applied to automatically generate and solve the Survivability model. Survivability metrics are defined to quantify system attributes. The proposed model and metrics not only enable us to quantitatively assess the system Survivability in terms of security risk and dependability, but also provide insights on the system investment decision. Numerical experiments are constructed to study the impact of key parameters on system security, dependability and profit.
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Survivability analysis of a computer system under an advanced persistent threat attack
International Workshop on Graphical Models for Security, 2016Co-Authors: Ricardo J. Rodríguez, Xiaolin Chang, Xiaodan Li, Kishor S. TrivediAbstract:Computer systems are potentially targeted by cybercriminals by means of specially crafted malicious software called Advanced Persistent Threats (APTs). As a consequence, any security attribute of the computer system may be compromised: disruption of service (availability), unauthorized data modification (integrity), or exfiltration of sensitive data (confidentiality). An APT starts with the exploitation of software vulnerability within the system. Thus, vulnerability mitigation strategies must be designed and deployed in a timely manner to reduce the window of exposure of vulnerable systems. In this paper, we evaluate the Survivability of a computer system under an APT attack using a Markov model. Generation and solution of the Markov model are facilitated by means of a high-level formalism based on stochastic Petri nets. Survivability metrics are defined to quantify security attributes of the system from the public announcement of a software vulnerability and during the system recovery. The proposed model and metrics not only enable us to quantitatively assess the system Survivability in terms of security attributes but also provide insights on the cost/revenue trade-offs of investment efforts in system recovery such as vulnerability mitigation strategies. Sensitivity analysis through numerical experiments is carried out to study the impact of key parameters on system secure Survivability.
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Reliability and Survivability of vehicular ad hoc networks: An analytical approach
Reliability Engineering and System Safety, 2016Co-Authors: Selvamuthu Dharmaraja, Resham Vinayak, Kishor S. Trivedi, Xiaomin MaAbstract:Vehicular ad hoc network (VANET) is a technology that facilitates communication between vehicles by creating a 'mobile Internet'. The system aims at ensuring road safety and achieving secured commutation. For this reason, reliability and Survivability of the network become matters of prime concern. Reliability and Survivability of the network is immensely dependent upon the hardware and channel availability. This paper, primarily focuses on the reliability and Survivability of VANET as a function of reliable hardware and channel availability. The reliability of the vehicles and the road side equipment is investigated using reliability block diagrams. The Survivability of the network, with respect to reliable hardware and channel availability is explored using Markov chains and Markov reward model. Considering that the communication between the vehicles may take place directly (i.e., vehicle-to-vehicle (V2V)) or through the road side equipment (i.e., vehicle-to-roadside (V2R)), the evaluation is ascertained for both V2V and V2R communications methodology, in terms of network reliability, connectivity and message lost due to unreliable hardware or channel availability. The technique of hierarchical modeling is adopted for the same. The results are also verified against simulation.
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network Survivability modeling
Computer Networks, 2009Co-Authors: Poul E Heegaard, Kishor S. TrivediAbstract:Critical services in a telecommunication network should be continuously provided even when undesirable events like sabotage, natural disasters, or network failures happen. It is essential to provide virtual connections between peering nodes with certain performance guarantees such as minimum throughput, maximum delay or loss. The design, construction and management of virtual connections, network infrastructures and service platforms aim at meeting such requirements. In this paper we consider the network's ability to survive major and minor failures in network infrastructure and service platforms that are caused by undesired events that might be external or internal. Survive means that the services provided comply with the requirement also in presence of failures. The network Survivability is quantified as defined by the ANSI T1A1.2 committee which is the transient performance from the instant an undesirable event occurs until steady state with an acceptable performance level is attained. The assessment of the Survivability of a network with virtual connections exposed to link or node failures is addressed in this paper. We have developed both simulation and analytic models to cross-validate our assumptions. In order to avoid state space explosion while addressing large networks we decompose our models first in space by studying the nodes independently and then in time by decoupling our analytic performance and recovery models which gives us a closed form solution. The modeling approaches are applied to both small and real-sized network examples. Three different scenarios have been defined, including single link failure, hurricane disaster, and instabilities in a large block of the system (transient common failure). The results show very good correspondence between the transient loss and delay performance in our simulations and in the analytic approximations.
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network Survivability performance evaluation a quantitative approach with applications in wireless ad hoc networks
Modeling Analysis and Simulation of Wireless and Mobile Systems, 2002Co-Authors: Dongyan Chen, Sachin Garg, Kishor S. TrivediAbstract:Network Survivability reflects the ability of a network to continue to function during and after failures. Our purpose in this paper is to propose a quantitative approach to evaluate network Survivability. We perceive the network Survivability as a composite measure consisting of both network failure duration and failure impact on the network. A wireless ad-hoc network is analyzed as an example, and the excess packet loss due to failures (ELF) is taken as the Survivability performance measure. To obtain ELF, we adopt a two phase approach consisting of the steady-state availability analysis and transient performance analysis. Assuming Markovian property for the system, this measure is obtained by solving a set of Markov models. By utilizing other analysis paradigms, our approach in this paper may also be applied to study the Survivability performance of more complex systems.
Franklin Webber - One of the best experts on this subject based on the ideXlab platform.
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Survivability architecture of a mission critical system the dpasa example
Annual Computer Security Applications Conference, 2005Co-Authors: J Chong, M Atigetchi, Paul Rubel, Franklin WebberAbstract:Many techniques and mechanisms exist today, some COTS, others less mature research products that can be used to deflect, detect, or even recover from specific types of cyber attacks. None of them individually is sufficient to provide an all around defense for a mission critical distributed system. A mission critical system must operate despite sustained attacks throughout the mission cycle, which in the case of military systems, can range from hours to days. A comprehensive Survivability architecture, where individual security tools and defense mechanisms are used as building blocks, is required to achieve this level of Survivability. We have recently designed a Survivability architecture, which combined elements of protection, detection, and adaptive reaction; and applied it to a DoD information system. The resulting defense-enabled system was first evaluated internally, and then deployed for external Red Team exercise. In this paper we describe the Survivability architecture of the system, and explain the rationale that motivated the design
Jack Yang - One of the best experts on this subject based on the ideXlab platform.
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breast cancer data analysis for Survivability studies and prediction
Computer Methods and Programs in Biomedicine, 2018Co-Authors: Nagesh Shukla, Markus Hagenbuchner, Khin Than Win, Jack YangAbstract:Abstract Background Breast cancer is the most common cancer affecting females worldwide. Breast cancer Survivability prediction is challenging and a complex research task. Existing approaches engage statistical methods or supervised machine learning to assess/predict the survival prospects of patients. Objective The main objectives of this paper is to develop a robust data analytical model which can assist in (i) a better understanding of breast cancer Survivability in presence of missing data, (ii) providing better insights into factors associated with patient Survivability, and (iii) establishing cohorts of patients that share similar properties. Methods Unsupervised data mining methods viz. the self-organising map (SOM) and density-based spatial clustering of applications with noise (DBSCAN) is used to create patient cohort clusters. These clusters, with associated patterns, were used to train multilayer perceptron (MLP) model for improved patient Survivability analysis. A large dataset available from SEER program is used in this study to identify patterns associated with the Survivability of breast cancer patients. Information gain was computed for the purpose of variable selection. All of these methods are data-driven and require little (if any) input from users or experts. Results SOM consolidated patients into cohorts of patients with similar properties. From this, DBSCAN identified and extracted nine cohorts (clusters). It is found that patients in each of the nine clusters have different Survivability time. The separation of patients into clusters improved the overall survival prediction accuracy based on MLP and revealed intricate conditions that affect the accuracy of a prediction. Conclusions A new, entirely data driven approach based on unsupervised learning methods improves understanding and helps identify patterns associated with the Survivability of patient. The results of the analysis can be used to segment the historical patient data into clusters or subsets, which share common variable values and Survivability. The Survivability prediction accuracy of a MLP is improved by using identified patient cohorts as opposed to using raw historical data. Analysis of variable values in each cohort provide better insights into Survivability of a particular subgroup of breast cancer patients.
