Public Attitudes Towards Surveillance and Privacy in Croatia

This paper investigates public attitudes towards surveillance and privacy in Croatia. It segments the respondents based on their views on surveillance and privacy, and examines differences between them with regard to their demographic characteristics. The empirical analysis is based on data obtained from a public opinion survey. The data were analyzed using descriptive statistics, exploratory and confirmatory factor analysis, Cronbach alpha calculation, chi-square test, and cluster analysis. The factor analysis showed six distinct factors: (1) perceived surveillance effectiveness, (2) concern about being surveilled, (3) trust in privacy protection procedures, (4) concern about CCTV privacy intrusion, (5) concern about personal data manipulation, and (6) a need for surveillance enforcement. K-means cluster analysis indicated the following three groups of citizens: pro-surveillance oriented citizens, citizens concerned about being surveilled, and citizens concerned about data and privacy protection. Significant differences between the groups were found in age and education, while no significant differences exist in gender, employment status, and household income. The findings of this study support the existence of different groups of citizens regarding their attitudes towards surveillance and privacy.surveillance, privacy concern, public opinion, segmentation, demographic characteristics, Croatia

Towards automated incident handling: how to select an appropriate response against a network-based attack?

The increasing amount of network-based attacks evolved to one of the top concerns responsible for network infrastructure and service outages. In order to counteract these threats, computer networks are monitored to detect malicious traffic and initiate suitable reactions. However, initiating a suitable reaction is a process of selecting an appropriate response related to the identified network-based attack. The process of selecting a response requires to take into account the economics of an reaction e.g., risks and benefits. The literature describes several response selection models, but they are not widely adopted. In addition, these models and their evaluation are often not reproducible due to closed testing data. In this paper, we introduce a new response selection model, called REASSESS, that allows to mitigate network-based attacks by incorporating an intuitive response selection process that evaluates negative and positive impacts associated with each countermeasure. We compare REASSESS with the response selection models of IE-IRS, ADEPTS, CS-IRS, and TVA and show that REASSESS is able to select the most appropriate response to an attack in consideration of the positive and negative impacts and thus reduces the effects caused by an network-based attack. Further, we show that REASSESS is aligned to the NIST incident life cycle. We expect REASSESS to help organizations to select the most appropriate response measure against a detected network-based attack, and hence contribute to mitigate them

Performance of Machine Learning and Big Data Analytics paradigms in Cybersecurity and Cloud Computing Platforms

The purpose of the research is to evaluate Machine Learning and Big Data Analytics paradigms for use in Cybersecurity. Cybersecurity refers to a combination of technologies, processes and operations that are framed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access. The main characteristic of Machine Learning (ML) is the automatic data analysis of large data sets and production of models for the general relationships found among data. ML algorithms, as part of Artificial Intelligence, can be clustered into supervised, unsupervised, semi-supervised, and reinforcement learning algorithms

Alcohol, assault and licensed premises in inner-city areas

This report contains eight linked feasibility studies conducted in Cairns during 2010. These exploratory studies examine the complex challenges of compiling and sharing information about incidents of person-to-person violence in a late night entertainment precinct (LNEP). The challenges were methodological as well as logistical and ethical. The studies look at how information can be usefully shared, while preserving the confidentiality of those involved. They also examine how information can be compiled from routinely collected sources with little or no additional resources, and then shared by the agencies that are providing and using the information.Although the studies are linked, they are also stand-alone and so can be published in peer-reviewed literature. Some have already been published, or are ‘in press’ or have been submitted for review. Others require the NDLERF board’s permission to be published as they include data related more directly to policing, or they include information provided by police.The studies are incorporated into the document under section headings. In each section, they are introduced and then presented in their final draft form. The final published form of each paper, however, is likely to be different from the draft because of journal and reviewer requirements. The content, results and implications of each study are discussed in summaries included in each section.Funded by the National Drug Law Enforcement Research Fund, an initiative of the National Drug StrategyAlan R Clough (PhD) School of Public Health, Tropical Medicine and Rehabilitation Sciences James Cook UniversityCharmaine S Hayes-Jonkers (BPsy, BSocSci (Hon1)) James Cook University, Cairns.Edward S Pointing (BPsych) James Cook University, Cairns

Evolution of security engineering artifacts: a state of the art survey

Security is an important quality aspect of modern open software systems. However, it is challenging to keep such systems secure because of evolution. Security evolution can only be managed adequately if it is considered for all artifacts throughout the software development lifecycle. This article provides state of the art on the evolution of security engineering artifacts. The article covers the state of the art on evolution of security requirements, security architectures, secure code, security tests, security models, and security risks as well as security monitoring. For each of these artifacts the authors give an overview of evolution and security aspects and discuss the state of the art on its security evolution in detail. Based on this comprehensive survey, they summarize key issues and discuss directions of future research

Analysis of update delays in signature-based network intrusion detection systems

Network Intrusion Detection Systems (NIDS) monitor network traffic looking for attempts to compromise the security of the system they protect. Signature-based NIDS rely on a set of known attack patterns to match malicious traffic. Accordingly, they are unable to detect a specific attack until a specific signature for the corresponding vulnerability is created, tested, released and deployed. Although vital, the delay in the updating process of these systems has not been studied in depth. This paper presents a comprehensive statistical analysis of this delay in relation to the vulnerability disclosure time, the updates of vulnerability detection systems (VDS), the software patching releases and the publication of exploits. The widely deployed NIDS Snort and its detection signatures release dates have been used. Results show that signature updates are typically available later than software patching releases. Moreover, Snort rules are generally released within the first 100 days from the vulnerability disclosure and most of the times exploits and the corresponding NIDS rules are published with little difference. Implications of these results are drawn in the context of security policy definition. This study can be easily kept up to date due to the methodology used.Publicad

A Cloud-based Intrusion Detection and Prevention System for Mobile Voting in South Africa

Publishe ThesisInformation and Communication Technology (ICT) has given rise to new technologies and solutions that were not possible a few years ago. One of these new technologies is electronic voting, also known as e-voting, which is the use of computerised equipment to cast a vote. One of the subsets of e-voting is mobile voting (m-voting). M-voting is the use of mobile phones to cast a vote outside the restricted electoral boundaries. Mobile phones are pervasive; they offer connection anywhere, at any time. However, utilising a fast-growing medium such as the mobile phone to cast a vote, poses various new security threats and challenges. Mobile phones utilise equivalent software design used by personal computers which makes them vulnerable or exposed to parallel security challenges like viruses, Trojans and worms. In the past, security solutions for mobile phones encountered several restrictions in practice. Several methods were used; however, these methods were developed to allow lightweight intrusion detection software to operate directly on the mobile phone. Nevertheless, such security solutions are bound to fail securing a device from intrusions as they are constrained by the restricted memory, storage, computational resources, and battery power of mobile phones. This study compared and evaluated two intrusion detection systems (IDSs), namely Snort and Suricata, in order to propose a cloud-based intrusion detection and prevention system (CIDPS) for m-voting in South Africa. It employed simulation as the primary research strategy to evaluate the IDSs. A quantitative research method was used to collect and analyse data. The researcher established that as much as Snort has been the preferred intrusion detection and prevention system (IDPS) in the past, Suricata presented more effective and accurate results close to what the researcher anticipated. The results also revealed that, though Suricata was proven effective enough to protect m-voting while saving the computational resources of mobile phones, more work needs to be done to alleviate the false-negative alerts caused by the anomaly detection method. This study adopted Suricata as a suitable cloud-based analysis engine to protect a mobile voting application like XaP

Information Security Management: A System Dynamics Approach

Managing security for information assets presents a challenging task. The need for effective information security management assumes greater importance with growing reliance on distributed systems and Internet-accessible systems. Many factors play a role in determining the vulnerability of information assets to security threats. Using a system dynamics approach, this study evaluates information security management strategies from a financial and asset loss perspective, with a view to providing managers guidance for information security decisions