Automatic detection of DNS manipulations

The DNS is a fundamental service that has been repeatedly attacked and abused. DNS manipulation is a prominent case: Recursive DNS resolvers are deployed to explicitly return manipulated answers to users' queries. While DNS manipulation is used for legitimate reasons too (e.g., parental control), rogue DNS resolvers support malicious activities, such as malware and viruses, exposing users to phishing and content injection. We introduce REMeDy, a system that assists operators to identify the use of rogue DNS resolvers in their networks. REMeDy is a completely automatic and parameter-free system that evaluates the consistency of responses across the resolvers active in the network. It operates by passively analyzing DNS traffic and, as such, requires no active probing of third-party servers. REMeDy is able to detect resolvers that manipulate answers, including resolvers that affect unpopular domains. We validate REMeDy using large-scale DNS traces collected in ISP networks where more than 100 resolvers are regularly used by customers. REMeDy automatically identifies regular resolvers, and pinpoint manipulated responses. Among those, we identify both legitimate services that offer additional protection to clients, and resolvers under the control of malwares that steer traffic with likely malicious goals

Dissecting Video Server Selection Strategies in the YouTube CDN

In this paper, we conduct a detailed study of theYouTube CDN with a view to understanding the mechanismsand policies used to determine which data centers users downloadvideo from. Our analysis is conducted using week-long datasetssimultaneously collected from the edge of five networks - twouniversity campuses and three ISP networks - located in threedifferent countries. We employ state-of-the-art delay-based geolo-cation techniques to find the geographical location of YouTubeservers. A unique aspect of our work is that we perform ouranalysis on groups of related YouTube flows. This enables us toinfer key aspects of the system design that would be difficultto glean by considering individual flows in isolation. Our resultsreveal that while the RTT between users and data centers plays arole in the video server selection process, a variety of other factorsmay influence this selection including load-balancing, diurnaleffects, variations across DNS servers within a network, limitedavailability of rarely accessed video, and the need to alleviatehot-spots that may arise due to popular video content

Mining Patterns in Mobile Network Logs

Alarm logs are a valuable source of information and play a crucial role in network management. Network devices such as backbone routers or 3G/4G base stations generate verbose and detailed logs that network managers process to detect problems and identify their root causes. Manual analysis of such logs is extremely time-consuming because of the extensive amount of data. Therefore, finding suitable automatic methods to process logs is an important problem in the network analysis area.In this paper, we target the automatic extraction of situations, i.e., sequences of events occurring close in time and space which identify common and recurring patterns. We adopt an unsupervised machine learning approach to automatically mine logs and provide information and correlations in network failures. We face a real use case processing more than 2 million alarms generated by 2 months of TIM Network Operations Center in Northern Italy. Most of the features are categorical and call for specific methodologies to process them. We choose rule mining of frequent items. We focus on event logs and apply rule mining methods to extract temporal-spatial correlations and co-occurrences, i.e., situations. To ease the analyst work, we highlight the most important rules and offer visualization techniques in both spatial and temporal dimensions. Results have been verified to be helpful to recognize common situations and identify possible future anomalies

QoS Routing Strategies when Link State Information is Out-of-Date

Ilkley, West Yorkshire, U.K

Routing Algorithms Evaluation for Elastic Traffic

An innovative simulation technique for the performance of routing algorithms in presence of elastic traffic is proposed in this paper

Trials Supported By Smart Networks Beyond 5G: the TrialsNet Approach

TrialsNet is a project focused on improving European urban ecosystems through 13 innovative use cases in the three representative domains of Infrastructure, Transportation, Security and Safety; eHealth and Emergency; and Culture, Tourism, and Entertainment. These use cases will be implemented across different clusters in Italy, Spain, Greece, and Romania, involving real users. This paper provides an overview of the various use cases that will be trialled in different contexts through the platform and network solutions that will be deployed by the project based on advanced functionalities such as dynamic slicing management, NFV, MEC, AI/ML, and others. To this end, TrialsNet will develop assessment frameworks to measure the impact of use cases on a technical, socio-economic, and societal level through the definition and measurement of proper Key Performance Indicators (KPIs) and Key Value Indicators (KVIs). The project seeks to identify network limitations, optimize infrastructure, and define new requirements for next-generation mobile networks. Ultimately, TrialsNet aims to enhance livability in urban environments by driving advancements in various domains

High mobility group A1 protein modulates autophagy in cancer cells.

High Mobility Group A1 (HMGA1) is an architectural chromatin protein whose overexpression is a feature of malignant neoplasias with a causal role in cancer initiation and progression. HMGA1 promotes tumor growth by several mechanisms, including increase of cell proliferation and survival, impairment of DNA repair and induction of chromosome instability. Autophagy is a self-degradative process that, by providing energy sources and removing damaged organelles and misfolded proteins, allows cell survival under stress conditions. On the other hand, hyper-activated autophagy can lead to non-apoptotic programmed cell death. Autophagy deregulation is a common feature of cancer cells in which has a complex role, showing either an oncogenic or tumor suppressor activity, depending on cellular context and tumor stage. Here, we report that depletion of HMGA1 perturbs autophagy by different mechanisms. HMGA1-knockdown increases autophagosome formation by constraining the activity of the mTOR pathway, a major regulator of autophagy, and transcriptionally upregulating the autophagy-initiating kinase Unc-51-like kinase 1 (ULK1). Consistently, functional experiments demonstrate that HMGA1 binds ULK1 promoter region and negatively regulates its transcription. On the other hand, the increase in autophagosomes is not associated to a proportionate increase in their maturation. Overall, the effects of HMGA1 depletion on autophagy are associated to a decrease in cell proliferation and ultimately impact on cancer cells viability. Importantly, silencing of ULK1 prevents the effects of HMGA1-knockdown on cellular proliferation, viability and autophagic activity, highlighting how these effects are, at least in part, mediated by ULK1. Interestingly, this phenomenon is not restricted to skin cancer cells, as similar results have been observed also in HeLa cells silenced for HMGA1. Taken together, these results clearly indicate HMGA1 as a key regulator of the autophagic pathway in cancer cells, thus suggesting a novel mechanism through which HMGA1 can contribute to cancer progression