1,721,094 research outputs found
Deep Transfer Learning for Intrusion Detection in Edge Computing Scenarios
Get PDFThe rapidly evolving landscape of cyber threats poses significant challenges to network security, particularly in decentralized environments such as edge computing. This paper proposes an enhanced Intrusion Detection System (IDS) architecture that integrates Transfer Learning (TL) to create a unified supermodel, enabling adaptability and scalability for detecting emerging threats across diverse datasets. The key contributions of this study include: (1) leveraging BERT-based feature extraction to enhance the semantic understanding of intrusion patterns, (2) employing an MLP classifier refined through TL to improve classification performance (3) addressing class imbalance using Synthetic Minority Over-Sampling Technique (SMOTE), and (4) optimizing model deployment by distributing the heaviest computational tasks for the creation of the unified supermodel across the edge nodes with the available capacity, thereby reducing latency and enabling accurate real-time threat detection by resource constrained IoT devices. The proposed TL-enabled supermodel is periodically updated and shared with the IoT devices, ensuring robust and adaptive security mechanisms without the need for extensive local training. Our experimental evaluation on CIC-IDS 2017 and NSL-KDD 2009 datasets demonstrates the effectiveness of the approach, achieving 99% accuracy, precision, recall, and F1-score. Our results highlight the scalability, efficiency, and real-world applicability of our IDS framework, reinforcing its role in fortifying network security within highly dynamic cyber threat landscapes
Empowering Network Security: BERT Transformer Learning Approach and MLP for Intrusion Detection in Imbalanced Network Traffic
Get PDFIntrusion detection systems (IDS) stand as formidable guardians in network security, playing a pivotal role in identifying and mitigating potential threats. As our digital landscape evolves, the imperative for robust intrusion detection mechanisms grows exponentially. The significance of IDS extends beyond preserving the integrity, confidentiality, and availability of network resources. In the dynamic realm of evolving cyber threats, IDS acts as the frontline defender—constantly monitoring network traffic to pinpoint suspicious activities and preemptively mitigate security breaches. In this paper, we study the effectiveness of combining the potency of the transformer-based model known as Bidirectional Encoder Representations from Transformers (BERT) in conjunction with the Synthetic Minority Over-sampling Technique (SMOTE) and a Multi-Layer Perceptron (MLP) to enhance the classification tasks in Machine Learning (ML) based IDS. We tested our approach on well-known and recent datasets, demonstrating that it is possible to obtain very high accuracy and robust performance in the detection of various attack types even when the datasets are affected by class imbalance. Beyond these results, our research introduces a novel perspective by seamlessly integrating the interpretability and context awareness of BERT with the efficient classification of MLP. This novel approach holds promise for advancing intrusion detection capabilities and contributing to the broader cybersecurity community
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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