DDoS attack detection at local area networks using information theoretical metrics

DDoS attacks are one of the major threats to Internet services. Sophisticated hackers are mimicking the features of legitimate network events, such as flash crowds, to fly under the radar. This poses great challenges to detect DDoS attacks. In this paper, we propose an attack feature independent DDoS flooding attack detection method at local area networks. We employ flow entropy on local area network routers to supervise the network traffic and raise potential DDoS flooding attack alarms when the flow entropy drops significantly in a short period of time. Furthermore, information distance is employed to differentiate DDoS attacks from flash crowds. In general, the attack traffic of one DDoS flooding attack session is generated by many bots from one botnet, and all of these bots are executing the same attack program. As a result, the similarity among attack traffic should higher than that among flash crowds, which are generated by many random users. Mathematical models have been established for the proposed detection strategies. Analysis based on the models indicates that the proposed methods can raise the alarm for potential DDoS flooding attacks and can differentiate DDoS flooding attacks from flash crowds with conditions. The extensive experiments and simulations confirmed the effectiveness of our proposed detection strategies.

Feature-based image fusion quality metrics

Image fusion quality metrics have evolved from image processing quality metrics. They measure the quality of fused images by estimating how much localized information has been transferred from the source images into the fused image. However, this technique assumes that it is actually possible to fuse two images into one without any loss. In practice, some features must be sacrificed and relaxed in both source images. Relaxed features might be very important, like edges, gradients and texture elements. The importance of a certain feature is application dependant. This paper presents a new method for image fusion quality assessment. It depends on estimating how much valuable information has not been transferred.

Discriminating DDoS flows from flash crowds using information distance

Discriminating DDoS flooding attacks from flash crowds poses a tough challenge for the network security community. Because of the vulnerability of the original design of the Internet, attackers can easily mimic the patterns of legitimate network traffic to fly under the radar. The existing fingerprint or feature based algorithms are incapable to detect new attack strategies. In this paper, we aim to differentiate DDoS attack flows from flash crowds. We are motivated by the following fact: the attack flows are generated by the same prebuilt program (attack tools), however, flash crowds come from randomly distributed users all over the Internet. Therefore, the flow similarity among DDoS attack flows is much stronger than that among flash crowds. We employ abstract distance metrics, the Jeffrey distance, the Sibson distance, and the Hellinger distance to measure the similarity among flows to achieve our goal. We compared the three metrics and found that the Sibson distance is the most suitable one for our purpose. We apply our algorithm to the real datasets and the results indicate that the proposed algorithm can differentiate them with an accuracy around 65%.

Health information technology governance : a perspective on investment decision processes

Realizing value from IT investments continues to be a challenge for most healthcare organizations. IT governance (ITG) is envisaged to solve many of these challenges. ITG is the practice that establishes accountability framework for IT investments by allocating decision rights among major participants involved in IT decision processes. As ITG is relatively new in healthcare industry, it is expected that knowledge about how healthcare organizations govern their IT decisions is limited. This research aims to extend this knowledge and to assist both researchers and professionals by providing insights on how IT decisions are made and governed in healthcare organizations (HOs). This research adopts case-study methodology to investigate IT governance in two distinctly different HOs. The research findings indicate that HOs implement ITG to achieve alignment between business objectives and IT. Both HOs set up a five-stage IT decision process to identify, evaluate and prioritize IT investment ideas. They also established generic committee-structures that clearly defined roles and decision authorities to govern such process. It is suggested here that ITG in HOs is heavily influenced by strategic priorities, organizational structure, governance experience and governmental initiatives. Effective ITG in HOs is challenged by IT alignment, policy government, involvement of healthcare executives, and lack of business metrics to justify and evaluate decisions. The research proposes recommendations to address these challenges.

Image fusion metrics : evolution in a nutshell

Image fusion process merges two images into a single more informative image. Objective image fusion per- formance metrics rely primarily on measuring the amount of information transferred from each source image into the fused image. Objective image fusion metrics have evolved from image processing dissimilarity metrics. Additionally, researchers have developed many additions to image dissimilarity metrics in order to better value the local fusion worthy features in source images. This paper studies the evolution of objective image fusion performance metrics and their subjective and objective validation. It describes how a fusion performance metric evolves starting with image dissimilarity metrics, its realization into image fusion contexts, its localized weighting factors and the validation process.

Biological, ecological, conservation and legal information for all species and subspecies of Australian bird

We introduce a dataset of biological, ecological, conservation and legal information for every species and subspecies of Australian bird, 2056 taxa or populations in total. Version 1 contains 230 fields grouped under the following headings: Taxonomy & nomenclature, Phylogeny, Australian population status, Conservation status, Legal status, Distribution, Morphology, Habitat, Food, Behaviour, Breeding, Mobility and Climate metrics. It is envisaged that the dataset will be updated periodically with new data for existing fields and the addition of new fields. The dataset has already had, and will continue to have applications in Australian and international ornithology, especially those that require standard information for a large number of taxa.

Dimensions and metrics for evaluating recommendation systems

Recommendation systems support users and developers of various computer and software systems to overcome information overload, perform information discovery tasks, and approximate computation, among others. They have recently become popular and have attracted a wide variety of application scenarios ranging from business process modeling to source code manipulation. Due to this wide variety of application domains, different approaches and metrics have been adopted for their evaluation. In this chapter, we review a range of evaluation metrics and measures as well as some approaches used for evaluating recommendation systems. The metrics presented in this chapter are grouped under sixteen different dimensions, e.g., correctness, novelty, coverage. We review these metrics according to the dimensions to which they correspond. A brief overview of approaches to comprehensive evaluation using collections of recommendation system dimensions and associated metrics is presented. We also provide suggestions for key future research and practice directions.

The usefulness metrics of the most popular eReader used by higher education students

In the digital technology era, mobile devices have an important rule to deploy a copy of data and information through the network. An electronic reader (eReader) allows readers to read written materials in an electronic manner that is available in many models. The objective of this study is to evaluate the usage of eReader by higher education students. We firstly identified the most frequently used eReader by surveying higher education students. The survey results showed that Apple iPad, Amazon Kindle, and Samsung Tablet are the most popular eReader devices used by higher education students. We presented these results, and then we analyzed the surveyed results in detail in order to develop an evaluation metric of the eReader in a mobile platform that clearly allows the selection of the most suitable eReader for higher education students. The main contribution of this paper is the development of a set of criteria that can be used by students in the selection of an eReader that matches their specific needs and requirements.