Reviewing Traffic ClassificationData Traffic Monitoring and Analysis

Traffic classification has received increasing attention in the last years. It aims at offering the ability to automatically recognize the application that has generated a given stream of packets from the direct and passive observation of the individual packets, or stream of packets, flowing in the network. This ability is instrumental to a number of activities that are of extreme interest to carriers, Internet service providers and network administrators in general. Indeed, traffic classification is the basic block that is required to enable any traffic management operations, from differentiating traffic pricing and treatment (e.g., policing, shaping, etc.), to security operations (e.g., firewalling, filtering, anomaly detection, etc.). Up to few years ago, almost any Internet application was using well-known transport layer protocol ports that easily allowed its identification. More recently, the number of applications using random or non-standard ports has dramatically increased (e.g. Skype, BitTorrent, VPNs, etc.). Moreover, often network applications are configured to use well-known protocol ports assigned to other applications (e.g. TCP port 80 originally reserved for Web traffic) attempting to disguise their presence. For these reasons, and for the importance of correctly classifying traffic flows, novel approaches based respectively on packet inspection, statistical and machine learning techniques, and behavioral methods have been investigated and are becoming standard practice. In this chapter, we discuss the main trend in the field of traffic classification and we describe some of the main proposals of the research community. We complete this chapter by developing two examples of behavioral classifiers: both use supervised machine learning algorithms for classifications, but each is based on different features to describe the traffic. After presenting them, we compare their performance using a large dataset, showing the benefits and drawback of each approac

YAP enhances the pro-proliferative transcriptional activity of mutant p53 proteins

Mutant p53 proteins are present in more than half of human cancers. Yes-associated protein (YAP) is a key transcriptional regulator controlling organ growth, tissue homeostasis, and cancer. Here, we report that these two determinants of human malignancy share common transcriptional signatures. YAP physically interacts with mutant p53 proteins in breast cancer cells and potentiates their pro-proliferative transcriptional activity. We found YAP as well as mutant p53 and the transcription factor NF-Y onto the regulatory regions of cyclin A, cyclin B, and CDK1 genes. Either mutant p53 or YAP depletion down-regulates cyclin A, cyclin B, and CDK1 gene expression and markedly slows the growth of diverse breast cancer cell lines. Pharmacologically induced cytoplasmic re-localization of YAP reduces the expression levels of cyclin A, cyclin B, and CDK1 genes both in vitro and in vivo. Interestingly, primary breast cancers carrying p53 mutations and displaying high YAP activity exhibit higher expression levels of cyclin A, cyclin B, and CDK1 genes when compared to wt-p53 tumors

Highly Sensitive Electrochemiluminescent Nanobiosensor for the Detection of Palytoxin

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Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy

Effectiveness of regional restrictions in reducing SARS-CoV-2 transmission during the second wave of COVID-19, Italy

To counter the second COVID-19 wave, the Italian government has adopted a scheme of three sets of restrictions (coded as yellow, orange, and red) imposed on a regional basis. We estimate that milder restrictions in regions at lower risk (yellow) resulted in a transmissibility reduction of about 18%, leading to a reproduction number Rt of about 0.99. Stricter measures (orange and red) led to reductions of 34% and 45% and Rt values of about 0.89 and 0.77 respectively

Impact of tiered restrictions on human activities and the epidemiology of the second wave of COVID-19 in Italy

To counter the second COVID-19 wave in autumn 2020, the Italian government introduced a system of physical distancing measures organized in progressively restrictive tiers (coded as yellow, orange, and red) imposed on a regional basis according to real-time epidemiological risk assessments. We leverage the data from the Italian COVID-19 integrated surveillance system and publicly available mobility data to evaluate the impact of the three-tiered regional restriction system on human activities, SARS-CoV-2 transmissibility and hospitalization burden in Italy. The individuals' attendance to locations outside the residential settings was progressively reduced with tiers, but less than during the national lockdown against the first COVID-19 wave in the spring. The reproduction number R(t) decreased below the epidemic threshold in 85 out of 107 provinces after the introduction of the tier system, reaching average values of about 0.95-1.02 in the yellow tier, 0.80-0.93 in the orange tier and 0.74-0.83 in the red tier. We estimate that the reduced transmissibility resulted in averting about 36% of the hospitalizations between November 6 and November 25, 2020. These results are instrumental to inform public health efforts aimed at preventing future resurgence of cases