Understanding DNS Query Composition at B-Root

The Domain Name System (DNS) is part of critical internet infrastructure, as DNS is invoked whenever a remote server is accessed (an URL is visited, an API request is made, etc.) by any application. DNS queries are served in hierarchical manner, with most queries served locally from cached data, and a small fraction propagating to the top of the hierarchy - DNS root name servers. Our research aims to provide a comprehensive, longitudinal characterization of DNS queries received at B-Root over ten years. We sampled and analyzed a 28-billion-query large dataset from the ten annual Day in the Life of the Internet (DITL) experiments from 2013 through 2022. We sought to identify and quantify unexpected DNS queries, establish longitudinal trends, and compare our findings with published results of others. We found that unexpected query traffic increased from 39.57% in 2013 to 67.91% in 2022, with 36.55% of queries being priming queries. We also observed growth and decline of Chromium-initiated, random DNS queries. Finally, we analyzed the largest DNS query senders and established that most of their traffic consists of unexpected queries.Comment: 20 pages with 18 figures and 1 table. Published and presented at the 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT

Aloe vera: A Multipurpose Healer and Bacterial Eradicator

The Aloe vera plant is a succulent known for its rich content in vitamins and minerals, thus gaining popularity over the years in healthcare products. With advancements in alternative medicine, it has been recently found useful in dentistry due to properties such as anti-inflammatory, antioxidant and antimicrobial actions that contribute to wound healing. The purpose of this study was to examine and discover how Aloe vera can be used as an alternative therapy in the dental field. The PubMed, Google Scholar and Dentistry & Oral Sciences (DOSS) databases were utilized to find current scientific evidence on the effects of Aloe vera. Relevant articles were summarized to write a review of findings. In this study, 21 articles published from 2015 to present were reviewed. From the studies, there is strong evidence to support that Aloe vera exhibits beneficial effects in prevention of carious lesions, non-surgical scaling and root planing in patients with chronic periodontitis, and oral wounds. Furthermore, it is cost effective and easily accessible. This review’s findings indicate that dental health care providers could recommend Aloe vera as a preventive and an alternative treatment option to improve patients’ oral health status.https://scholarscompass.vcu.edu/denh_student/1005/thumbnail.jp

Growing trees in Internet news groups and forums

We present an empirical study of the networks created by users within internet news groups and forums and show that they organ- ise themselves into scale-free trees. The structure of these trees depends on the topic under discussion; specialist topics have trees with a short shallow structure whereas more universal topics are discussed widely and have a deeper tree structure. For news groups we find that the distribu- tion of the time intervals between when a message is posted and when it receives a response exhibits a composite power-law behaviour. From our statistics we can see if the news group or forum is free or is overseen by a moderator. The correlation function of activity, the number of messages posted in a given time, shows long range correlations connected with the users’ daily routines. The distribution of distances between each message and its root is exponential for most news groups and power-law for the fo- rums. For both formats we find that the relation between the supremacy ( the total number of nodes that are under the node i, including node i) and the degree is linear s(k) k, in contrast to the analytical relation for Barab´asi-Albert network

A Churn for the Better: Localizing Censorship using Network-level Path Churn and Network Tomography

Recent years have seen the Internet become a key vehicle for citizens around the globe to express political opinions and organize protests. This fact has not gone unnoticed, with countries around the world repurposing network management tools (e.g., URL filtering products) and protocols (e.g., BGP, DNS) for censorship. However, repurposing these products can have unintended international impact, which we refer to as "censorship leakage". While there have been anecdotal reports of censorship leakage, there has yet to be a systematic study of censorship leakage at a global scale. In this paper, we combine a global censorship measurement platform (ICLab) with a general-purpose technique -- boolean network tomography -- to identify which AS on a network path is performing censorship. At a high-level, our approach exploits BGP churn to narrow down the set of potential censoring ASes by over 95%. We exactly identify 65 censoring ASes and find that the anomalies introduced by 24 of the 65 censoring ASes have an impact on users located in regions outside the jurisdiction of the censoring AS, resulting in the leaking of regional censorship policies

The RIPE NCC internet measurement data repository

This paper describes datasets that will shortly be made available to the research community through an Internet measurement data repository operated by the RIPE NCC. The datasets include measurements collected by RIPE NCC projects, packet trace sets recovered from the defunct NLANR website and datasets collected and currently hosted by other research institutions. This work aims to raise awareness of these datasets amongst researchers and to promote discussion about possible changes to the data collection processes to ensure that the measurements are relevant and useful to the community

SENATUS: An Approach to Joint Traffic Anomaly Detection and Root Cause Analysis

In this paper, we propose a novel approach, called SENATUS, for joint traffic anomaly detection and root-cause analysis. Inspired from the concept of a senate, the key idea of the proposed approach is divided into three stages: election, voting and decision. At the election stage, a small number of \nop{traffic flow sets (termed as senator flows)}senator flows are chosen\nop{, which are used} to represent approximately the total (usually huge) set of traffic flows. In the voting stage, anomaly detection is applied on the senator flows and the detected anomalies are correlated to identify the most possible anomalous time bins. Finally in the decision stage, a machine learning technique is applied to the senator flows of each anomalous time bin to find the root cause of the anomalies. We evaluate SENATUS using traffic traces collected from the Pan European network, GEANT, and compare against another approach which detects anomalies using lossless compression of traffic histograms. We show the effectiveness of SENATUS in diagnosing anomaly types: network scans and DoS/DDoS attacks