Intelligent Phishing Detection Scheme Using Deep Learning Algorithms

Purpose: Phishing attacks have evolved in recent years due to high-tech-enabled economic growth worldwide. The rise in all types of fraud loss in 2019 has been attributed to the increase in deception scams and impersonation, as well as to sophisticated online attacks such as phishing. The global impact of phishing attacks will continue to intensify, and thus, a more efficient phishing detection method is required to protect online user activities. To address this need, this study focussed on the design and development of a deep learning-based phishing detection solution that leveraged the universal resource locator and website content such as images, text and frames. Design/methodology/approach: Deep learning techniques are efficient for natural language and image classification. In this study, the convolutional neural network (CNN) and the long short-term memory (LSTM) algorithm were used to build a hybrid classification model named the intelligent phishing detection system (IPDS). To build the proposed model, the CNN and LSTM classifier were trained by using 1m universal resource locators and over 10,000 images. Then, the sensitivity of the proposed model was determined by considering various factors such as the type of feature, number of misclassifications and split issues. Findings: An extensive experimental analysis was conducted to evaluate and compare the effectiveness of the IPDS in detecting phishing web pages and phishing attacks when applied to large data sets. The results showed that the model achieved an accuracy rate of 93.28% and an average detection time of 25 s. Originality/value: The hybrid approach using deep learning algorithm of both the CNN and LSTM methods was used in this research work. On the one hand, the combination of both CNN and LSTM was used to resolve the problem of a large data set and higher classifier prediction performance. Hence, combining the two methods leads to a better result with less training time for LSTM and CNN architecture, while using the image, frame and text features as a hybrid for our model detection. The hybrid features and IPDS classifier for phishing detection were the novelty of this study to the best of the authors' knowledge

Efficacy and effectiveness of dihydroartemisinin-piperaquine versus artesunate-mefloquine in falciparum malaria: an open-label randomised comparison.

BACKGROUND: Artemisinin-based combinations are judged the best treatments for multidrug-resistant Plasmodium falciparum malaria. Artesunate-mefloquine is widely recommended in southeast Asia, but its high cost and tolerability profile remain obstacles to widespread deployment. To assess whether dihydroartemisinin-piperaquine is a suitable alternative to artesunate-mefloquine, we compared the safety, tolerability, efficacy, and effectiveness of the two regimens for the treatment of uncomplicated falciparum in western Myanmar (Burma). METHODS: We did an open randomised comparison of 3-day regimens of artesunate-mefloquine (12/25 mg/kg) versus dihydroartemisinin-piperaquine (6.3/50 mg/kg) for the treatment of children aged 1 year or older and in adults with uncomplicated falciparum malaria in Rakhine State, western Myanmar. Within each group, patients were randomly assigned supervised or non-supervised treatment. The primary endpoint was the PCR-confirmed parasitological failure rate by day 42. Failure rates at day 42 were estimated by Kaplan-Meier survival analysis. This study is registered as an International Standard Randomised Controlled Trial, number ISRCTN27914471. FINDINGS: Of 652 patients enrolled, 327 were assigned dihydroartemisinin-piperaquine (156 supervised and 171 not supervised), and 325 artesunate-mefloquine (162 and 163, respectively). 16 patients were lost to follow-up, and one patient died 22 days after receiving dihydroartemisinin-piperaquine. Recrudescent parasitaemias were confirmed in only two patients; the day 42 failure rate was 0.6% (95% CI 0.2-2.5) for dihydroartemisinin-piperaquine and 0 (0-1.2) for artesunate-mefloquine. Whole-blood piperaquine concentrations at day 7 were similar for patients with observed and non-observed dihydroartemisinin-piperaquine treatment. Gametocytaemia developed more frequently in patients who had received dihydroartemisinin-piperaquine than in those on artesunate-mefloquine: day 7, 18 (10%) of 188 versus five (2%) of 218; relative risk 4.2 (1.6-11.0) p=0.011. INTERPRETATION: Dihydroartemisinin-piperaquine is a highly efficacious and inexpensive treatment of multidrug-resistant falciparum malaria and is well tolerated by all age groups. The effectiveness of the unsupervised treatment, as in the usual context of use, equalled its supervised efficacy, indicating good adherence without supervision. Dihydroartemisinin-piperaquine is a good alternative to artesunate-mefloquine

Poverty among households living in slum area of Hlaing Tharyar Township, Yangon City, Myanmar

Background: Slums can be regarded as physical manifestations of urban poverty. Although the world has made dramatic improvement in reducing poverty since 1990, poverty still persists at an unacceptable level. Although current situations highlights the importance of slum areas to be given priority in poverty alleviation, there are limited data on poverty level among people living in urban slums of Myanmar.Methods: A cross-sectional study was conducted among households living in slum areas of Hlaing Tharyar Township, Yangon City, Myanmar during 2016. Multi-staged systematic random sampling and face-to-face interview were applied in selecting the samples and collecting the data, respectively. The new global poverty line (1.9 USD per person per day) was used as a threshold in determining the poverty. Chi-squared test and multivariate logistic regression analysis were utilized in data analysis.Results: Altogether 254 participants were recruited after getting informed consent. The occurrence of poverty among households was 54.3% (95% CI: 48.2%, 60.5%). Head counts of poverty among study population was 58.8%. The education status of household’s head, size of household and the presence of less than 15 years old children in the household were detected as significant determinants of being poor household.Conclusions: Poverty among households living in slum area of Hlaing Tharyar Township, Yangon City was high. Measures to alleviate poverty in urban slums should be intensified. Education level of household’s heads should be improved. Family planning or birth spacing programme should also be strengthened, especially in urban slums.

Advances in crowd analysis for urban applications through urban event detection

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined

Soft-shell mud crab farming

Farming of soft-shell mud crab (Scylla serrata) has been practiced for some time now in a number of Asian countries. Because of its profitability, there is an increasing interest to engage in this aquaculture business venture. Crabs collected from the wild are the major source of stocks for farming. However, the use of hatchery reared crabs is encouraged so as not to deplete the wild population. Although communal rearing of crabs for soft-shell crab production in cages or in tanks is also practiced, this manual describes the individual rearing of crabs in boxes based on experience in Ranong, Thailand. The techniques can be modified depending on the site. This manual provides a section on the biology of mud crab that includes species identification, molting, and autotomy and regeneration which discloses important information related to the management of soft-shell crab farming. This is followed by a detailed discussion on the setting up and management of the farm for soft-shell crabs. The basis for the computation of cost and return analysis is included under the section on profitability. Cost of materials and labor varies in each country hence only the materials needed and other technical assumptions are listed as basis for computation

A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. In this paper, we studied the extended Markowitz's mean-variance portfolio optimization model. We considered the cardinality, quantity, pre-assignment and round lot constraints in the extended model. These four real-world constraints limit the number of assets in a portfolio, restrict the minimum and maximum proportions of assets held in the portfolio, require some specific assets to be included in the portfolio and require to invest the assets in units of a certain size respectively. An efficient learning-guided hybrid multi-objective evolutionary algorithm is proposed to solve the constrained portfolio optimization problem in the extended mean-variance framework. A learning-guided solution generation strategy is incorporated into the multi-objective optimization process to promote the efficient convergence by guiding the evolutionary search towards the promising regions of the search space. The proposed algorithm is compared against four existing state-of-the-art multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-2), Pareto Envelope-based Selection Algorithm (PESA-II) and Pareto Archived Evolution Strategy (PAES). Computational results are reported for publicly available OR-library datasets from seven market indices involving up to 1318 assets. Experimental results on the constrained portfolio optimization problem demonstrate that the proposed algorithm significantly outperforms the four well-known multi-objective evolutionary algorithms with respect to the quality of obtained efficient frontier in the conducted experiments