1,720,994 research outputs found
Privacy risk analysis and metrics in capturing and storing network traffic
No full textNetwork traffic analysis is a process of paramount importance to monitor network availability and operational activity, identify anomalies, maximize performance, find threats, and detect attacks. Due to this fact, in everyday work network managers need to capture, analyze and store a tremendous amount of data which can definitely be classified as 'Big Data'. On the other side, it is crucial to point out that the captured network traffic has significant privacy implications, in particular in the territorial scope of GDPR or other similar regulations because, according to GDPR, not only the payload but also the IP address of the sender and the receiver of packets have to be considered personal data. This paper deals with the privacy issues related to network traffic capture/processing/storage, the risks, and the associated mitigation techniques. As a conclusion of the work, a privacy risk analysis using PIA, together with the methodology developed by the French Data Protection Authority (CNIL) is discussed. The analysis performed highlights the effect of the use of some well-known anonymization and pseudonymization techniques on the severity and likelihood of risk
An Innovative Approach to Real-Time Concept Drift Detection in Network Security
No full textIn the realm of cybersecurity, the detection of Concept Drift holds the potential to improve the adaptability and effectiveness of security systems. In particular, Security Information and Event Management (SIEM) frameworks can benefit from real-time Drift Detection, enabling prompt detection of changing attack patterns, and consequent update of the detection criteria. To explore such an opportunity, the proposed approach extends a previously introduced SIEM solution with Concept Drift Detectors. An experimental evaluation is presented using two well-known unsupervised detectors on a merged dataset featuring Concept Drift, taking into consideration metrics such as Error Rate, Precision, Recall, and Window Average Error Rate. The results demonstrate that the integrated mechanism successfully identifies Concept Drift, triggering SIEM alerts and prompting timely updates to correlation rules. The experiment’s implications, limitations, and future directions are discussed, emphasizing the importance of continuous improvement in cybersecurity measures
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
Effective Rules for a Rule-Based SIEM System in Detecting DoS Attacks: An Association Rule Mining Approach
No full textIn today’s interconnected digital landscape, Security Information and Event Management (SIEM) systems play a vital role as the frontline defense against cyber threats, providing prompt detection of the most common cyber-threats. As Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks remain among the most challenging hazards for organizations worldwide, their quick and effective detection is a major concern. This research paper explores innovative methods to enhance the effectiveness of rule-based SIEM systems in detecting DoS and DDoS attacks. The SIEM rule sets are augmented leveraging Association Rule Mining (ARM), a data mining technique for uncovering hidden relationships within dataset’s features. By identifying and applying association rules to network traffic data, our methodology aims to strengthen SIEM rules, ultimately leading to more accurate DDoS attack detection
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
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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