Global disassortative rewiring strategy for enhancing the robustness of scale-free networks against localized attack

Geng, Haoran; Cao, Meng; Guo, Chengwen; Peng, Chenglei; Du, Sidan; Yuan, Jie

doi:10.1103/physreve.103.022313

Cited by 6 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The structural properties of the network are not considered to establish a robust network. [13][14][15]20 S4: GCE, ASPL and ACC provide information to establish an efficient network.…”

Section: L4mentioning

confidence: 99%

“…Whereas, it is weak against malicious attacks. 8,9 Hence, it is a research challenge to design a resilient network against malicious attacks [10][11][12][13][14][15] along with topology optimization. [16][17][18] The SFNs is constructed using a preferential attachment process.…”

Section: Introductionmentioning

confidence: 99%

“…Whereas, it is weak against malicious attacks 8,9 . Hence, it is a research challenge to design a resilient network against malicious attacks 10–15 along with topology optimization 16–18 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards a robust scale‐free network in internet of health things against multiple attacks using an inter‐core based reconnection strategy

Abbas,

Javaid,

Alrajeh

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

SummaryWireless sensor networks (WSNs) have attained a great attraction of researchers in the recent years. In these networks, many structures are considered that have different properties. This article offers a unique approach, the inter‐core based reconnection strategy (ICRS), which is intended to improve the robustness of Scale‐Free Networks (SFNs) in the setting of wireless sensor networks (WSNs), with a special emphasis on the Internet of Health Things (IoHT) network. SFNs' vulnerabilities to malicious assaults while remaining resilient to random attacks. The proposed ICRS overcomes this issue by offering a novel reconnection approach that employs separate edges between network centers. Destructive assaults that have a significant impact on network connectivity, emphasizing the importance of a robust network that can resist a variety of attacks. ICRS is positioned as a solution that optimizes the network via reconnection techniques, changing it into an onion‐like structure with increased robustness. The simulation results depict that ICRS outperforms the existing algorithms in terms of robustness enhancement. The results show that ICRS performs 48%, 29%, 22%, and 16% better than Barabasi Albert (BA), Hill Climbing (HC), Simulated Annealing (SA), Random Edge Swap Mechanism (RESM), and Robustness Strategy (ROSE), respectively.

show abstract

“…The structural properties of the network are not considered to establish a robust network. [13][14][15]20 S4: GCE, ASPL and ACC provide information to establish an efficient network.…”

Section: L4mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards a robust scale‐free network in internet of health things against multiple attacks using an inter‐core based reconnection strategy

Abbas,

Javaid,

Alrajeh

et al. 2024

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The first studies in this field were able to identify a substantial difference in the response to attacks in dependence of the structure of the attacked network [1,2,3]: in fact, networks with exponential degree distribution, such as the Erdős-Rényi graph, are equally sensitive to random failures and targeted attacks, while scale-free networks are almost insensitive to random failures, being severely disrupted by targeted attacks. Random failures are generally understood to consist in random deletion of nodes [1,4,5,6,7], while targeted attacks are usually performed by removing nodes according to some centrality measure, such as degree centrality [1,3,4,5,6,7,8,9], or betweenness centrality [3,5,6,7,8,9], even though other types of attacks have been studied in the literature [3,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

A machine learning procedure to detect network attacks

Davide¹,

Cermelli²

2023

Preprint

View full text Add to dashboard Cite

The goal of this note is to assess whether simple machine learning algorithms can be used to determine whether and how a given network has been attacked. The procedure is based on the k-Nearest Neighbor and the Random Forest classification schemes, using both intact and attacked Erdős-Rényi, Barabasi-Albert and Watts-Strogatz networks to train the algorithm. The types of attacks we consider here are random failures and maximum-degree or maximum-betweenness node deletion. Each network is characterized by a list of 4 metrics, namely the normalized reciprocal maximum degree, the global clustering coefficient, the normalized average path length and the assortativity: a statistical analysis shows that this list of graph metrics is indeed significantly different in intact or damaged networks. We test the procedure by choosing both artificial and real networks, performing the attacks and applying the classification algorithms to the resulting graphs: the procedure discussed here turns out to be able to distinguish between intact networks and those attacked by the maximum-degree of maximum-betweenness deletions, but cannot detect random failures. Our results suggest that this approach may provide a basis for the analysis and detection of network attacks.

show abstract