A Vulnerability Assessment Method in Industrial Internet of Things Based on Attack Graph and Maximum Flow

Wang, Huan; Chen, Zhanfang; Zhao, Jianping; Di, Xiaoqiang; Dan, Liu

doi:10.1109/access.2018.2805690

Cited by 101 publications

(59 citation statements)

References 23 publications

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“…(i) For the ideal attack scenario, the common metrics involve the most likely attack path [17][18][19], success probability of intention [10,16,18,19], and the number of attack paths [16][17][18][19], which are investigated in the acyclic graph. Furthermore, [18,19] further analyzed the mean, median, and mode of path lengths.…”

Section: Comparisons and Discussionmentioning

confidence: 99%

“…A modified version of Floyd-Warshall and Dijkstra algorithm is proposed by Sarraute et al [16] to compute the shortest attack path. To explore the fast and accurate solution of finding a potential vulnerable path in the network, Wang et al [17] used the augmented road algorithm to find optimal attack path within the global paths. To integrate the above security metrics, Idika et al [18] presented a suite of AG-based security metrics.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

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New Insights into Approaches to Evaluating Intention and Path for Network Multistep Attacks

Liu

Yang³

et al. 2018

Mathematical Problems in Engineering

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The attack graph (AG) is an abstraction technique that reveals the ways an attacker can use to leverage vulnerabilities in a given network to violate security policies. The analyses developed to extract security-relevant properties are referred to as AG-based security evaluations. In recent years, many evaluation approaches have been explored. However, they are generally limited to the attacker's "monotonicity" assumption, which needs further improvements to overcome the limitation. To address this issue, the stochastic mathematical model called absorbing Markov chain (AMC) is applied over the AG to give some new insights, namely, the expected success probability of attack intention (EAIP) and the expected attack path length (EAPL). Our evaluations provide the preferred mitigating target hosts and the vulnerabilities patching prioritization of middle hosts. Tests on the public datasets DARPA2000 and Defcon's CTF23 both verify that our evaluations are available and reliable.

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Section: Comparisons and Discussionmentioning

confidence: 99%

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

New Insights into Approaches to Evaluating Intention and Path for Network Multistep Attacks

Liu

Yang³

et al. 2018

Mathematical Problems in Engineering

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“…Due to the advancement in information and communication technologies (ICT), the internet of things (IoT) has emerged as a new area of research. The well-known applications of the GT in the IoT area are: (1) graph-based clustering for two-tier architecture [77], (2) risk assessment in internet of things environment [78], (3) data functional networks and a domain functional networks [79], (4) information diffusion in narrowband internet of things [80], (5) smart transportation using internet-of-things-generated big data [81], (6) vulnerability assessment method in an industrial internet of things (IIoTs) based on the attack graph and maximum flow [82], and information fusion of multiple sources to defend intentional attacks [83].…”

Section: Applications Of Graph Theory In Scheduling-related Problemsmentioning

confidence: 99%

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Majeed

Rauf

2020

Inventions

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Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.

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“…Light fidelity (LiFi) has been the interesting technology for the modern network in the coming years because it can offer the large demands of applications . The high capacity of information such as big data can be performed and implemented by the internet of things (IoT) . Moreover, the use of materials such as GaAsInP‐P, silicon, graphene, and chalcogenide for waveguides and devices can provide wider applications .…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4] The high capacity of information such as big data [5][6][7] can be performed and implemented by the internet of things (IoT). [8][9][10][11][12] Moreover, the use of materials such as GaAsInP-P, silicon, graphene, and chalcogenide for waveguides and devices can provide wider applications. [13][14][15][16][17][18] Recently, the use of the stacked layers of silicon-graphene-gold has shown the very interesting results, 19,20 where the conversion between electrical and optical signals can be made as following details.…”

Section: Introductionmentioning

confidence: 99%

Electro‐optic conversion circuit incorporating a fiber optic loop for light fidelity up‐down link use

Chaiwong

Bahadoran

Amiri

et al. 2018

Micro & Optical Tech Letters

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We propose the use of the electro‐optic circuit to integrate with the short distance fiber optic loop network, which can be used to form the light fidelity (LiFi) network platform within the building. The electro‐optic conversion circuit is formed by a plasmonic circuit. It is a passive device consisting of an embedded plasmonic island within the microring conjugate mirror (MCM), in which the applied input‐output information can be connected to the fiber optic loop. In this design, all sources of the information can be coupled to the plasmonic transducer and connected to the network. The transducer mobility and two different wavelengths of the input sources are demonstrated and all port signals plotted. The free spectral range and the full‐width at half maximum at the center wavelength of 1.30 μm are ~370.0 and ~0.62 nm, respectively. This is equal to the bandwidth of ~77 PetaHz, from which the output intensity power is ranged between 5 and 15 mW (μm)−2. The linear relationship between the driven mobility and the input power of ~0.75 (cm)2. (Vs)−1 W−1 is achieved at the drop port, with the gold layer length is 600 nm.

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A Vulnerability Assessment Method in Industrial Internet of Things Based on Attack Graph and Maximum Flow

Cited by 101 publications

References 23 publications

New Insights into Approaches to Evaluating Intention and Path for Network Multistep Attacks

New Insights into Approaches to Evaluating Intention and Path for Network Multistep Attacks

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Electro‐optic conversion circuit incorporating a fiber optic loop for light fidelity up‐down link use

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