1,720,956 research outputs found
Towards the usage of invariant-based app behavioral fingerprinting for the detection of obfuscated versions of known malware
App fingerprints can be used to verify whether two apps are the same, and are useful tools for malware detection because they can allow to recognize obfuscated versions of known malware. Fingerprinting an app on the base of static features is known to fail against obfuscation, as it is successful in hiding the static characteristics that reveal the malicious nature of an app. In this paper we propose a novel way to compute app fingerprints, which is based on behavioral features. The aim is to capture the semantics of the app, so that obfuscation results ineffective. The technique we introduce exploits invariants, found among pairs of metrics, collected during app execution, and produces a fingerprint consisting of the list of the correlation values of these pairs. We present an experimental evaluation carried out on a real Android device, whose obtained results support the methodology we propose, and show it can be a viable research direction to investigate further
NIRVANA: A Non-intrusive Black-Box Monitoring Framework for Rack-Level Fault Detection
Many organizations today still manage mid or large in-house data centers that require very expensive maintenance efforts, including fault detection. Common monitoring frameworks used to quickly detect faults are complex to deploy/maintain, expensive, and intrusive as they require the installation of probes on monitored hw/sw to collect raw data. Such intrusiveness can be problematic as it imposes installation/management overhead and may interfere with security/privacy policies. In this paper we introduce NIRVANA, a novel monitoring system for fault detection that works at rack-level and is (i) non-intrusive, i.e., it does not require the installation of software probes on the hosts to be monitored and (ii) black-box, i.e., agnostic with respect to monitored applications. At the core of our solution lies the observation that aggregated features that can be monitored at rack-level in a non-intrusive and black-box way, show predictable behaviors while the system works in both fault-free and faulty states, it is therefore possible to detect and identify faults by monitoring and analyzing any perturbations to these behaviors. An extensive experimental evaluation shows that non-intrusiveness does not significantly hamper the fault detection capabilities of the monitoring system, thus validating our approach
Going Beyond Counting First Authors in Author Co-citation Analysis
The 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
“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
Automatic Invariant Selection for Online Anomaly Detection
Invariants are stable relationships among system metrics expected to hold during normal operating conditions. The violation of such relationships can be used to detect anomalies at runtime. However, this approach does not scale to large systems, as the number of invariants quickly grows with the number of considered metrics. The resulting “background noise” for the invariant-based detection system hinders its effectiveness. In this paper we propose a general and automatic approach for identifying a subset of mined invariants that properly model system runtime behavior with a reduced amount of background noise. This translates into better overall performance (i.e., less false positives)
Appropriate Similarity Measures for Author Cocitation Analysis
We 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
We 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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