Predicting Phishing Websites using Neural Network trained with Back-Propagation

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates

Issues for computer modelling of room acoustics in non-concert hall settings

The basic principle of common room acoustics computer models is the energy-based geometrical room acoustics theory. The energy-based calculation relies on the averaging effect provided when there are many reflections from many different directions, which is well suited for large concert halls at medium and high frequencies. In recent years computer modelling has become an established tool in architectural acoustics design thanks to the advance in computing power and improved understanding of the modelling accuracy. However concert hall is only one of many types of built environments that require good acoustic design. Increasingly computer models are being sought for non-concert hall applications, such as in small rooms at low frequencies, flat rooms in workplace surroundings, and long enclosures such as underground stations. In these built environments the design issues are substantially difference from that of concert halls and in most cases the common room acoustics models will needed to be modified or totally re-formulated in order to deal with these new issues. This paper looks at some examples of these issues. In workplace environments we look at the issues of directional propagation and volume scattering by furniture and equipment instead of the surface scattering that is common assumed in concert hall models. In small rooms we look at the requirement of using wave models, such as boundary element models, or introducing phase information into geometrical room acoustics models to determine wave behaviours. Of particular interest is the ability of the wave models to provide phase information that is important not only for room modes but for the construction of impulse response for auralisation. Some simulated results using different modelling techniques will be presented to illustrate the problems and potential solutions

Training Process Reduction Based On Potential Weights Linear Analysis To Accelarate Back Propagation Network

Learning is the important property of Back Propagation Network (BPN) and finding the suitable weights and thresholds during training in order to improve training time as well as achieve high accuracy. Currently, data pre-processing such as dimension reduction input values and pre-training are the contributing factors in developing efficient techniques for reducing training time with high accuracy and initialization of the weights is the important issue which is random and creates paradox, and leads to low accuracy with high training time. One good data preprocessing technique for accelerating BPN classification is dimension reduction technique but it has problem of missing data. In this paper, we study current pre-training techniques and new preprocessing technique called Potential Weight Linear Analysis (PWLA) which combines normalization, dimension reduction input values and pre-training. In PWLA, the first data preprocessing is performed for generating normalized input values and then applying them by pre-training technique in order to obtain the potential weights. After these phases, dimension of input values matrix will be reduced by using real potential weights. For experiment results XOR problem and three datasets, which are SPECT Heart, SPECTF Heart and Liver disorders (BUPA) will be evaluated. Our results, however, will show that the new technique of PWLA will change BPN to new Supervised Multi Layer Feed Forward Neural Network (SMFFNN) model with high accuracy in one epoch without training cycle. Also PWLA will be able to have power of non linear supervised and unsupervised dimension reduction property for applying by other supervised multi layer feed forward neural network model in future work.Comment: 11 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact factor 0.42

Forecasting the Spreading of Technologies in Research Communities

Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution

Data-driven approach to machine condition prognosis using least square regression trees

Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao’s method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis

A functional analysis of change propagation

A thorough understanding of change propagation is fundamental to effective change management during product redesign. A new model of change propagation, as a result of the interaction of form and function is presented and used to develop an analysis method that determines how change is likely to propagate. The analysis produces a Design Structure Matrix, which clearly illustrates change propagation paths and highlights connections that could otherwise be ignored. This provides the user with an in-depth knowledge of product connectivity, which has the potential to support the design process and reduce the product's susceptibility to future change

Towards a change process planning tool

The relationship between a product and its design process is generally complex and not fully understood. When modifying a product, industry still rarely considers the implementation process and its consequences for other design activities in the company, which is hard to assess with conventional planning methods. Although change processes are highly constrained, product and process constraints are not usually considered together or traded off against each other when planning the change. Inadequate assessment and planning of the change implementation process can lead to costly knock-on effects across the product and the design process. This paper argues for a combination of change and process research and discusses requirements for a change process planning tool. It proposes a system for the analysis of the impact of change on the product as well as other company activities. Then, a more informed selection between change alternatives is possible

Mechanical MNIST: A benchmark dataset for mechanical metamodels

Metamodels, or models of models, map defined model inputs to defined model outputs. Typically, metamodels are constructed by generating a dataset through sampling a direct model and training a machine learning algorithm to predict a limited number of model outputs from varying model inputs. When metamodels are constructed to be computationally cheap, they are an invaluable tool for applications ranging from topology optimization, to uncertainty quantification, to multi-scale simulation. By nature, a given metamodel will be tailored to a specific dataset. However, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present, the most pragmatic metamodel selection for dealing with mechanical data has not been thoroughly explored. Drawing inspiration from the benchmark datasets available to the computer vision research community, we introduce a benchmark data set (Mechanical MNIST) for constructing metamodels of heterogeneous material undergoing large deformation. We then show examples of how our benchmark dataset can be used, and establish baseline metamodel performance. Because our dataset is readily available, it will enable the direct quantitative comparison between different metamodeling approaches in a pragmatic manner. We anticipate that it will enable the broader community of researchers to develop improved metamodeling techniques for mechanical data that will surpass the baseline performance that we show here.Accepted manuscrip