Aggregating Centrality Rankings: A Novel Approach to Detect Critical Infrastructure Vulnerabilities

Oliva, Gabriele; Amideo, Annunziata Esposito; Starita, Stefano; Setola, Roberto; Scaparra, Maria Paola

doi:10.1007/978-3-030-37670-3_5

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Another centrality-based method was proposed by Kay et al [9] to identify critical assets using a PageRank-based criticality score in the infrastructure network representing resource supplies between assets. On the contrary, Oliva et al [15] applied different perspectives from the degree, betweenness, eigenvector, and PageRank centrality measures to identify the most influential vertices in a network. However, in our opinion, it is worth adjusting the computation of centrality measures instead of adjusting their input or combining them.…”

Section: Network Centrality Measuresmentioning

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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Section: Network Centrality Measuresmentioning

confidence: 99%

Cyber Key Terrain Identification Using Adjusted PageRank Centrality

Sadlek,

Čeleda

2024

IFIP Advances in Information and Communication Technology

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“…From a network topology perspective, node criticality is related to the location of the node in the network structure (Oliva et al, 2019). The most widely used analyses for measuring the criticality of nodes have been based on centrality measures (Rodrigues, 2019).…”

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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“…However, the adoption of a unique metric or indicator about the criticality rate of each node of the system is quite unrealistic due to the complex nature of the infrastructures. Two approaches able to consider multiple metrics with the aim to compute a final aggregated criticality holistic indicator are presented in [12,13]. The proposed approaches take into account multiple indicators based on multiple data source (topology data, field-related data, expert evaluations, etc.)…”

Section: Related Workmentioning

confidence: 99%

A Strategy to Improve Infrastructure Survivability via Prioritizing Critical Nodes Protection

Faramondi¹,

Assenza²,

Oliva³

et al. 2021

Issues on Risk Analysis for Critical Infrastructure Protection

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From an engineering point of view, the survivability of a system is defined as its ability to continue to operate despite a natural or human-made disturbance; for example a serious mechanical fault, a human error, or a malicious cyber or physical attack. In the context of critical infrastructures, due to their relevance for the public wellness, it is mandatory to improve the robustness of such systems in order to ensure the availability of essential services such as the distribution of water, gas and electrical power. Nowadays, due to the increasing number of cyber incidents, the definition of protection strategies, able to improve the survivability level of this infrastructure, is at the heart of the scientific debate. In this chapter we propose a procedure based on three steps aimed at improving infrastructure survivability. In the first stage we propose some approaches to identify the criticality degree of each subsystem composing the infrastructure, in the second stage we propose a method to aggregate multiple criticality evaluations performed by subject matter experts by providing a unique holistic indicator. Finally, on the basis of such indicator, we propose a protection strategy to improve the robustness of the entire system.

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Aggregating Centrality Rankings: A Novel Approach to Detect Critical Infrastructure Vulnerabilities

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 4 publications

References 23 publications

Cyber Key Terrain Identification Using Adjusted PageRank Centrality

Cyber Key Terrain Identification Using Adjusted PageRank Centrality

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

A Strategy to Improve Infrastructure Survivability via Prioritizing Critical Nodes Protection

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