The Experts below are selected from a list of 111 Experts worldwide ranked by ideXlab platform
Kishor S Trivedi - One of the best experts on this subject based on the ideXlab platform.
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GraMSec@CSF - Survivability Analysis of a Computer System Under an Advanced Persistent Threat Attack
Graphical Models for Security, 2016Co-Authors: Ricardo J Rodriguez, Xiaodan Li, Xiaolin Chang, 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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survivability analysis of a computer system under an advanced persistent threat attack
International Workshop on Graphical Models for Security, 2016Co-Authors: Ricardo J Rodriguez, Xiaodan Li, Xiaolin Chang, 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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DSN - Modeling and quantification of Security Attributes of software systems
Proceedings International Conference on Dependable Systems and Networks, 2002Co-Authors: Bharat B. Madan, K. Gogeva-popstojanova, Kalyanaraman Vaidyanathan, Kishor S TrivediAbstract:Quite often failures in network based services and server systems may not be accidental, but rather caused by deliberate Security intrusions. We would like such systems to either completely preclude the possibility of a Security intrusion or design them to be robust enough to continue functioning despite Security attacks. Not only is it important to prevent or tolerate Security intrusions, it is equally important to treat Security as a QoS Attribute at par with, if not more important than other QoS Attributes such as availability and performability. This paper deals with various issues related to quantifying the Security Attribute of an intrusion tolerant system, such as the SITAR system. A Security intrusion and the response of an intrusion tolerant system to the attack is modeled as a random process. This facilitates the use of stochastic modeling techniques to capture the attacker behavior as well as the system's response to a Security intrusion. This model is used to analyze and quantify the Security Attributes of the system. The Security quantification analysis is first carried out for steady-state behavior leading to measures like steady-state availability. By transforming this model to a model with absorbing states, we compute a Security measure called the "mean time (or effort) to Security failure" and also compute probabilities of Security failure due to violations of different Security Attributes.
Jeremy L Jacob - One of the best experts on this subject based on the ideXlab platform.
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cloud cover using user Security Attribute preferences and propagation analysis to prioritise threats to systems
European Intelligence and Security Informatics Conference, 2015Co-Authors: Mustafa Aydin, Jeremy L JacobAbstract:We present Cloud-COVER (Controls and Orderings for Vulnerabilities and ExposuRes), a cloud Security threat modelling tool. Cloud-COVER takes input from a user about their deployment, requiring information about the data, instances, connections, their properties, and the importance of various Security Attributes. This input is used to analyse the relevant threats, and the way they propagate through the system. They are then presented to the user, ordered according to the Security Attributes they have prioritised, along with the best countermeasures to secure against the dangers listed.
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EISIC - Cloud-COVER: Using User Security Attribute Preferences and Propagation Analysis to Prioritise Threats to Systems
2015 European Intelligence and Security Informatics Conference, 2015Co-Authors: Mustafa Aydin, Jeremy L JacobAbstract:We present Cloud-COVER (Controls and Orderings for Vulnerabilities and ExposuRes), a cloud Security threat modelling tool. Cloud-COVER takes input from a user about their deployment, requiring information about the data, instances, connections, their properties, and the importance of various Security Attributes. This input is used to analyse the relevant threats, and the way they propagate through the system. They are then presented to the user, ordered according to the Security Attributes they have prioritised, along with the best countermeasures to secure against the dangers listed.
Ricardo J Rodriguez - One of the best experts on this subject based on the ideXlab platform.
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GraMSec@CSF - Survivability Analysis of a Computer System Under an Advanced Persistent Threat Attack
Graphical Models for Security, 2016Co-Authors: Ricardo J Rodriguez, Xiaodan Li, Xiaolin Chang, 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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survivability analysis of a computer system under an advanced persistent threat attack
International Workshop on Graphical Models for Security, 2016Co-Authors: Ricardo J Rodriguez, Xiaodan Li, Xiaolin Chang, 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.
Shawn Butler - One of the best experts on this subject based on the ideXlab platform.
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Security Attribute evaluation method a cost benefit approach
International Conference on Software Engineering, 2002Co-Authors: Shawn ButlerAbstract:Conducting cost-benefit analyses of architectural Attributes such as Security has always been difficult, because the benefits are difficult to assess. Specialists usually make Security decisions, but program managers are left wondering whether their investment in Security is well spent. This paper summarizes the results of using a cost-benefit analysis method called SAEM to compare alternative Security designs in a financial and accounting information system. The case study presented in this paper starts with a multi-Attribute risk assessment that results in a prioritized list of risks. Security specialists estimate countermeasure benefits and how the organization's risks are reduced. Using SAEM, Security design alternatives are compared with the organization's current selection of Security technologies to see if a more cost-effective solution is possible. The goal of using SAEM is to help information-system stakeholders decide whether their Security investment is consistent with the expected risks.
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ICSE - Security Attribute evaluation method: a cost-benefit approach
Proceedings of the 24th international conference on Software engineering - ICSE '02, 2002Co-Authors: Shawn ButlerAbstract:Conducting cost-benefit analyses of architectural Attributes such as Security has always been difficult, because the benefits are difficult to assess. Specialists usually make Security decisions, but program managers are left wondering whether their investment in Security is well spent. This paper summarizes the results of using a cost-benefit analysis method called SAEM to compare alternative Security designs in a financial and accounting information system. The case study presented in this paper starts with a multi-Attribute risk assessment that results in a prioritized list of risks. Security specialists estimate countermeasure benefits and how the organization's risks are reduced. Using SAEM, Security design alternatives are compared with the organization's current selection of Security technologies to see if a more cost-effective solution is possible. The goal of using SAEM is to help information-system stakeholders decide whether their Security investment is consistent with the expected risks.
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incorporating nontechnical Attributes in multi Attribute analysis for Security
2002Co-Authors: Shawn Butler, Mary ShawAbstract:The most obvious considerations that affect an organization's choice of Security technologies are the threats the organization considers significant and the costeffectiveness of various Security technologies against those threats. In practice, however, the choice is also strongly driven by less tangible, more nontechnical, considerations such as ease of implementation and maintenance, fit with organizational culture, or intuitive appeal to Security personnel. We originally designed the Security Attribute Evaluation Method (SAEM) to respond to the former considerations. As SAEM has evolved, its multi-Attribute risk elicitation and sensitivity analysis also address the latter considerations by helping Security engineers make consistent judgements, focus on the highest points of leverage, and understand the implications of potential changes. As a result, the benefit of the method lies not only in its recommendations, but also in its ability to sharpen the Security engineers' understanding of their needs and options.
Mustafa Aydin - One of the best experts on this subject based on the ideXlab platform.
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cloud cover using user Security Attribute preferences and propagation analysis to prioritise threats to systems
European Intelligence and Security Informatics Conference, 2015Co-Authors: Mustafa Aydin, Jeremy L JacobAbstract:We present Cloud-COVER (Controls and Orderings for Vulnerabilities and ExposuRes), a cloud Security threat modelling tool. Cloud-COVER takes input from a user about their deployment, requiring information about the data, instances, connections, their properties, and the importance of various Security Attributes. This input is used to analyse the relevant threats, and the way they propagate through the system. They are then presented to the user, ordered according to the Security Attributes they have prioritised, along with the best countermeasures to secure against the dangers listed.
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EISIC - Cloud-COVER: Using User Security Attribute Preferences and Propagation Analysis to Prioritise Threats to Systems
2015 European Intelligence and Security Informatics Conference, 2015Co-Authors: Mustafa Aydin, Jeremy L JacobAbstract:We present Cloud-COVER (Controls and Orderings for Vulnerabilities and ExposuRes), a cloud Security threat modelling tool. Cloud-COVER takes input from a user about their deployment, requiring information about the data, instances, connections, their properties, and the importance of various Security Attributes. This input is used to analyse the relevant threats, and the way they propagate through the system. They are then presented to the user, ordered according to the Security Attributes they have prioritised, along with the best countermeasures to secure against the dangers listed.