Propose Vulnerability Metrics to Measure Network Secure using Attack Graph

Al-Araji, Zaid. J.; Ahmad, Sharifah Sakinah Syed; Abdullah, Raihana Syahirah

doi:10.14569/ijacsa.2021.0120508

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Therefore, the key of FCM is to determine the cluster center V and the membership degree matrix U. The objective function of FCM is shown in Equation (17).…”

Section: Discovery Of Key Vulnerable Nodes Based On Improved Fcmmentioning

confidence: 99%

“…To enhance the relevance between vulnerability assessment and network systems, many studies have conducted network vulnerability analysis based on attack graphs. They realized the association analysis of key vulnerabilities in network systems by studying attack graph construction techniques [13,14], node analysis techniques [15][16][17] and attack path analysis [18,19]. However, the attack graph does not have the ability of quantitative analysis.…”

Section: Introductionmentioning

confidence: 99%

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Key Vulnerable Nodes Discovery Based on Bayesian Attack Subgraphs and Improved Fuzzy C-Means Clustering

Xu,

Liu,

Sun

et al. 2024

Mathematics

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Aiming at the problem that the search efficiency of key vulnerable nodes in large-scale networks is not high and the consideration factors are not comprehensive enough, in order to improve the time and space efficiency of search and the accuracy of results, a key vulnerable node discovery method based on Bayesian attack subgraphs and improved fuzzy C-means clustering is proposed. Firstly, the attack graph is divided into Bayesian attack subgraphs, and the analysis results of the complete attack graph are quickly obtained by aggregating the information of the attack path analysis in the subgraph to improve the time and space efficiency. Then, the actual threat features of the vulnerability nodes are extracted from the analysis results, and the threat features of the vulnerability itself in the common vulnerability scoring standard are considered to form the clustering features together. Next, the optimal number of clusters is adaptively adjusted according to the variance idea, and fuzzy clustering is performed based on the extracted clustering features. Finally, the key vulnerable nodes are determined by setting the feature priority. Experiments show that the proposed method can optimize the time and space efficiency of analysis, and the fuzzy clustering considering multiple features can improve the accuracy of analysis results.

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“…Therefore, the key of FCM is to determine the cluster center V and the membership degree matrix U. The objective function of FCM is shown in Equation (17).…”

Section: Discovery Of Key Vulnerable Nodes Based On Improved Fcmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Key Vulnerable Nodes Discovery Based on Bayesian Attack Subgraphs and Improved Fuzzy C-Means Clustering

Xu,

Liu,

Sun

et al. 2024

Mathematics

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show abstract

Design of Adaptive Detection Algorithm for Mobile Social Network Security Vulnerability Based on Static Analysis

Qian,

Chen,

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

et al. 2022

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Geographically dispersed Fog Computing architecture ubiquitously connected to a range of heterogeneous nodes at the edge of the network can provide cooperative flexible, and variable computations, communications, and storage services. Several fog computing methods, models, and techniques have been used to solve cloud issues. The fuzzy theory has also been used in many aspects of fog computing. Objectives: This work presents a systematic literature review of the use of fuzzy theory in Fog Computing, highlighting the main practical motivations, classification types in research approaches, fuzzy methods used, popular evaluation tools, open issues, and future trends. Methods: The investigations were systematically performed using fuzzy theory in fog computing, and four databases which are ScienceDirect, Web of Science (WoS), Scopus, and IEEE Xplore Digital Library from 2015 to 2022, were used to analyse their performance evaluation, architecture, and applications. Results: 94 articles were selected based on fuzzy theory in fog computing using different methods, models, and techniques, based on the proposed exclusion and inclusion criteria. The results of the taxonomy were divided into five major classes: task and resource management, intrusion detection systems, trust management, and healthcare services. Discussion: Applications requiring real-time, low latency, and quick responses are well suited for fog computing. These studies show that resource sharing improves the fog computing architecture by delivering reduced latency, distributed processing, improved scalability, better security, fault tolerance, and privacy. Conclusion: The majority of the time, research areas on fuzzy theory in fog computing are crucially significant. We conclude that this review will enhance research capacity, thereby expanding and creating new research domains.

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Propose Vulnerability Metrics to Measure Network Secure using Attack Graph

Cited by 3 publications

References 22 publications

Key Vulnerable Nodes Discovery Based on Bayesian Attack Subgraphs and Improved Fuzzy C-Means Clustering

Key Vulnerable Nodes Discovery Based on Bayesian Attack Subgraphs and Improved Fuzzy C-Means Clustering

Design of Adaptive Detection Algorithm for Mobile Social Network Security Vulnerability Based on Static Analysis

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

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