Surveillance, Security and Sporting Mega Events: Toward a Research Agenda on the Organisation of Security Networks

Surveillance and security at sports mega events have been the subject of considerable scholarly attention. Events such as the Olympic Games and Fédération Internationale de Football Association (FIFA) World Cups have become occasions of almost unparalleled economic, political and social significance. In the lead up to the London 2012 Olympic Games, scholars have examined issues such as the ‘security legacies’ of sports mega events, the infrastructures and technologies used in an attempt to secure these events, and the planning mentalities underpinning the staggering ‘security spectacle’ of these globally televised events. This paper deals with the subject of how surveillance and security practices at sports mega events are organised. It uses the emerging paradigm of ‘security networks’ to call attention to some important issues involving the entire ‘security assemblage’ that accompanies these mega events. The paper presents five levels of analysis—structural, cultural, policy, technological and relational—to examine these practices and documents several key areas for further research on sports mega events.

Topic model kernel : an empirical study towards probabilistically reduced features for classification

Probabilistic topic models have become a standard in modern machine learning with wide applications in organizing and summarizing ‘documents’ in high-dimensional data such as images, videos, texts, gene expression data, and so on. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics than bag-of-word interpretation, but also more informative for classification tasks. This paper describes the Topic Model Kernel (TMK), a high dimensional mapping for Support Vector Machine classification of data generated from probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks from real world datasets. We outperform existing kernels on the distributional features and give the comparative results on non-probabilistic data types.

Software similarity and classification

This thesis analyses software programs in the context of their similarity to other software programs. Applications proposed and implemented include detecting malicious software and discovering security vulnerabilities.

Internet traffic classification using constrained clustering

Statistics-based Internet traffic classification using machine learning techniques has attracted extensive research interest lately, because of the increasing ineffectiveness of traditional port-based and payload-based approaches. In particular, unsupervised learning, that is, traffic clustering, is very important in real-life applications, where labeled training data are difficult to obtain and new patterns keep emerging. Although previous studies have applied some classic clustering algorithms such as K-Means and EM for the task, the quality of resultant traffic clusters was far from satisfactory. In order to improve the accuracy of traffic clustering, we propose a constrained clustering scheme that makes decisions with consideration of some background information in addition to the observed traffic statistics. Specifically, we make use of equivalence set constraints indicating that particular sets of flows are using the same application layer protocols, which can be efficiently inferred from packet headers according to the background knowledge of TCP/IP networking. We model the observed data and constraints using Gaussian mixture density and adapt an approximate algorithm for the maximum likelihood estimation of model parameters. Moreover, we study the effects of unsupervised feature discretization on traffic clustering by using a fundamental binning method. A number of real-world Internet traffic traces have been used in our evaluation, and the results show that the proposed approach not only improves the quality of traffic clusters in terms of overall accuracy and per-class metrics, but also speeds up the convergence.

On addressing the imbalance problem : A correlated KNN approach for network traffic classification

With the arrival of big data era, the Internet traffic is growing exponentially. A wide variety of applications arise on the Internet and traffic classification is introduced to help people manage the massive applications on the Internet for security monitoring and quality of service purposes. A large number of Machine Learning (ML) algorithms are introduced to deal with traffic classification. A significant challenge to the classification performance comes from imbalanced distribution of data in traffic classification system. In this paper, we proposed an Optimised Distance-based Nearest Neighbor (ODNN), which has the capability of improving the classification performance of imbalanced traffic data. We analyzed the proposed ODNN approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments were implemented on the real-world traffic dataset. The results show that the performance of “small classes” can be improved significantly even only with small number of training data and the performance of “large classes” remains stable.

Food security: one of a number of ‘securities’ we need for a full life: an Australian perspective

Although agriculture in Australia is very productive, the current food supply systems in Australia fail to deliver healthy diets to all Australians and fail to protect the natural resources on which they depend. The operation of the food systems creates ‘collateral damage’ to the natural environment including biodiversity loss. In coming decades, Australia’s food supply systems will be increasingly challenged by resource price inflation and climate change. Australia exports more than half of its current agricultural production. Government and business are aiming to substantially increase production to bolster exports. This will increase pressure on agricultural resources and exacerbate ‘collateral’ damage to the environment. The Australian public have a deep and ongoing interest in a very wide range of issues associated with the food systems including the environment, health and sustainability. Food is something we require in order to live and a good diet is something we have to have to be healthy. For health over a life-time we need food security. However, we also require a range of other material goods and social arrangements in order to develop and flourish as human beings. And we need these other things to be secure over a life-time. Food is therefore one security among a range of other securities we need in order to flourish. The paper outlines a number of approaches, as examples, that help to identify what these other goods and arrangements might be. The approaches mentioned in this paper include human rights, national securities, human needs, authentic happiness, capabilities, sustainability and environmental ethics. The different approaches provide a way of evaluating the current situation and indicating a direction for change within the food systems that will address the problems. However, changing large systems such as those involved in food supply is difficult because inertias and vested interests make the current food supply systems resilient to change. The paper suggests that one of the first and ongoing tasks is to develop an understanding of the situation from a comprehensive social–ecological systems perspective. The paper also suggests that a practical leverage point for system change is restructuring the flow of information on the health, natural resources and biodiversity loss issues related to the food supply systems.

Towards designing an email classification system using multi-view based semi-supervised learning

The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training data. However, data labeling is a labor intensive task and requires in-depth domain knowledge. Thus, only a very small proportion of the data can be labeled in practice. This bottleneck greatly degrades the effectiveness of supervised email classification systems. In order to address this problem, in this work, we first identify some critical issues regarding supervised machine learning-based email classification. Then we propose an effective classification model based on multi-view disagreement-based semi-supervised learning. The motivation behind the attempt of using multi-view and semi-supervised learning is that multi-view can provide richer information for classification, which is often ignored by literature, and semi-supervised learning supplies with the capability of coping with labeled and unlabeled data. In the evaluation, we demonstrate that the multi-view data can improve the email classification than using a single view data, and that the proposed model working with our algorithm can achieve better performance as compared to the existing similar algorithms.

Australia-Malaysia security cooperation as a pivotal component for more stable bilateral relations

In the 1980s and 1990s, Australian-Malaysian relations reached a critical juncture due to a series of crises, such as the 1986 capital punishment of convicted drug smugglers Barlow and Chambers, and the 1993 "recalcitrant" jibe by Australian Prime Minister Paul Keating. Following the election of the Howard government in 1996, relations continued to be on a roller coaster with the Malaysian Prime Minister Mahathir Mohamad leading anti-Australia protests over the "Howard Doctrine," the Australian leadership of the 1999 intervention in East Timor, and the "Deputy Sheriff" controversy. Despite this, defense relations between the two remained strong. The success of this cooperation rests on shared political commitment to the security of the region. This article examines the impact that positive cooperation in "high politics" has had in mitigating the negative aspects of crises in "low politics." It argues that close bilateral defense relations have worked to prevent the emergence of further critical junctures in 2012 following the collapse of the Australian-Malaysian refugee swap deal and statements by Australian politicians about Malaysia's poor treatment of asylum seekers, and in 2013 over the overt support by many Australian politicians of the opposition, especially Anwar Ibrahim, during the Malaysian general elections.

Migration and the right to social security: perceptions of off-farm migrants' rights to social insurance in China's Jiangsu Province

In 2001 China ratified the International Covenant on Economic, Social and Cultural Rights. By so doing the national government became legally bound, "to the maximum of its available resources", to achieve "progressively" full realization of the rights specified in the Covenant. Included amongst these entitlements is the "right of everyone to social security, including social insurance". This paper uses data from Jiangsu to examine the extent to which urbanites agree that previously disenfranchised migrants have the same right to social insurance as the urban population. Many urbanites fear that their existing entitlements to social protection will be diluted if social insurance coverage is extended to include new populations. Accordingly, state agencies and the media have sought to promote acceptance of a more positive view of migrant workers than has traditionally prevailed within towns and cities. We find that younger urban residents, urban residents who already have social insurance and urban residents working in the state-owned sector are more likely to agree that migrants have the same right to social insurance as the urban population. © 2007 Institute of World Economics and Politics, Chinese Academy of Social Sciences.

Food security among asylum seekers in Melbourne

Objectives: This research explores food insecurity among asylum seekers who are members of the Asylum Seeker Resource Centre (ASRC) in Melbourne, Australia. Methods: Structured person-assisted questionnaires were conducted with 56 asylum seekers. The questionnaires examined issues around access to food, cultural appropriateness of available food, transport issues, use of the ASRC Foodbank and questions about general health. Results: Findings suggest that: 1) almost all asylum seekers in this study were food insecure; 2) most of the asylum seekers using the ASRC Foodbank have no access to food other than that provided at the centre; and 3) the reason that most asylum seekers are food insecure is related to structural problems associated with limitations imposed by different visas. Conclusions and implications: The ability of asylum seekers to achieve food security is limited by their restricted access to welfare and government or work-related income. Given that the current policy situation is likely to continue, providers such as the ASRC will find continuing demands on their services and increasing pressures to provide more than a 'supplemental' food supply.

Cyber security and protection of ICS systems: An australian example

Many aspects of our modern society now have either a direct or implicit dependence upon information technology. As such, a compromise of the availability or integrity in relation to these systems (which may encompass such diverse domains as banking, government, health care, and law enforcement) could have dramatic consequences from a societal perspective. These key systems are often referred to as critical infrastructure. Critical infrastructure can consist of corporate information systems or systems that control key industrial processes; these specific systems are referred to as ICS (Industry Control Systems) systems. ICS systems have devolved since the 1960s from standalone systems to networked architectures that communicate across large distances, utilise wireless network and can be controlled via the Internet. ICS systems form part of many countries’ key critical infrastructure, including Australia. They are used to remotely monitor and control the delivery of essential services and products, such as electricity, gas, water, waste treatment and transport systems. The need for security measures within these systems was not anticipated in the early development stages as they were designed to be closed systems and not open systems to be accessible via the Internet. We are also seeing these ICS and their supporting systems being integrated into organisational corporate systems.

Surveillance, responsibility and control: an analysis of government and industry discourses about 'problem' and 'responsible' gambling

Background: Discussions of gambling have traditionally focused on ideas of “problem” and “responsible” gambling. However, few studies have examined how Institutions attempt to exert social control over gamblers in order to promote so-called “responsible” behaviour. In this study, we examine the way “problem” and “responsible” gambling are discussed by Australian governments and the gambling industry, using a theoretical framework based on the work of Foucault.

Method: We conducted a thematic analysis of discourses surrounding problem and responsible gambling in government and gambling industry websites, television campaigns and responsible gambling materials.

Results: Documents distinguished between gambling, which was positive for the community, and problem gambling, which was portrayed as harmful and requiring medical intervention. The need for responsible gambling was emphasised in many of the documents, and reinforced by mechanisms including self-monitoring, self-control and surveillance of gamblers.

Conclusions: Government and industry expect gamblers to behave “responsibly”, and are heavily influenced by neoliberal ideas of rational, controlled subjects in their conceptualisation of what constitutes “responsible behaviour”. As a consequence, problem gamblers become constructed as a deviant group. This may have significant consequences for problem gamblers, such as the creation of stigma.

Robust network traffic classification

As a fundamental tool for network management and security, traffic classification has attracted increasing attention in recent years. A significant challenge to the robustness of classification performance comes from zero-day applications previously unknown in traffic classification systems. In this paper, we propose a new scheme of Robust statistical Traffic Classification (RTC) by combining supervised and unsupervised machine learning techniques to meet this challenge. The proposed RTC scheme has the capability of identifying the traffic of zero-day applications as well as accurately discriminating predefined application classes. In addition, we develop a new method for automating the RTC scheme parameters optimization process. The empirical study on real-world traffic data confirms the effectiveness of the proposed scheme. When zero-day applications are present, the classification performance of the new scheme is significantly better than four state-of-the-art methods: random forest, correlation-based classification, semi-supervised clustering, and one-class SVM.

Semi-supervised and compound classification of network traffic

This paper presents a new semi-supervised method to effectively improve traffic classification performance when very few supervised training data are available. Existing semisupervised 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 labelling unlabelled flows according to their correlation to the pre-labelled flows. Consequently, a traffic classifier achieves excellent performance because of the enhanced training data set. 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 significantly outperforms the state-of-the-art flow statistical feature based classification methods. Copyright © 2012 Inderscience Enterprises Ltd.