Pattern for malware remediation – A last line of defence tool against Malware in the global communication platform

Malware is becoming a major problem to every organization that operates on the global communication platform. The malicious software programs are advancing in sophistication in many ways in order to defeat harden deployed defenses. When an organization’s defense fails to keep this malice invasion out, the organization would incur significant amount of risks and damages. Risks include data leakage, inability to operate and tarnished corporate image. Damages include compensation costs to customers and partners, service unavailability and loss of customers’ and partners’ confidence in the organization. This in turn will affect the organization’s business continuity. In order to manage the risks and damages induced by Malware incidents, incident responders are called upon to be the last line of defense against the digital onslaught assault. However incident responders are challenged too by the deep levels of knowledge, skills and experience required to contain the ever advancing and persistent Malware. This paper proposes the establishment of a Pattern template for Malware Remediation to aid incident responders to overcome their competency limitations in order to provide organizations the tool to repel Malware and to reduce the associated risks. Examples and details of the proposed patters are provided with discussions on future direction of the research work

The Campaign to Arrest Ed Shann’s Influence in Western Australia

Shann towered over the discipline of economics in the state of Western Australia in the first third of the twentieth century. He was the foundation professor in history and economics from 1913 to 1931 and inaugural professor of economics from 1931 to 1934 at the University of Western Australia (UWA); he set the curriculum for the subjects that constituted the economics major that was offered at UWA over this period and ensured that it had a market-driven, policy-oriented and historical flavour; he trained a generation of bright young men and women—such as John La Nauze, Nugget Coombs, Merab Harris, Paul Hasluck, Arthur Tange and Alexander Reid—who drew upon his teachings (even when they disagreed with certain elements of it) to guide their actions as servants of the public; he exploited his contacts in the commercial and professional world of Perth to draw men of intellect, but not formal economic training, into the newly established local branch of the Economic Society of Australia and New Zealand in 1925; he established close contacts with local men of finance, including Alfred Davidson of the Bank of New South Wales, in a way that eventually allowed him (and his students!) to provide policy advice at a national level; and he used his power as an administrator, at one time acting as the Vice Chancellor of the university, to establish a faculty of law and a diploma in journalism, both of which thereafter had close associations with the economics discipline at UWA. Shann, in short, created the discipline of economics in Western Australia in his own image. Unfortunately, however, a number of powerful identities in Perth resented the free-market commentaries that Shann dispensed in the public domain and before his students, and hence orchestrated a public campaign to arrest his influence. In this paper I provide an account of Shann’s influence in Western Australia from 1913 to 1934 and trace the campaign waged against him (and economics) which eventually induced him to leave this state

Deep graphical regression for jointly moderate and extreme Australian wildfires

Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. Hazard quantification for extreme wildfires is an important component of wildfire management, as it facilitates efficient resource distribution, adverse effect mitigation, and recovery efforts. However, although extreme wildfires are typically the most impactful, both small and moderate fires can still be devastating to local communities and ecosystems. Therefore, it is imperative to develop robust statistical methods to reliably model the full distribution of wildfire spread. We do so for a novel dataset of Australian wildfires from 1999 to 2019, and analyse monthly spread over areas approximately corresponding to Statistical Areas Level 1 and 2 (SA1/SA2) regions. Given the complex nature of wildfire ignition and spread, we exploit recent advances in statistical deep learning and extreme value theory to construct a parametric regression model using graph convolutional neural networks and the extended generalised Pareto distribution, which allows us to model wildfire spread observed on an irregular spatial domain. We highlight the efficacy of our newly proposed model and perform a wildfire hazard assessment for Australia and population-dense communities, namely Tasmania, Sydney, Melbourne, and Perth.</p

Deep graphical regression for jointly moderate and extreme Australian wildfires

Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. Hazard quantification for extreme wildfires is an important component of wildfire management, as it facilitates efficient resource distribution, adverse effect mitigation, and recovery efforts. However, although extreme wildfires are typically the most impactful, both small and moderate fires can still be devastating to local communities and ecosystems. Therefore, it is imperative to develop robust statistical methods to reliably model the full distribution of wildfire spread. We do so for a novel dataset of Australian wildfires from 1999 to 2019, and analyse monthly spread over areas approximately corresponding to Statistical Areas Level~1 and~2 (SA1/SA2) regions. Given the complex nature of wildfire ignition and spread, we exploit recent advances in statistical deep learning and extreme value theory to construct a parametric regression model using graph convolutional neural networks and the extended generalized Pareto distribution, which allows us to model wildfire spread observed on an irregular spatial domain. We highlight the efficacy of our newly proposed model and perform a wildfire hazard assessment for Australia and population-dense communities, namely Tasmania, Sydney, Melbourne, and Perth

Timing attack detection on BACnet via a machine learning approach

Building Automation Systems (BAS), alternatively known as Building Management Systems (BMS), which centralise the management of building services, are often connected to corporate networks and are routinely accessed remotely for operational management and emergency purposes. The protocols used in BAS, in particular BACnet, were not designed with security as a primary requirement, thus the majority of systems operate with sub-standard or non-existent security implementations. As intrusion is thus likely easy to achieve, intrusion detection systems should be put in place to ensure they can be detected and mitigated. Existing intrusion detection systems typically deal only with known threats (signature-based approaches) or suffer from a high false positive rate (anomaly-based approaches). In this paper we present an overview of the problem space with respect to BAS, and suggest that state aware machine learning techniques could be used to discover threats that comprise a collection of legitimate commands. We provide a first step showing that the concept can be used to detect an attack where legitimate write commands being sent in rapid succession may cause system failure. We capture the state as a ‘time since last write’ event and use a basic artificial neural network classifier to detect attacks

Regional tourist destinations - the role of information and communications technology (ICT) in collaboration amongst tourism providers

The tourism industry can be seen as one of the first business sectors where business functions are almost exclusively using information and communications technologies (ICT). This has impacted on the way in which regional tourism destinations are promoted. The method of promoting regions via the development of regional tourist destination websites or portals using Internet technologies is increasingly being adopted both in Australia and around the world. This paper investigates whether this approach is the most effective to achieve increased awareness and subsequent visitation of a region. Are there other ways to achieve a similar outcome? One such alternative is via a bottom up approach achieved through co-opetition or collaboration established within the group of local tourism industry operators. This cooperative networking is made possible via the use of ICT to facilitate the establishment of virtual business networks amongst tourism operators in a local community, cascading into an informal secondary tourism network within that region. In many Australian regional areas the tourism bureau has been the key node for local tourism, but this structure has been fraught with many problems. Little is known about their effectiveness in delivering services to local small and medium tourism enterprises (SMTEs). The role of tourism bureaus in local tourism networks is changing and a study of this dynamic is provided here as an example of the interaction between top down and bottom up approaches. Published case studies from around the world are considered demonstrating alternative approaches to using ICT to promote a region and communicate with potential visitors. Future empirical research is required to more fully understand the effectiveness of the different approaches

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

Dynamical Networks of Social Influence: Modern Trends and Perspectives

Dynamics and control of processes over social networks, such as the evolution of opinions, social influence and interpersonal appraisals, diffusion of information and misinformation, emergence and dissociation of communities, are now attracting significant attention from the broad research community that works on systems, control, identification and learning. To provide an introduction to this rapidly developing area, a Tutorial Session was included into the program of IFAC World Congress 2020. This paper provides a brief summary of the three tutorial lectures, covering the most “mature” directions in analysis of social networks and dynamics over them: 1) formation of opinions under social influence; 2) identification and learning for analysis of a network’s structure; 3) dynamics of interpersonal appraisals