Network and traffic engineering in emerging distributed computing applications

This book focuses on network management and traffic engineering for Internet and distributed computing technologies, as well as present emerging technology trends and advanced platform

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.

Direct versus search engine traffic : an innovative approach to demand analysis in the property market

Purpose – The application of “Google” econometrics (Geco) has evolved rapidly in recent years and can be applied in various fields of research. Based on accepted theories in existing economic literature, this paper seeks to contribute to the innovative use of research on Google search query data to provide a new innovative to property research.

Design/methodology/approach – In this study, existing data from Google Insights for Search (GI4S) is extended into a new potential source of consumer sentiment data based on visits to a commonly-used UK online real-estate agent platform (Rightmove.co.uk). In order to contribute to knowledge about the use of Geco's black box, namely the unknown sampling population and the specific search queries influencing the variables, the GI4S series are compared to direct web navigation.

Findings – The main finding from this study is that GI4S data produce immediate real-time results with a high level of reliability in explaining the future volume of transactions and house prices in comparison to the direct website data. Furthermore, the results reveal that the number of visits to Rightmove.co.uk is driven by GI4S data and vice versa, and indeed without a contemporaneous relationship.

Originality/value – This study contributes to the new emerging and innovative field of research involving search engine data. It also contributes to the knowledge base about the increasing use of online consumer data in economic research in property markets.

Victims' perceptions of a new model of sexual assault investigation adopted by Victoria Police

This paper evaluates a method of operation for sexual assault investigation recently developed by Victoria Police (Australia). The model (which is new to Victoria) is characterised by two core components: the establishment of specialist teams of investigators (responsible for investigation and victim support) and service sites referred to as ‘Multidisciplinary Centres’ where all key services are provided to victims in a single location separate from police stations. The approach consisted of in-depth interviews with 25 victims of sexual assault aged between 15 and 54 years. The overriding theme to arise from the interviews was the importance to victims of being treated with dignity and respect; six elements were highlighted by victims as assisting this. These elements are presented along with evidence to demonstrate that the police response to victims has become more victim-centred under the new model of service delivery.

Moving right along: what powers do police have to 'move-on' protestors?

When police removed a young woman’s “tent dress” this week at the Occupy Melbourne encampment, it was yet another controversial interaction between protesters and authorities.

Effects of exposure to traffic noise on health

Traffic noise causes adverse effects on the health and quality of life of individuals and communities exposed to it, including annoyance, sleep disturbance, decreased performance at school/work, stress, hypertension, and ischemic heart disease. In Australia there are few standards or policies addressing noise in urban environments, with many discrepancies in noise level thresholds when comparing states and regions. Currently Victoria has a day-to-night threshold for noise levels well above accepted levels in Europe, and there is no standard for the late night period. A better understanding of the health impacts of noise in the Australian context is vital for informing development and implementation of policy and legislation for road traffic noise management. This paper reviews the evidence base and policies related to traffic noise in urban areas, and presents a case study of noise mapping and assessing population health impacts (eg. sleep disturbance), in Geelong,Vcitoria,Australia.

Distributed denial of service (DDoS) detection by traffic pattern analysis

In this paper, we propose a behavior-based detection that can discriminate Distributed Denial of Service (DDoS) attack traffic from legitimated traffic regardless to various types of the attack packets and methods. Current DDoS attacks are carried out by attack tools, worms and botnets using different packet-transmission rates and packet forms to beat defense systems. These various attack strategies lead to defense systems requiring various detection methods in order to identify the attacks. Moreover, DDoS attacks can craft the traffics like flash crowd events and fly under the radar through the victim. We notice that DDoS attacks have features of repeatable patterns which are different from legitimate flash crowd traffics. In this paper, we propose a comparable detection methods based on the Pearson’s correlation coefficient. Our methods can extract the repeatable features from the packet arrivals in the DDoS traffics but not in flash crowd traffics. The extensive simulations were tested for the optimization of the detection methods. We then performed experiments with several datasets and our results affirm that the proposed methods can differentiate DDoS attacks from legitimate traffics.

The association between alcohol interventions and Dui incidents in Geelong, Victoria

Introduction and aims : Driving under the influence of alcohol is a major public health problem, every year affecting the lives of billions around the world - and not least in Australia. Since 2001, several Traffic Accident Commission (TAC), police, and community interventions have been implemented in Geelong, Australia to curb drink driving. The current paper aims to assess the impact of 13 alcohol interventions on drink-driving rates in the Geelong region of Australia. The interventions comprised seven TAC media campaigns, three Victoria Police operations, two community interventions targeting licensed premises, and the alcohol interlock program.

Method : This study examined two types of Victoria Police frequency data: Driving under the influence (DUI) offences, and roadside preliminary breath testing (PBT) rates. Multiple regressions were carried out to determine if any of the interventions were significantly associated with frequency fluctuations in the data.

Results : Of the 13 alcohol interventions examined, three TAC campaigns and one Victoria Police operation precipitated significant decreases in drink-driving rates, while another three TAC campaigns were associated with significant increases in drink-driving rates. Over one in five (22.5%) had recorded prior DUI offences.

Conclusions : The most promising approach to curbing DUI-rates in Geelong, appear to be through informative media campaigns which show people specific settings where they might become mildly intoxicated without being aware of it, such as TAC’s ‘Education 1’ campaign. However, there remains a worrying level of recidivist drink drivers in Geelong suggesting the need for tailored approaches.

Evaluating police interviewing practices with suspects in child-sexual abuse cases

This article examined adherence to current best practice recommendations for police interviewing of individuals suspected of committing child-sexual offences. We analysed 81 police records of interviews (electronically recorded and then transcribed) with suspects in child-sexual abuse cases in England and Australia. Overall we found areas of skilled practice, indicating that police interviewing in Australia and England is in a far better place than 20 years ago. However, this study also demonstrated that there is still a gap between the recommended guidelines for interviewing and what actually happens in practice. Specifically, limitations were found in the following areas: transparency of the interview process; introduction of allegations; disclosure of evidence; questioning techniques; and the interviewing approach or manner adopted. The practical implications of these findings are discussed.

An examination of police officers' beliefs about how children report abuse

The aim of this study was to examine police officers’ beliefs about how children report abuse. Fifty-two officers read transcripts of nine interviews, which were conducted with actual children or adults playing the role of the child witness. Officers indicated whether they thought the interviews were with an actual child and justified their decisions. In-depth interviews were conducted to determine the reasons behind their decisions. Overall, officers’ decisions were no better than chance. When making these decisions, officers focused on three areas: whether they considered the child's language to be age-appropriate, whether they thought that the content of the statement was plausible, and whether they thought that the child had acted in a manner consistent with recollecting a traumatic event. The findings suggest that the characteristics officers rely on when evaluating children's statements of abuse are not reliable indicators. They suggest that officers’ beliefs about these statements need to be challenged during training to reduce the effects of those beliefs on their later decisions.