On the Computation of Centrality Metrics for Network Security in Mesh Networks

Maccari, Leonardo; Nguyen, Quynh Nhu Quang; Cigno, Renato Lo

doi:10.1109/glocom.2016.7842049

Cited by 19 publications

(15 citation statements)

References 24 publications

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“…Likewise, centrality measures are studied in the context of network security. For instance, they have been used to configure distributed firewalls [25] and to identify critical nodes in which intrusion detection and firewalling is most suitable [26]. In addition, they were applied to build cloud-based filters and footprints of traffic inspection algorithms at global scrubbing centers [27].…”

Section: Centrality In Computer Networkmentioning

confidence: 99%

Machine Learning in Network Centrality Measures

2018

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Complex networks are ubiquitous to several Computer Science domains. Centrality measures are an important analysis mechanism to uncover vital elements of complex networks. However, these metrics have high computational costs and requirements that hinder their applications in large real-world networks. In this tutorial, we explain how the use of neural network learning algorithms can render the application of the metrics in complex networks of arbitrary size. Moreover, the tutorial describes how to identify the best configuration for neural network training and learning such for tasks, besides presenting an easy way to generate and acquire training data. We do so by means of a general methodology, using complex network models adaptable to any application. We show that a regression model generated by the neural network successfully approximates the metric values and therefore are a robust, effective alternative in real-world applications. The methodology and proposed machine learning model use only a fraction of time with respect to other approximation algorithms, which is crucial in complex network applications.

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Section: Centrality In Computer Networkmentioning

confidence: 99%

Machine Learning in Network Centrality Measures

2018

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“…Conversely, a new node can be added to G' to perform collusion between two sub-groups in a design. We can calculate BC of a node as follows [18]:…”

Section: Definitions and Terminologymentioning

confidence: 99%

Socio-network Analysis of RTL Designs for Hardware Trojan Localization

Islam

Mime

Asaduzzaman

et al. 2019

2019 22nd International Conference on Computer and Information Technology (ICCIT)

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The recent surge in hardware security is significant due to offshoring the proprietary Intellectual property (IP). One distinct dimension of the disruptive threat is malicious logic insertion, also known as Hardware Trojan (HT). HT subverts the normal operations of a device stealthily. The diversity in HTs activation mechanisms and their location in design brings no catch-all detection techniques. In this paper, we propose to leverage principle features of social network analysis to security analysis of Register Transfer Level (RTL) designs against HT. The approach is based on investigating design properties, and it extends the current detection techniques. In particular, we perform both node-and graph-level analysis to determine the direct and indirect interactions between nets in a design. This technique helps not only in finding vulnerable nets that can act as HT triggering signals but also their interactions to influence a particular net to act as HT payload signal. We experiment the technique on 420 combinational HT instances, and on average, we can detect both triggering and payload signals with accuracy up to 97.37%.

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“…Leveraging Puzis' heuristic it is possible to show that, even using a very low-power device, the exact computation can be carried out for networks made of hundreds of nodes in just a few seconds [5]. We exploit this result to further study how sensitive the betweenness metric is to topology changes in…”

Section: A Centrality In Networkmentioning

confidence: 99%

“…The first step to design Prince was to implement Puzis' heuristic and test its performance on real hardware. Preliminary results have shown that for a network made of 236 nodes, a low-cost wireless router (an Ubiquiti M5 wireless router, equipped with a MIPS 390MHz processor and 32M of RAM) requires 7 seconds to compute the centrality of each node using Puzis' heuristic, less than 1 3 of the time required by Brandes' algorithm [5].…”

Section: A Implementing Puzis' Heuristicmentioning

confidence: 99%

“…Pop-Routing requires every node to compute the betweenness centrality of every other node in the network. The complexity of this computation using the state-of-the-art algorithm is polynomial with the number of nodes [4], but still it can be hardly performed in real-time in resource-limited devices such as wireless routers [5]. In the second part of the paper (Secs.…”

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confidence: 99%

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Improving Routing Convergence With Centrality: Theory and Implementation of Pop-Routing

Maccari

Cigno

2018

IEEE/ACM Trans. Networking

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One of the key features of a routing protocol is its ability to recover from link or node failures, recomputing routes efficiently without creating temporary loops. Link-State protocols perform better than Distance-Vector ones and they are often presented as ideal from this perspective. Indeed, in real conditions there is always a trade-off between the overhead due to the periodic generation of control messages and route convergence time. This work formalizes the problem of the choice of timers for control message generation as an optimization problem that minimizes the route convergence time constrained to a constant signaling overhead. The solution requires the knowledge of nodes' centrality in the topology and can be obtained with a computational complexity low enough to allow on-line computation of the timers. Results on both synthetic and real topologies show a significant shrinkage of the transient duration with the consequent performance gain in terms of reduced number of unreachable destinations and routing loops that lead to traffic loss. Next, we present the extension of OLSRv2 with our proposal, named Pop-Routing, and discuss its performance and the stability of centrality metrics in three large-scale real wireless mesh networks. This exhaustive analysis on traces of the topology evolution of real networks for one entire week show that Pop-Routing outperforms the non-enhanced protocol in every situation, even when it runs with sub-optimal timers due to centrality computation on stale information. This situation must be taken into account, as, albeit computationally lightweight, the optimization cannot run in real-time on wireless routers.

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On the Computation of Centrality Metrics for Network Security in Mesh Networks

Cited by 19 publications

References 24 publications

Machine Learning in Network Centrality Measures

Machine Learning in Network Centrality Measures

Socio-network Analysis of RTL Designs for Hardware Trojan Localization

Improving Routing Convergence With Centrality: Theory and Implementation of Pop-Routing

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