No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone

It is generally recognized that the traffic generated by an individual connected to a network acts as his biometric signature. Several tools exploit this fact to fingerprint and monitor users. Often, though, these tools assume to access the entire traffic, including IP addresses and payloads. This is not feasible on the grounds that both performance and privacy would be negatively affected. In reality, most ISPs convert user traffic into NetFlow records for a concise representation that does not include, for instance, any payloads. More importantly, large and distributed networks are usually NAT'd, thus a few IP addresses may be associated to thousands of users. We devised a new fingerprinting framework that overcomes these hurdles. Our system is able to analyze a huge amount of network traffic represented as NetFlows, with the intent to track people. It does so by accurately inferring when users are connected to the network and which IP addresses they are using, even though thousands of users are hidden behind NAT. Our prototype implementation was deployed and tested within an existing large metropolitan WiFi network serving about 200,000 users, with an average load of more than 1,000 users simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned out to be very effective, with an accuracy greater than 90%. We also devised new tools and refined existing ones that may be applied to other contexts related to NetFlow analysis

No Place to Hide that Bytes won't Reveal: Sniffing Location-Based Encrypted Traffic to Track a User's Position

News reports of the last few years indicated that several intelligence agencies are able to monitor large networks or entire portions of the Internet backbone. Such a powerful adversary has only recently been considered by the academic literature. In this paper, we propose a new adversary model for Location Based Services (LBSs). The model takes into account an unauthorized third party, different from the LBS provider itself, that wants to infer the location and monitor the movements of a LBS user. We show that such an adversary can extrapolate the position of a target user by just analyzing the size and the timing of the encrypted traffic exchanged between that user and the LBS provider. We performed a thorough analysis of a widely deployed location based app that comes pre-installed with many Android devices: GoogleNow. The results are encouraging and highlight the importance of devising more effective countermeasures against powerful adversaries to preserve the privacy of LBS users.Comment: 14 pages, 9th International Conference on Network and System Security (NSS 2015

Topical issue on nuclear symmetry energy

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A Machine Learning Approach for Carotid Diseases using Heart Rate Variability Features

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A new database of healthy and pathological voices☆ Ugo Cesari a, Giuseppe De Pietro b, Elio Marciano c, Ciro Niri d, Giovanna Sannino,b, Laura Verde e a Department of Otorhinolaryngology, University Hospital (Policlinico) Federico II of Naples, Via S.Pansini, 5 Naples, Italy b Institute of High Performance Computing and Networking (ICAR-CNR), Via Pietro Castellino, 111, Naples, Italy c Area of Audiology, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via S.Pansini, 5, Naples, Italy d Independent Doctor Surgeon Specialized in Audiology and Phoniatrics, Naples, Italy e Department of Engineering, University of Naples Parthenope, Centro Direzionale di Napoli, Isola C4, Naples, Italy

In the era of Edge-of-Things computing for the accomplishment of smart healthcare systems, the availability of accurate and reliable databases is important to provide the right tools for researchers and business companies to design, develop and test new techniques, methodologies and/or algorithms to monitor or detect the patient’s healthcare status. In this paper, the study and building of the VOice ICar fEDerico II (VOICED) database are presented, useful for anybody who needs voice signals in her/his research activities. It consists of 208 healthy and pathological voices collected during a clinical study performed following the guidelines of the medical SIFEL (Società Italiana di Foniatria e Logopedia) protocol and the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 Statement. For each subject, the database contains a recording of the vowel /a/ of five seconds in length, lifestyle information, the medical diagnosis, and the results of two specific medical questionnaires

voice disorder identification by using machine learning techniques

Nowadays, the use of mobile devices in the healthcare sector is increasing significantly. Mobile technologies offer not only forms of communication for multimedia content (e.g. clinical audio-visual notes and medical records) but also promising solutions for people who desire the detection, monitoring, and treatment of their health conditions anywhere and at any time. Mobile health systems can contribute to make patient care faster, better, and cheaper. Several pathological conditions can benefit from the use of mobile technologies. In this paper we focus on dysphonia, an alteration of the voice quality that affects about one person in three at least once in his/her lifetime. Voice disorders are rapidly spreading, although they are often underestimated. Mobile health systems can be an easy and fast support to voice pathology detection. The identification of an algorithm that discriminates between pathological and healthy voices with more accuracy is necessary to realize a valid and precise mobile health system. The key contribution of this paper is to investigate and compare the performance of several machine learning techniques useful for voice pathology detection. All analyses are performed on a dataset of voices selected from the Saarbruecken voice database. The results obtained are evaluated in terms of accuracy, sensitivity, specificity, and receiver operating characteristic area. They show that the best accuracy in voice diseases detection is achieved by the support vector machine algorithm or the decision tree one, depending on the features evaluated by using opportune feature selection methods

COL R Acinetobacter baumannii sRNA Signatures : Computational Comparative Identification and Biological Targets

Multidrug-Resistant (MDR) and Extensively Drug Resistant (XDR) Acinetobacter baumannii (Ab) represent a serious cause of healthcare-associated infections worldwide. Currently, the available treatment options are very restricted and colistin-based therapies are last-line treatments of these infections, even though colistin resistant (COL R) Ab have rarely been isolated yet. In bacteria, small non-coding RNAs (sRNAs) have been implicated in regulatory pathways of different biological functions, however, no knowledge exists about the sRNA role on the biological adaptation in COL R Ab. Our study investigated two Italian XDR isogenic colistin-susceptible/resistant (COL S/R) Ab strain-pairs to discover new sRNA signatures. Comparative sRNA transcriptome (sRNAome) analyses were carried out by Illumina RNA-seq using both a Tru-Seq and a Short Insert library, whilst Ab ATCC 17978 and ACICU Reference Genome assembly, mapping, annotation and statistically significant differential expression (q -value ≤ 0.01) of the raw reads were performed by the Rockhopper tool. A computational filtering, sorting only similarly statistically significant differentially expressed (DE) sRNAs mapping on the same gene in both COL R Ab isolates was conducted. COL R vs. COL S sRNAome, analyzed integrating the DE sRNAs obtained from the two different libraries, revealed some statistically significant DE sRNAs in COL R Ab. In detail, we found: (i) two different under-expressed cis -acting sRNAs (Ab sRNA and Ab sRNA) mapping in antisense orientation the 16S rRNA gene A1S_r01, (ii) one under-expressed cis -acting sRNA (Ab sRNA) targeting the A1S_2505 gene (hypothetical protein), (iii) one under-expressed microRNA-size small RNA fragment (Ab sRNA) and its pre-micro Ab sRNA targeting the A1S_0501 gene (hypothetical protein), (iv) as well as an over-expressed microRNA-size small RNA fragment (Ab sRNA) and its pre-micro Ab sRNA targeting the A1S_3097 gene (signal peptide). Custom TaqMan ® probe-based real-time qPCRs validated the expression pattern of the selected sRNA candidates shown by RNA-seq. Furthermore, analysis on sRNA ΔA1S_r01, ΔA1S_2505 as well as the over-expressed A1S_3097 mutants revealed no effects on colistin resistance. Our study, for the first time, found the sRNAome signatures of clinical COL R Ab with a computational prediction of their targets related to protein synthesis, host-microbe interaction and other different biological functions, including biofilm production, cell-cycle control, virulence, and antibiotic-resistance

Colistin Resistance Onset Strategies and Genomic Mosaicism in Clinical Acinetobacter baumannii Lineages

Abstract: The treatment of multidrug-resistant Gram-negative infections is based on colistin. As result, COL-resistance (COL-R) can develop and spread. In Acinetobacter baumannii, a crucial step is to understand COL-R onset and stability, still far to be elucidated. COL-R phenotypic stability, onset modalities, and phylogenomics were investigated in a clinical A. baumannii sample showing a COL resistant (COLR) phenotype at first isolation. COL-R was confirmed by Minimum-InhibitoryConcentrations as well as investigated by Resistance-Induction assays and Population-AnalysisProfiles (PAPs) to determine: (i) stability; (ii) inducibility; (iii) heteroresistance. Genomics was performed by Mi-Seq Whole-Genome-Sequencing, Phylogenesis, and Genomic Epidemiology by bioinformatics. COLR A. baumannii were subdivided as follows: (i) 3 A. baumannii with stable and high COL MICs defining the “homogeneous-resistant” onset phenotype; (ii) 6 A. baumannii with variable and lower COL MICs displaying a “COL-inducible” onset phenotype responsible for adaptive-resistance or a “subpopulation” onset phenotype responsible for COL-heteroresistance. COL-R stability and onset strategies were not uniquely linked to the amount of LPS and cell envelope charge. Phylogenomics categorized 3 lineages clustering stable and/or unstable COL-R phenotypes with increasing genomic complexity. Likewise, different nsSNP profiling in genes already associated with COL-R marked the stable and/or unstable COL-R phenotypes. Our investigation finds out that A. baumannii can range through unstable or stable COLR phenotypes emerging via different “onset strategies” within phylogenetic lineages displaying increasing genomic mosaicism

Radiation therapy affects YAP expression and intracellular localization by modulating lamin A/C levels in breast cancer

The microenvironment of breast cancer actively participates in tumorigenesis and cancer progression. The changes observed in the architecture of the extracellular matrix initiate an oncogene-mediated cell reprogramming, that leads to a massive triggering of YAP nuclear entry, and, therefore, to cancer cell proliferation, invasion and probably to increased radiation-resistance. However, it is not yet fully understood how radiotherapy regulates the expression and subcellular localization of YAP in breast cancer cells experiencing different microenvironmental stiffnesses. To elucidate the role of extracellular matrix stiffness and ionizing radiations on YAP regulation, we explored the behaviour of two different mammary cell lines, a normal epithelial cell line (MCF10A) and a highly aggressive and invasive adenocarcinoma cell line (MDA-MB-231) interacting with polyacrylamide substrates mimicking the mechanics of both normal and tumour tissues (~1 and ~13 kPa). We report that X-ray radiation affected in a significant way the levels of YAP expression, density, and localization in both cell lines. After 24 h, MCF10A and MDA-MB231 increased the expression level of YAP in both nucleus and cytoplasm in a dose dependent manner and particularly on the stiffer substrates. After 72 h, MCF10A reduced mostly the YAP expression in the cytoplasm, whereas it remained high in the nucleus of cells on stiffer substrates. Tumour cells continued to exhibit higher levels of YAP expression, especially in the cytoplasmic compartment, as indicated by the reduction of nuclear/ cytoplasmic ratio of total YAP. Then, we investigated the existence of a correlation between YAP localization and the expression of the nuclear envelope protein lamin A/C, considering its key role in modulating nuclear deformability and changes in YAP shuttling phenomena. As supposed, we found that the effects of radiation on YAP nucleus/cytoplasmic expression ratio, increasing in healthy cells and decreasing in tumour ones, were accompanied by lower and higher lamin A/C levels in MCF10A and MDAMB-231 cells, respectively. These findings point to obtain a deeper knowledge of the role of the extracellular matrix and the effects of X-rays on YAP and lamin A/C expression that can be used in the design of doses and timing of radiation therapy