Vulnerability of complex networks under three-level-tree attacks

Hao, Yaohui; Han, Jing; Lin, Yi; Liu, Lin

doi:10.1016/j.physa.2016.06.130

Cited by 24 publications

(11 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, the relation between the topological characteristics of the ground-truth network and the robustness of the network attack strategies should be investigated in the future research. Moreover, new attack strategies [2, 4] have been proposed since CI, and analyzing the robustness of these strategies is also important future work. Third, to understand the effectiveness of network attack strategies for the network immunization problem, more detailed investigations are still necessary.…”

Section: Resultsmentioning

confidence: 99%

“…The network attack problem has received much attention, and several network attack strategies have been proposed [1–4]. The network attack problem involves finding a (small) set of nodes whose removal fragments the network [1].…”

Section: Introductionmentioning

confidence: 99%

“…While most existing network attack strategies assume complete knowledge about the network [1, 2, 4], in reality it is difficult to obtain the complete structure of large-scale complex networks [5, 13–16]. Because real-world networks such as social networks and the Internet are huge, obtaining the entire network structure is prohibitive, and typically only part of the network structure is available from network sampling [17–19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robustness of network attack strategies against node sampling and link errors

Otsuka

Tsugawa

2019

PLoS ONE

View full text Add to dashboard Cite

We investigate the effectiveness of network attack strategies when the attacker has only imperfect information about the network. While most existing network attack strategies assume complete knowledge about the network, in reality it is difficult to obtain the complete structure of a large-scale complex network. This paper considers two scenarios in which the available network information is imperfect. In one scenario, the network contains link errors (i.e., missing and false links) due to measurement errors, and in the other scenario the target network is so large that only part of the network structure is available from network sampling. Through extensive simulations, we show that particularly in a network with highly skewed degree distribution, network attack strategies are robust against link errors. Even if the network contains 30% false links and missing links, the strategies are just as effective as when the complete network is available. We also show that the attack strategies are far less effective when the network is obtained from random sampling, whereas the detrimental effects of network sampling on network attack strategies are small when using biased sampling strategies such as breadth-first search, depth-first search, and sample edge counts. Moreover, the effectiveness of network attack strategies is examined in the context of network immunization, and the implications of the results are discussed.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robustness of network attack strategies against node sampling and link errors

Otsuka

Tsugawa

2019

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Previous studies show that a large number of real systems such as the World Wide Web, the Internet, power grids, and airline networks, etc. are with notable heterogeneity [14], which means that the role of nodes in network structure and function varies greatly. These networks have a higher tolerance to random failures but are sensitive to intentional attacks.…”

Section: Introductionmentioning

confidence: 99%

Critical Nodes Identification in Complex Networks

Yang

2020

Symmetry

View full text Add to dashboard Cite

Critical nodes identification in complex networks is significance for studying the survivability and robustness of networks. The previous studies on structural hole theory uncovered that structural holes are gaps between a group of indirectly connected nodes and intermediaries that fill the holes and serve as brokers for information exchange. In this paper, we leverage the property of structural hole to design a heuristic algorithm based on local information of the network topology to identify node importance in undirected and unweighted network, whose adjacency matrix is symmetric. In the algorithm, a node with a larger degree and greater number of structural holes associated with it, achieves a higher importance ranking. Six real networks are used as test data. The experimental results show that the proposed method not only has low computational complexity, but also outperforms degree centrality, k-shell method, mapping entropy centrality, the collective influence algorithm, DDN algorithm that based on node degree and their neighbors, and random ranking method in identifying node importance for network connectivity in complex networks.

show abstract

“…In order to obtain the security status of the network and to predict its trend, the researchers first studied attack threats [1,2], network vulnerability [3,4], and other related aspects. In summary, the researches on these aspects are relatively mature.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Method for Network Security Situation Based on Attack Prediction

Zhang

Liu

et al. 2017

Security and Communication Networks

View full text Add to dashboard Cite

Multistep attack prediction and security situation awareness are two big challenges for network administrators because future is generally unknown. In recent years, many investigations have been made. However, they are not sufficient. To improve the comprehensiveness of prediction, in this paper, we quantitatively convert attack threat into security situation. Actually, two algorithms are proposed, namely, attack prediction algorithm using dynamic Bayesian attack graph and security situation quantification algorithm based on attack prediction. The first algorithm aims to provide more abundant information of future attack behaviors by simulating incremental network penetration. Through timely evaluating the attack capacity of intruder and defense strategies of defender, the likely attack goal, path, and probability and time-cost are predicted dynamically along with the ongoing security events. Furthermore, in combination with the common vulnerability scoring system (CVSS) metric and network assets information, the second algorithm quantifies the concealed attack threat into the surfaced security risk from two levels: host and network. Examples show that our method is feasible and flexible for the attack-defense adversarial network environment, which benefits the administrator to infer the security situation in advance and prerepair the critical compromised hosts to maintain normal network communication.

show abstract

Vulnerability of complex networks under three-level-tree attacks

Cited by 24 publications

References 21 publications

Robustness of network attack strategies against node sampling and link errors

Robustness of network attack strategies against node sampling and link errors

Critical Nodes Identification in Complex Networks

Quantitative Method for Network Security Situation Based on Attack Prediction

Contact Info

Product

Resources

About