1,721,418 research outputs found
Secure Static Content Delivery for CDN Using Blockchain Technology
No full textA Content Distribution Network (CDN) is a new kind of network to distribute services and content spatially relative to end-users, providing high availability and high performance. The Origin server uses several replicas to reach this goal, but trust issues are present between them and between servers and clients. In this work, we present a proof-of-concept for secure static content delivery (e.g., documents, images) by using Blockchain, a technology with the capability to ensure reliability and trust without a central authority. To test our proposal’s feasibility, we developed a system prototype on the Ethereum private network. The test shows the system’s goodness and the ability to create a new content distribution model over the Internet
A few-shot malware classification approach for unknown family recognition using malware feature visualization
No full textWith the ever-increasing threat of malware attacks, building an effective malware classifier to detect malware promptly is of utmost importance. Malware visualization approaches and deep learning techniques have proven effective in classifying sophisticated malware from benchmark datasets. A major problem with traditional deep learning classifier is the need to re-train the classifier when a new malware family emerges. In this paper, we propose few-shot classification techniques which allows us to classify malware based on a few instances and without the need for re-training the classifier for novel malware families. We also propose a novel malware visualization technique that can represent a malware binary as a 3-channel image. We experiment with two distinct few-shot learning architectures namely CSNN (Convolutional Siamese Neural Network) and Shallow-FS (Shallow Few-Shot). CSNN is more suitable when scarce data is available for training, otherwise Shallow-FS can be used to achieve better performance. Our architectures outperforms state of the art few-shot learning approaches and achieves high accuracy in traditional malware classification. Our experiments show our models’ ability to classify recent and novel malware families from just a few instances with high accuracy
Obfuscation detection in Android applications using deep learning
No full textMalware is often hidden in illegitimately cloned software. Android, with over two billions active devices, is one of the most affected platforms because code cloning is quite simple and there are several not controlled markets. Obfuscation is both a cause and a solution to this scenario: a cause because obfuscated malware is harder to detect, a solution because obfuscation of legitimate applications makes code cloning more difficult. A deeper understanding of the obfuscation techniques would lead to more effective and aware use. In the literature, there are few methods of obfuscation detection with limited accuracy. Manual reverse engineering is too time-consuming to achieve this purpose, we need faster and automated techniques. In this work, we propose several deep learning models that can detect and classify the presence of obfuscation in Android applications. In addition to classical ML methods, we leverage natural language processing or image recognition approaches, then with a hybrid model, we exploit the best of each approach. Tests over a large dataset, made using different obfuscation tools, showed improvements compared to previous obfuscation detection methods. We target four obfuscation classes: identifier renaming, string encryption, reflection and class encryption, achieving an average F-measure of 0.985
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
Detection of algorithmically-generated domains: An adversarial machine learning approach
No full textDomain name detection techniques are widely used to detect Algorithmically Generated Domain names (AGD) applied by Botnets. A major difficulty with these algorithms is to detect those generated names which are meaningful. In this way, Command and Control (C2) servers are detected. Machine learning techniques have been of great use to generalize the attributes of the meaningful names, generated algorithmically. To resist such techniques, the distribution of characters is used as a basis to generate meaningful domain names. Such techniques are called adversarial attacks attempting to fool machine learning methods. However, our experiments with more than 252757 samples show that in addition to character distribution of domain names, randomness property and pronounceability attributes are of great use to detect such meaningful names. Using these additional attributes, we have been able to identify malicious domain names with an accuracy of 98.19%
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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