Stability of aqueous films between bubbles. Part 2. Effects of trace impurities and evaporation

The stability of water films has been investigated with a Mysels-Scheludko type film balance. Minor trace impurities in water do not affect the lifetime of water films under vapor saturation, but significantly influence the stability in free evaporation. Trace amounts of positively adsorbed contaminants induce Marangoni-driven flow that destabilizes films under evaporation conditions whereas negatively adsorbed electrolytes actually prolong stability by reversing interfacial tension gradients and driving a steady circulation within the film. At high thinning rates, pure-water films develop exotic-appearing flow patterns and break due to a strong coupling between hydrodynamic and interfacial tensiongradient adsorption stresses. The most dominant factor of transient film stabilization in dynamic conditions under evaporation is a surface tension gradient created in the film. We discuss surface tension gradients in transient films created by temperature differences, impurity concentration, and expansion of the films.

Stability of aqueous films between bubbles. Part 1. The effect of speed on bubble coalescence in purified water and simple electrolyte solutions

Film thinning experiments have been conducted with aqueous films between two air phases in a thin film pressure balance. The films are free of added surfactant but simple NaCl electrolyte is added in some experiments. Initially the experiments begin with a comparatively large volume of water in a cylindrical capillary tube a few millimeters in diameter, and by withdrawing water from the center of the tube the two bounding menisci are drawn together at a prescribed rate. Thismodels two air bubbles approaching at a controlled speed. In pure water, the results show three regimes of behavior depending on the approach speed; at slow speed (<1 μm/s) it is possible to form a flat film of pure water, ∼100 nm thick, that is stabilized indefinitely by disjoining pressure due to repulsive double-layer interactions between naturally charged air/water interfaces. The data are consistent with a surface potential of -57mV on the bubble surfaces. At intermediate approach speed (∼1-150 μm/s), the films are transiently stable due to hydrodynamic drainage effects, and bubble coalescence is delayed by ∼10-100 s. At approach speeds greater than ∼150 μm/s, the hydrodynamic resistance appears to become negligible, and the bubbles coalesce without any measurable delay. Explanations for these observations are presented that take into account Derjaguin-Landau-Verwey-Overbeek and Marangoni effects entering through disjoining pressure, surface mobility, and hydrodynamic flow regimes in thin film drainage. In particular, it is argued that the dramatic reduction in hydrodynamic resistance is a transition from viscosity-controlled drainage to inertia-controlled drainage associated with a change from immobile to mobile air/water interfaces on increasing the speed of approach of two bubbles. A simple model is developed that accounts for the boundaries between different film stability or coalescence regimes. Predictions of the model are consistent with the data, and the effects of adding electrolyte can be explained. In particular, addition of electrolyte at high concentration inhibits the near-instantaneous coalescence phenomenon, thereby contributing to increased foam film stability at high approach speeds, as reported in previous literature. This work highlights the significance of bubble approach speed as well as electrolyte concentration in affecting bubble coalescence.

Internet traffic classification using machine learning : a token-based approach

Due to the increasing unreliability of traditional port-based methods, Internet traffic classification has attracted a lot of research efforts in recent years. Quite a lot of previous papers have focused on using statistical characteristics as discriminators and applying machine learning techniques to classify the traffic flows. In this paper, we propose a novel machine learning based approach where the features are extracted from packet payload instead of flow statistics. Specifically, every flow is represented by a feature vector, in which each item indicates the occurrence of a particular token, i.e.; a common substring, in the payload. We have applied various machine learning algorithms to evaluate the idea and used different feature selection schemes to identify the critical tokens. Experimental result based on a real-world traffic data set shows that the approach can achieve high accuracy with low overhead.

A novel semi-supervised approach for network traffic clustering

Network traffic classification is an essential component for network management and security systems. To address the limitations of traditional port-based and payload-based methods, recent studies have been focusing on alternative approaches. One promising direction is applying machine learning techniques to classify traffic flows based on packet and flow level statistics. In particular, previous papers have illustrated that clustering can achieve high accuracy and discover unknown application classes. In this work, we present a novel semi-supervised learning method using constrained clustering algorithms. The motivation is that in network domain a lot of background information is available in addition to the data instances themselves. For example, we might know that flow ƒ1 and ƒ2 are using the same application protocol because they are visiting the same host address at the same port simultaneously. In this case, ƒ1 and ƒ2 shall be grouped into the same cluster ideally. Therefore, we describe these correlations in the form of pair-wise must-link constraints and incorporate them in the process of clustering. We have applied three constrained variants of the K-Means algorithm, which perform hard or soft constraint satisfaction and metric learning from constraints. A number of real-world traffic traces have been used to show the availability of constraints and to test the proposed approach. The experimental results indicate that by incorporating constraints in the course of clustering, the overall accuracy and cluster purity can be significantly improved.

Discriminating DDoS attacks from flash crowds using flow correlation coefficient

Distributed Denial of Service (DDoS) attack is a critical threat to the Internet, and botnets are usually the engines behind them. Sophisticated botmasters attempt to disable detectors by mimicking the traffic patterns of flash crowds. This poses a critical challenge to those who defend against DDoS attacks. In our deep study of the size and organization of current botnets, we found that the current attack flows are usually more similar to each other compared to the flows of flash crowds. Based on this, we proposed a discrimination algorithm using the flow correlation coefficient as a similarity metric among suspicious flows. We formulated the problem, and presented theoretical proofs for the feasibility of the proposed discrimination method in theory. Our extensive experiments confirmed the theoretical analysis and demonstrated the effectiveness of the proposed method in practice.

Chemical and thermal stability of surface‐modified porous polyethylene membranes

In this paper, we describe the surface modification of porous polyethylene by the adsorption of polyelectrolyte mutilayers on plasma‐activated polyethylene surfaces. We use the migration rates of deionized water as an effective alternative to contact angle measurements in order to probe the interfacial energy of the modified surface. The newly acquired surface properties that result from the surface modification are monitored with respect to several key chemical and environmental variables. These variables were chosen so that they will reflect some of the common handling procedures in a laboratory or health care environments, such as exposure to solvents of different pH and polarities, and fluctuations of ambient temperature over an extended period, i.e., “shelf‐life” duration. The stability of these surface properties of the modified membranes is a fundamental requirement for their potential use in a variety of applications involving lateral flow and binding media for bio‐assays. In this paper, we show that a membrane modified by a polyelectrolyte monolayer is more stable than a membrane that has undergone plasma activation alone, while a membrane modified by a polyelectrolyte bilayer exhibits retention of the enhanced surface hydrophilic properties under various conditions and over a long period of time.

Internet traffic clustering with constraints

Due to the limitations of the traditional port-based and payload-based traffic classification approaches, the past decade has seen extensive work on utilizing machine learning techniques to classify network traffic based on packet and flow level features. In particular, previous studies have shown that the unsupervised clustering approach is both accurate and capable of discovering previously unknown application classes. In this paper, we explore the utility of side information in the process of traffic clustering. Specifically, we focus on the flow correlation information that can be efficiently extracted from packet headers and expressed as instance-level constraints, which indicate that particular sets of flows are using the same application and thus should be put into the same cluster. To incorporate the constraints, we propose a modified constrained K-Means algorithm. A variety of real-world traffic traces are used to show that the constraints are widely available. The experimental results indicate that the constrained approach not only improves the quality of the resulted clusters, but also speeds up the convergence of the clustering process.

Semi-supervised and compound classification of network traffic

This paper presents a new semi-supervised method to effectively improve traffic classification performance when few supervised training data are available. Existing semi supervised methods label a large proportion of testing flows as unknown flows due to limited supervised information, which severely affects the classification performance. To address this problem, we propose to incorporate flow correlation into both training and testing stages. At the training stage, we make use of flow correlation to extend the supervised data set by automatically labeling unlabeled flows according to their correlation to the pre-labeled flows. Consequently, the traffic classifier has better performance due to the extended size and quality of the supervised data sets. At the testing stage, the correlated flows are identified and classified jointly by combining their individual predictions, so as to further boost the classification accuracy. The empirical study on the real-world network traffic shows that the proposed method outperforms the state-of-the-art flow statistical feature based classification methods.

Classification of correlated internet traffic flows

A critical problem for Internet traffic classification is how to obtain a high-performance statistical feature based classifier using a small set of training data. The solutions to this problem are essential to deal with the encrypted applications and the new emerging applications. In this paper, we propose a new Naive Bayes (NB) based classification scheme to tackle this problem, which utilizes two recent research findings, feature discretization and flow correlation. A new bag-of-flow (BoF) model is firstly introduced to describe the correlated flows and it leads to a new BoF-based traffic classification problem. We cast the BoF-based traffic classification as a specific classifier combination problem and theoretically analyze the classification benefit from flow aggregation. A number of combination methods are also formulated and used to aggregate the NB predictions of the correlated flows. Finally, we carry out a number of experiments on a large scale real-world network dataset. The experimental results show that the proposed scheme can achieve significantly higher classification accuracy and much faster classification speed with comparison to the state-of-the-art traffic classification methods.

Internet traffic classification by aggregating correlated naive bayes predictions

This paper presents a novel traffic classification scheme to improve classification performance when few training data arc available. In the proposed scheme, traffic flows are described using the discretized statistical features and flow correlation information is modeled by bag-of-flow (BoF). We solve the BoF-based traffic classification in a classifier combination framework and theoretically analyze the performance benefit. Furthermore, a new BoF-based traffic classification method is proposed to aggregate the naive Bayes (NB) predictions of the correlated flows. We also present an analysis on prediction error sensitivity of the aggregation strategies. Finally, a large number of experiments are carried out on two large-scale real-world traffic datasets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing state-of-the-art traffic classification methods.

Network traffic classification using correlation information

Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, we propose a novel nonparametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples.

Classifying network traffic in the big data era

With the arrival of Big Data Era, properly utilizing the power of big data is becoming increasingly essential for the strength and competitiveness of businesses and organizations. We are facing grand challenges from big data from different perspectives, such as processing, communication, security, and privacy. In this talk, we discuss the big data challenges in network traffic classification and our solutions to the challenges. The significance of the research lies in the fact that each year the network traffic increase exponentially on the current Internet. Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine-learning techniques to flow statistical feature based classification methods. In this talk, we propose a series of novel approaches for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approaches and their performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic datasets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples. Our work has significant impact on security applications.

Internet traffic clustering with side information

Internet traffic classification is a critical and essential functionality for network management and security systems. Due to the limitations of traditional port-based and payload-based classification approaches, the past several years have seen extensive research on utilizing machine learning techniques to classify Internet traffic based on packet and flow level characteristics. For the purpose of learning from unlabeled traffic data, some classic clustering methods have been applied in previous studies but the reported accuracy results are unsatisfactory. In this paper, we propose a semi-supervised approach for accurate Internet traffic clustering, which is motivated by the observation of widely existing partial equivalence relationships among Internet traffic flows. In particular, we formulate the problem using a Gaussian Mixture Model (GMM) with set-based equivalence constraint and propose a constrained Expectation Maximization (EM) algorithm for clustering. Experiments with real-world packet traces show that the proposed approach can significantly improve the quality of resultant traffic clusters. © 2014 Elsevier Inc.