Forthcoming and Online First Articles

International Journal of Networking and Security

International Journal of Networking and Security (IJNSec)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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(3 papers in press)

Regular Issues

  • Ensemble model for network representation learning   Order a copy of this article
    by Pranav Nerurkar, Madhav Chandane, Sunil Bhirud 
    Abstract: Multiple LSR techniques are available in the literature that could scale to networks with more than $10^3$ nodes. However, these techniques preserved one of the three key proximity measures. Hence, Ensemble models were proposed to preserve the three main proximity measures in a network simultaneously. The proposed ensemble models were applied to six network datasets with more than $10^3$ nodes. The computation complexity of proposed ensemble models was better than SBM and LVM. A key drawback observed was that the proposed ensemble models required all nodes in the network to be present during training. This feature meant that proposed models were transductive. They would not generalize to unseen nodes (non-inductive). The proposed models discarded attribute data associated with the network nodes. Alternatively, "deep encoder" based techniques could be investigated for situations where all network nodes cannot be available at the time of training. Such models could also be used where network nodes were associated with attributes.
    Keywords: Representation learning; network embedding; latent space; clustering; ensemble models.

  • Statistical-based Approach for Outlier Detection in Wireless Sensor Networks   Order a copy of this article
    by Chafiq Titouna, Farid Naït-Abdesselam, Ashfaq Khokhar 
    Abstract: Wireless Sensor Networks (WSNs) are sets of autonomous sensors dedicated to capture physical conditions of an area of interest. The collected data is converted to numerical data and transmitted to a central station for further processing. WSNs are also subject to several constraints, such as energy depletion, communication errors, and malicious attacks that impact negatively the quality of the transmitted data. Therefore, and in order to ensure a reliable and accurate data in this type of networks, it is crucial to endow them with mechanisms for detecting and cleaning erroneous and outlier instances of the collected data. To address this ever-increasing problem, we propose in this article a new algorithm called Outlier Detection Algorithm based on Mahalanobis Distance (ODA-MD). In this case, the Mahalanobis metric is used to calculate and compare the collected data in a fully distributed manner. The efficiency of the algorithm for detecting outliers lies also in its ability to perform this task online and in the presence of multivariate data among the data collected. Its functionalities have been evaluated through simulations using realistic datasets obtained from the Intel-Berkeley Research Laboratory (IBRL). The obtained results demonstrate the high effectiveness of the proposed algorithm in comparison to the literature in terms of several metrics.
    Keywords: Wireless sensor networks; outlier detection; Mahalanobis distance.

  • PageRank-based approaches: a component-aware survey   Order a copy of this article
    by José Devezas, Sérgio Nunes 
    Abstract: PageRank is a random walk-based approach that was introduced in 1997 as Google's ranking algorithm. Since then, it has been studied and extended for multiple other applications. From query-dependent ranking to community detection and image ranking, PageRank has proven to be flexible, robust and useful in diverse contexts. In this survey, we provide an overview on different types of PageRank applications, covering several interpretations, components and calculation approaches that can be tinkered with to manipulate and model PageRank-based approaches. We provide a normalised notation, focusing on delivering out-of-the-box, power-iteration-ready equations for each type of PageRank we cover. In many cases, we have rewritten the original formulas to provide a vectorised alternative. This way, we aim to provide a comprehensive guide to PageRank, that can be directly used by anyone with access to a linear algebra framework.
    Keywords: PageRank; survey; Markov chains; power iteration; link analysis; search; node ranking; graph-based applications.
    DOI: 10.1504/IJNSEC.2021.10039004
     

 

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