Network security risk mitigation using Bayesian decision networks

Khosravi-Farmad, Masoud; Rezaee, Razieh; Harati, Ahad; Bafghi, Abbas Ghaemi

doi:10.1109/iccke.2014.6993444

Cited by 13 publications

(4 citation statements)

References 13 publications

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“…Augessy et al 30 proposed an approach to model ongoing and possible future attacks. Khosravi-Farhad et al 31,32 proposed the use of the Bayesian decision networks to measure the impact of vulnerabilities and to find minimum-cost security measures.…”

Section: Attack Graphs and Bayesian Network In Cybersecuritymentioning

confidence: 99%

Mission‐centric decision support in cybersecurity via Bayesian Privilege Attack Graph

Javorník

Husák

2022

Engineering Reports

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We present an approach to decision support in cybersecurity with respect to cyber threats and stakeholders' requirements. We approach situations in which cybersecurity experts need to take actions to mitigate the risks, such as temporarily putting an IT system out of operation, but need to consult them with other stakeholders. We propose a decision support system that uses a mission decomposition model representing the organization's functional and security requirements on its IT infrastructure. Based on the cybersecurity state assessment, that is, discovery of vulnerabilities and attacker's position, the decision support system calculates the resilience metrics for each IT infrastructure's configuration, that is, how likely are they to not be disrupted. The calculation is enabled by two novel formal models, Privilege-Exploit Attack Graph and Bayesian Privilege Attack Graph, which reduce complex attack graphs into a comprehensible bipartite graph. Moreover, they illustrate the impact of exploiting the vulnerabilities and attackers gaining the privileges. The system recommends the most resilient mission configurations that are comprehensible to both cybersecurity experts and non-technical stakeholders, who may then choose which configuration to apply. Our approach is illustrated in a case study of a real-world medical information system.

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Section: Attack Graphs and Bayesian Network In Cybersecuritymentioning

confidence: 99%

Mission‐centric decision support in cybersecurity via Bayesian Privilege Attack Graph

Javorník

Husák

2022

Engineering Reports

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“…In literature [46], Bayesian decision network is proposed to yield scalability, and integrate the risk assessment and outcome. The premise of using this model is to generate Bayesian attack graph of the network at first.…”

Section: The Nodes In the First Slice Of A 2tbn Do Not Have Any Parammentioning

confidence: 99%

“…Bayesian Decision Network [46]: This model represents a decision network which combines a Bayesian network with additional node types for actions and utilities. Bayesian decision network contains three types of nodes.…”

Section: The Nodes In the First Slice Of A 2tbn Do Not Have Any Parammentioning

confidence: 99%

Unknown Vulnerability Risk Assessment Based on Directed Graph Models: A Survey

2019

IEEE Access

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Nowadays, vulnerability attacks occur frequently. Due to the information asymmetry between attackers and defenders, vulnerabilities can be divided into known and unknown. Existing researches mainly focus on the risk assessment of known vulnerabilities. However, unknown vulnerabilities are more threatening and harder to detect. Therefore, unknown vulnerability risk assessment deserves the widespread attention. To model the exploit process, directed graph models are applied to vulnerability risk assessment. And security metrics are used to quantify the exploitability of vulnerabilities. In this paper, according to the data source of nodes, related works of unknown vulnerability risk assessment based on directed graph models are divided into two types. One is based on network-level data, the other is based on system-level data. The former is to visualize the network status, while the latter is to reflect the running process of the system. The concept and purpose of these directed graph models are given at first. Then, these models are analyzed from three aspects, including advantages, flaws and solutions. After that, challenges and solutions of unknown vulnerability risk assessment based on directed graph models are given. Meantime, security metrics for unknown vulnerability risk assessment based on directed graph models are summarized and classified. Finally, future work directions of unknown vulnerability risk assessment are discussed from the perspective of techniques and application trends. Consequently, this paper can fill in the lack of current survey on unknown vulnerability risk assessment based on directed graph models. INDEX TERMS Directed graph model, risk assessment, security metric, unknown vulnerability.

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“…Peltola and Kekolahti [30] use Bayesian networks and expert knowledge to perform a risk assessment of the Finnish TETRA PSS network, where several risk sources and controls affect the availability of the network. Bayesian networks have been also used in several research papers to model interdependent information security vulnerabilities as attack scenarios [13][14][15]17]. Sommestad et al [16] present a framework for analysing cyber security using Bayesian statistics and Mo et al [12] propose a quantitative model for evaluating a firm's cyber security readiness by use of Bayesian networks.…”

Section: Bayesian Networkmentioning

confidence: 99%

Information Security Risk Assessment of Smartphones using Bayesian Networks

Herland

Hämmáinen

Kekolahti

2016

JCSM

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This study comprises an information security risk assessment of smartphone use in Finland using Bayesian networks. The primary research method is a knowledge-based approach to build a causal Bayesian network model of information security risks and consequences. The risks, consequences, probabilities and impacts are identified from domain experts in a 2-stage interview process with 8 experts as well as from existing research and statistics. This information is then used to construct a Bayesian network model which lends itself to different use cases such as sensitivity and scenario analysis. The identified risks'probabilities follow a long tail wherein the most probable risks include unintentional data disclosure, failures of device or network, shoulder surfing or eavesdropping and loss or theft of device. Experts believe that almost 50% of users share more information to other parties through their smartphones than they acknowledge or would be willing to share. This study contains several implications for consumers as well as indicates a clear need for increasing security awareness among smartphone users.

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Network security risk mitigation using Bayesian decision networks

Cited by 13 publications

References 13 publications

Mission‐centric decision support in cybersecurity via Bayesian Privilege Attack Graph

Mission‐centric decision support in cybersecurity via Bayesian Privilege Attack Graph

Unknown Vulnerability Risk Assessment Based on Directed Graph Models: A Survey

Information Security Risk Assessment of Smartphones using Bayesian Networks

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