Contrasting the perceptions of child testimony experts, prosecutors and police officers regarding individual child abuse interviews

The aim of this study was to compare and contrast the perceptions of various stakeholders regarding a series of interviews about child abuse. Eight focus groups were conducted, each involving a police officer (child abuse investigator), a prosecutor who specializes in child abuse and a child testimony expert. The aim of the focus groups was to discuss the strengths and weaknesses of the specific interviews, which were conducted by the police officer in each focus group. Thematic analysis showed that the prosecutors and child testimony experts were relatively consistent in their perceptions about the need for more free narrative from child witnesses, and to ensure that police officers demonstrate open-mindedness when interviewing children. Differences in priorities and assumptions about the value of various interview techniques, however, were found among the stakeholders. These differences and their practical implications are discussed.

Life satisfaction amongst police officers working in the area of child abuse investigation

Child abuse investigation is an area of work reported to be associated with high levels of work stress. This potentially places professionals at risk of psychological harm and may lead to lower life satisfaction than in the general population. The current study examined this issue within a large sample of Australian police officers. Specifically, 214 officers working in the area of child abuse investigation responded to a single global measure of life satisfaction (LS) known to be highly related to other measures of subjective wellbeing as well as clinical depression. The results revealed that, irrespective of the officers’ gender or degree of exposure to child abuse cases, the mean score from LS score was within the expected adult normative range. Further, the overall incidence of low LS in this sample (1.9 per cent) was not significantly different from the general population (4.3 per cent). The implications of these findings for police organisations are discussed.

Closed vs open corporate social networking communities : a case study of the Victoria Police and Dairy Australia.

The availability of affordable, user-friendly audio-visual equipment, software and the Internet, allows anyone to become a content creator or media outlet. This exploratory case study examines the adoption of social networking by Victoria Police and Diary Australia, a non-media and non-profit organization respectively, in corporate communication, public infmmation and community relations. The paper initiates discussions on the implications for traditional media and audiences of this phenomenon. It content analyzed the two websites during a two-week period and conducted interviews with their moderators about the sites' content, functions and efficacy. The purpose, role and community acceptance of these sites are examined, along with organizational motivations for establishing these channels to reach audiences, bypassing traditional media's gatekeeping function. It highlights how these organizations may set both media and public agendas when traditional media use this web content in their news gathering and reporting, similar to using press releases in the past.

Discriminating DDoS attack traffic from flash crowd through packet arrival patterns

Current DDoS attacks are carried out by attack tools, worms and botnets using different packet-transmission strategies and various forms of attack packets to beat defense systems. These problems lead to defense systems requiring various detection methods in order to identify attacks. Moreover, DDoS attacks can mix their traffics during flash crowds. By doing this, the complex defense system cannot detect the attack traffic in time. In this paper, we propose a behavior based detection that can discriminate DDoS attack traffic from traffic generated by real users. By using Pearson's correlation coefficient, our comparable detection methods can extract the repeatable features of the packet arrivals. The extensive simulations were tested for the accuracy of detection. We then performed experiments with several datasets and our results affirm that the proposed method can differentiate traffic of an attack source from legitimate traffic with a quick response. We also discuss approaches to improve our proposed methods at the conclusion of this paper.

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.

Reliable aggregation on network traffic for web based knowledge discovery

The web is a rich resource for information discovery, as a result web mining is a hot topic. However, a reliable mining result depends on the reliability of the data set. For every single second, the web generate huge amount of data, such as web page requests, file transportation. The data reflect human behavior in the cyber space and therefore valuable for our analysis in various disciplines, e.g. social science, network security. How to deposit the data is a challenge. An usual strategy is to save the abstract of the data, such as using aggregation functions to preserve the features of the original data with much smaller space. A key problem, however is that such information can be distorted by the presence of illegitimate traffic, e.g. botnet recruitment scanning, DDoS attack traffic, etc. An important consideration in web related knowledge discovery then is the robustness of the aggregation method , which in turn may be affected by the reliability of network traffic data. In this chapter, we first present the methods of aggregation functions, and then we employe information distances to filter out anomaly data as a preparation for web data mining.

Hierarchical summarization techniques for network traffic

In today’s high speed networks it is becoming increasingly challenging for network managers to understand the nature of the traffic that is carried in their network. A major problem for traffic analysis in this context is how to extract a concise yet accurate summary of the relevant aggregate traffic flows that are present in network traces. In this paper, we present two summarization techniques to minimize the size of the traffic flow report that is generated by a hierarchical cluster analysis tool. By analyzing the accuracy and compaction gain of our approach on a standard benchmark dataset, we demonstrate that our approach achieves more accurate summaries than those of an existing tool that is based on frequent itemset mining.

Number plate survey methodology for long-distance traffic flows

The lack of comprehensive data on transport operations is a long- standing problem in transport research. Information on road transport in particular has proved difficult to obtain. This Paper documents a study which was aimed at developing and testing a technique to estimate long-distance passenger and freight movements based on direct observation of vehicle movements.

Predicted packet padding for anonymous web browsing against traffic analysis attacks

Anonymous communication has become a hot research topic in order to meet the increasing demand for web privacy protection. However, there are few such systems which can provide high level anonymity for web browsing. The reason is the current dominant dummy packet padding method for anonymization against traffic analysis attacks. This method inherits huge delay and bandwidth waste, which inhibits its use for web browsing. In this paper, we propose a predicted packet padding strategy to replace the dummy packet padding method for anonymous web browsing systems. The proposed strategy mitigates delay and bandwidth waste significantly on average. We formulated the traffic analysis attack and defense problem, and defined a metric, cost coefficient of anonymization (CCA), to measure the performance of anonymization. We thoroughly analyzed the problem with the characteristics of web browsing and concluded that the proposed strategy is better than the current dummy packet padding strategy in theory. We have conducted extensive experiments on two real world data sets, and the results confirmed the advantage of the proposed method.

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

This article presents a qualitative evaluation of a new method of operation for sexual assault investigation developed by Victoria Police. The model is characterised by two core components: the establishment of specialist teams of investigators responsible for investigation and victim support; and the establishment of service sites, referred to as 'Multidisciplinary Centres', where all key services are located in a single building separate from police stations. The research approach consisted of in-depth interviews with 90 stakeholders (police, counsellors, medical officers, child protection workers and prosecutors). Collectively, these interviews revealed strong, unanimous support for the ideologies that underpinned the new reforms. Reported outcomes included the following: improved collaboration; increased victim satisfaction, referrals between professionals and reporting rates; reduced response and investigation times; better quality briefs; and higher prosecution and conviction rates. These findings, along with the stakeholders' suggestions for further improvements, are discussed.

Social media in disaster response : Queensland Police Service - public engagement during the 2011 floods

Social Media, particularly Microblogging services, are now being adopted as an additional tool for emergency service agencies to be able to interact with the community at all stages of a disaster. Unfortunately, no standard framework for Social Media adoption for disaster management exists and emergency service agencies are adopting Social Media in an ad-hoc fashion. This paper seeks to provide a general understanding of how Social Media is being used by emergency service agencies during disasters, to better understand how we might develop a standardised framework of adoption. In this study of the 2010/11 Queensland Flood event, Facebook broadcast messages from the Queensland Police Service to the general public, were analysed by genre. Findings show that these Microblogging activities were mostly about information distribution and warning broadcasts and that the strength of Social Media for two-way communication and collaboration with the general public, was under-utilised during this event.