Malware Detection in Cloud Computing Infrastructures

Cloud services are prominent within the private, public and commercial domains. Many of these services are expected to be always on and have a critical nature; therefore, security and resilience are increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection components of our cloud resilience architecture. More specifically, we exhibit the applicability of novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of features gathered at the system and network levels of a cloud node. We demonstrate that our scheme can reach a high detection accuracy of over 90% whilst detecting various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only system-level data, but also network-level data depending on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is particularly applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware strains with no prior knowledge of their functionality or their underlying instructions. Index Terms—Security, resilience, invasive software, multi-agent systems, network-level security and protection

Assessing the impact of intra-cloud live migration on anomaly detection

Virtualized cloud environments have emerged as a necessity within modern unified ICT infrastructures and have established themselves as a reliable backbone for numerous always-on services. `Live' intra-cloud virtual-machine (VM) migration is a widely used technique for efficient resource management employed within modern cloud infrastructures. Despite the benefits of such functionality, there are still several security issues which have not yet been thoroughly assessed and quantified. We investigate the impact of live virtual-machine migration on state-of-the-art anomaly detection (AD) techniques (namely PCA and K-means), by evaluating live migration under various attack types and intensities. We find that the performance for both detectors degrades as shown by their Receiver Operating Characteristics (ROC) curves when intra-cloud live migration is initiated while VMs are under a netscan (NS) or a denial-of-service (DoS) attack

Julia implementation of the Dynamic Distributed Dimensional Data Model

Julia is a new language for writing data analysis programs that are easy to implement and run at high performance. Similarly, the Dynamic Distributed Dimensional Data Model (D4M) aims to clarify data analysis operations while retaining strong performance. D4M accomplishes these goals through a composable, unified data model on associative arrays. In this work, we present an implementation of D4M in Julia and describe how it enables and facilitates data analysis. Several experiments showcase scalable performance in our new Julia version as compared to the original Matlab implementation

Whatever happened to brownfield land?

No abstract available

Factors associated with the decision to investigate child protective services referrals: a systematic review

Background: Limited resources for child protection create challenging decision situations for child protective services (CPS) workers at the point of intake. A body of research has examined the factors associated with worker decisions and processes using a variety of methodological approaches to gain knowledge on decision-making. However, few attempts have been made to systematically review this literature. Objective: As part of a larger project on decision-making at intake, this systematic review addressed the question of the factors associated with worker decisions to investigate alleged maltreatment referrals. Methods: Quantitative studies that examined factors associated with screening decisions in CPS practice settings were included in the review. Database and other search methods were used to identify research published in English over a 35-year period (1980-2015). Findings: Of 1,147 identified sources, 18 studies were selected for full data extraction. The studies were conducted in the U.S., Canada, and Sweden and varied in methodological quality. Most studies examined case factors with few studies examining other domains. Conclusions: To inform CPS policy and practice, additional research is needed to examine the relationships between decision-making factors and case outcomes. Greater attention needs to be given to the organizational and external factors that influence decision-making

Changes in Achilles Tendon Thickness Following a 10 – Week Heavy Load Eccentric Exercise Program

Achilles tendinopathy (AT) is a common overuse injury in running or jumping activities where the tendon is unable to properly respond to the load. AT increases tendon thickness while decreasing stiffness and function (1). Studies have shown that tendon properties can be positively influenced by load (5,6). Therefore, AT has typically been treated conservatively through physical therapy, with eccentric calf strengthening exercises as the focus of the treatment (2)

Bridging the Gap – Enhancing Private Investment in Future Infrastructure Provision

This research enhances understanding of the infrastructure investment landscape within six countries, Canada, China, India, Singapore, the UK and the US. We find differences in maturity, transparency and openness to international investment across the six countries Through a comprehensive series of interviews with investors, developers, policy makers and advisers we highlight the importance of governments as ‘facilitators’ of private infrastructure investment; providing strategic vision, sustained political commitment and, perhaps most importantly, active project pipelines. The protracted and uncertain nature of infrastructure development pipelines and the complexities in governance frameworks that support infrastructure provision have been considerable barriers to enhanced private investment. Our research has shown that more efficient procurement and investment models for greenfield projects would also enhance private sector investment flows, which in turn would contribute to the re-allocation of the societal and economic benefits associated with new infrastructure provision