Using Centrality Measures in Dependency Risk Graphs for Efficient Risk Mitigation

Stergiopoulos, George; Theocharidou, Marianthi; Kotzanikolaou, Panayiotis; Gritzalis, Dimitris

doi:10.1007/978-3-319-26567-4_18

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…The decline of package centrality could be a symptom that better alternatives have emerged or a shift happened in the community interest. In the scientific literature, centrality has been used in many disciplines such as social networks to identify central node of a network (e.g., [18,20,19,21]) and software engineering (e.g., [6,35]). If the approach can aptly capture packages in decline, it can be embedded in package search engines, such as npms, to increase developers' awareness of the community interest and help them make a better-informed decision to select or reevaluate their package dependencies.…”

Section: Resultsmentioning

confidence: 99%

“…By definition, centrality is a measure of the prominence or importance of a node in a social network [16]. Centrality has been used in many fields e.g., in finance to measure the salability of banks in financial networks [17], in electrical engineering to rank the importance of components in network infrastructures [18,19], and other fields including computer science and software engineering [20,21]. In our context, the centrality allows us to rank the packages based on the popularity/importance of packages that depend on them.…”

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confidence: 99%

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Towards Using Package Centrality Trend to Identify Packages in Decline

Mujahid,

Costa,

Abdalkareem

et al. 2021

Preprint

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Due to its increasing complexity, today's software systems are frequently built by leveraging reusable code in the form of libraries and packages. Software ecosystems (e.g., npm) are the primary enablers of this code reuse, providing developers with a platform to share their own and use others' code. These ecosystems evolve rapidly: developers add new packages every day to solve new problems or provide alternative solutions, causing obsolete packages to decline in their importance to the community. Developers should avoid depending on packages in decline, as these packages are reused less over time and may become less frequently maintained. However, current popularity metrics (e.g., Stars, and Downloads) are not fit to provide this information to developers because their semantics do not aptly capture shifts in the community interest.In this paper, we propose a scalable approach that uses the package's centrality in the ecosystem to identify packages in decline. We evaluate our approach with the npm ecosystem and show that the trends of centrality over time can correctly distinguish packages in decline with an ROC-AUC of 0.9. The approach can capture 87% of the packages in decline, on average 18 months before the trend is shown in currently used package popularity metrics. We implement this approach in a tool that can be used to augment npms metrics and help developers avoid packages in decline when reusing packages from npm.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Towards Using Package Centrality Trend to Identify Packages in Decline

Mujahid,

Costa,

Abdalkareem

et al. 2021

Preprint

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“…A bridge failure affects the traffic served by it (and this is why traffic is considered in our criticality evaluation), connected roads, other bridges and municipalities. Numerous centrality measures, such as variations of "betweenness" or "degree index", can be tailored to assess bridge criticality (Jafino et al, 2020;Stergiopoulos et al, 2015). Amongst them, we have selected "closeness centrality" and "vitality" to form the novel measure of ABA-closeness vitality for the following reasons.…”

Section: Aba-closeness Vitalitymentioning

confidence: 99%

Condition and criticality-based predictive maintenance prioritisation for networks of bridges

Hadjidemetriou

Herrera

Parlikad

2021

Structure and Infrastructure Engineering

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“…For example, the PageRank measure by Brin and Page [2] estimates the importance of web pages based on references by other web pages and the importance of these referencing web pages. Researchers also claim that the most critical network paths contain assets with high centrality [22], and high vertex centrality indicates asset criticality [9]. However, these methods can only be used to determine criticality plausibly if they consider computer network specifics, i.e., differences among vertices and edges from the network [10].…”

Section: Introductionmentioning

confidence: 99%

Cyber Key Terrain Identification Using Adjusted PageRank Centrality

Sadlek,

Čeleda

2024

IFIP Advances in Information and Communication Technology

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The cyber terrain contains devices, network services, cyber personas, and other network entities involved in network operations. Designing a method that automatically identifies key network entities to network operations is challenging. However, such a method is essential for determining which cyber assets should the cyber defense focus on. In this paper, we propose an approach for the classification of IP addresses belonging to cyber key terrain according to their network position using the PageRank centrality computation adjusted by machine learning. We used hill climbing and random walk algorithms to distinguish Page-Rank's damping factors based on source and destination ports captured in IP flows. The one-time learning phase on a static data sample allows near-real-time stream-based classification of key hosts from IP flow data in operational conditions without maintaining a complete network graph. We evaluated the approach on a dataset from a cyber defense exercise and on data from the campus network. The results show that cyber key terrain identification using the adjusted computation of centrality is more precise than its original version.

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Using Centrality Measures in Dependency Risk Graphs for Efficient Risk Mitigation

Cited by 12 publications

References 18 publications

Towards Using Package Centrality Trend to Identify Packages in Decline

Towards Using Package Centrality Trend to Identify Packages in Decline

Condition and criticality-based predictive maintenance prioritisation for networks of bridges

Cyber Key Terrain Identification Using Adjusted PageRank Centrality

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