Mining Unclassified Traffic Using Automatic Clustering Techniques

In this paper we present a fully unsupervised algorithm to identify classes of traffic inside an aggregate. The algorithm leverages on the K-means clustering algorithm, augmented with a mechanism to automatically determine the number of traffic clusters. The signatures used for clustering are statistical representations of the application layer protocols. The proposed technique is extensively tested considering UDP traffic traces collected from operative networks. Performance tests show that it can clusterize the traffic in few tens of pure clusters, achieving an accuracy above 95%. Results are promising and suggest that the proposed approach might effectively be used for automatic traffic monitoring, e.g., to identify the birth of new applications and protocols, or the presence of anomalous or unexpected traffi

KISS: Stochastic Packet Inspection Classifier for UDP Traffic

This paper proposes KISS, a novel Internet classifica- tion engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming appli- cations, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square-like test, which extracts the protocol "format," but ignores the protocol "semantic" and "synchronization" rules. The signatures feed a decision process based either on the geometric distance among samples, or on Sup- port Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asym- metry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server proto- cols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are al- most perfect when dealing with new P2P streaming applications

Quasiconformal contact foliations

We show that every quasiconformal contact foliation supports an invariant metric and characterise such foliations by the dynamical property of $C^1$-equicontinuity. We prove that a generalisation of the Weinstein conjecture holds for quasiconformal contact foliations, and provide a lower bound to the number of closed leaves. In particular, we show that the Weinstein conjecture holds for quasiconformal Reeb fields

Uncovering the big players of the web

In this paper we aim at observing how today the Internet large organizations deliver web content to end users. Using one-week long data sets collected at three vantage points aggregating more than 30,000 Internet customers, we characterize the offered services precisely quantifying and comparing the performance of different players. Results show that today 65% of the web traffic is handled by the top 10 organiza- tions. We observe that, while all of them serve the same type of content, different server architectures have been adopted considering load bal- ancing schemes, servers number and location: some organizations handle thousands of servers with the closest being few milliseconds far away from the end user, while others manage few data centers. Despite this, the performance of bulk transfer rate offered to end users are typically good, but impairment can arise when content is not readily available at the server and has to be retrieved from the CDN back-en

Characterization of ISP Traffic: Trends, User Habits, and Access Technology Impact

In the recent years, the research community has increased its focus on network monitoring which is seen as a key tool to understand the Internet and the Internet users. Several studies have presented a deep characterization of a particular application, or a particular network, considering the point of view of either the ISP, or the Internet user. In this paper, we take a different perspective. We focus on three European countries where we have been collecting traffic for more than a year and a half through 5 vantage points with different access technologies. This humongous amount of information allows us not only to provide precise, multiple, and quantitative measurements of "What the user do with the Internet" in each country but also to identify common/uncommon patterns and habits across different countries and nations. Considering different time scales, we start presenting the trend of application popularity; then we focus our attention to a one-month long period, and further drill into a typical daily characterization of users activity. Results depict an evolving scenario due to the consolidation of new services as Video Streaming and File Hosting and to the adoption of new P2P technologies. Despite the heterogeneity of the users, some common tendencies emerge that can be leveraged by the ISPs to improve their servic

Fixed And Growth Mindset In Education And How Grit Helps Students Persist In The Face Of Adversity

Students face a wealth of challenges in college for example a lack of support, sometimes making it difficult to persevere.  However, in an academic environment that teaches grit and fosters growth, students can learn to persist. Those who believe intelligence is fixed and cannot be changed exert less effort to succeed. Students who persevere when faced with challenges and adversity seem to have what Angela Duckworth calls, grit. This is the idea behind a growth mindset in learning according to Dweck (1999, 2007, 2010) and Duckworth, Peterson, Matthews, and Kelly (2007). Grit can be defined as “…passion and perseverance for long-term goals…” (Frontiers In Psychology, 2014).  One grit effect study was conducted in collaboration with the U.S. Army and the University of Pennsylvania to create predictors of retention. The purpose of this present work is to explore researched competencies related to persistence in reaching academic goals, review literature in grit and growth mindset related to learning and persistence, and examine what educators can do to foster grit and a growth mindset.  Recommendations for increasing persistence and grit in college students will be provided

Seizure outcome after switching antiepileptic drugs: A matched, prospective study.

OBJECTIVE: Outcomes after changing antiepileptic drugs (AEDs) have largely been studied in single cohort series. We recently reported the first study to examine this question in a controlled manner. Here we expand on these results by using a matched, prospective methodology applied to both uncontrolled and well-controlled patients taking any AED. METHODS: We reviewed all outpatient notes over a 9-month period and identified patients with focal epilepsy who were on monotherapy. We classified those who switched AEDs as case patients, with those remaining on the same drug serving as controls. We matched cases with controls for seizure status (seizure-free in the preceding 6 months or not), current AED, and number of failed AEDs. We subsequently assessed outcome 6 months later. RESULTS: Seizure-free patients who switched drug (n = 12) had a 16.7% rate of seizure recurrence at 6 months, compared to 2.8% among controls remaining on the same drug (n = 36, p = 0.11). There was a 37% remission rate among uncontrolled patients who switched drug compared to 55.6% among controls (n = 27 per group, p = 0.18). Uncontrolled patients who had previously tried more than one AED were somewhat less likely to enter remission (p = 0.057). Neither AED mechanism of action nor change in dosage impacted outcome. SIGNIFICANCE: Herein we provide further estimation of the modest risk (~14%) associated with switching AEDs in patients in remission compared to being maintained on the same regimen. Uncontrolled patients were no more likely to enter remission after a drug switch than they were after remaining on the same drug, suggesting that spontaneous changes in disease state, and not drug response, underlie remission in this population