Polymorphic malware has evolved as a major threat in Computer Systems. Their creation technology is constantly evolving using sophisticated tactics to create multiple instances of the existing ones. Current solutions are not yet able to sufficiently address this problem. They are mostly signature based; however, a changing malware means a changing signature. They, therefore, easily evade detection. Classifying them into their respective families is also hard, thus making elimination harder. In this paper, we propose a new feature engineering (NFE) approach for a better classification of polymorphic malware based on a hybrid of structural and behavioural features. We use accuracy, recall, precision, and F score to evaluate our approach. We achieve an improvement of 12% on accuracy between raw features and NFE features. We also demonstrated the robustness of NFE on feature selection as compared to other feature selection techniques.
Rainfall extremes have strong connotations to socioeconomic activities and human well-being in Uganda's Lake Victoria Basin (LVB). Reliable prediction and dissemination of extreme rainfall events are therefore of paramount importance to the region's development agenda. The main objective of this study was to contribute to the prediction of rainfall extremes over this region using a numerical modelling approach. The Weather Research and Forecasting (WRF) model was used to simulate a 20-day period of extremely heavy rainfall that was observed in the March to May season of 2008. The underlying interest was to investigate the performance of different combinations of cumulus and microphysical parameterization along with the model grid resolution and domain size. The model output was validated against rainfall observations from the Tropical Rainfall Measuring Mission (TRMM) using 5 metrics; the rainfall distribution, root mean square error, mean error, probability of detection and false alarm ratio. The results showed that the model was able to simulate extreme rainfall and the most satisfactory skill was obtained with a model setup using the Grell 3D cumulus scheme combined with the SBU_YLin microphysical scheme. This study concludes that the WRF model can be used for simulating extreme rainfall over western LVB. In the other 2 regions, central and eastern LVB, its performance is limited by failure to simulate nocturnal rainfall. Furthermore, increasing the model grid resolution showed good potential for improving the model simulation especially when a large domain is used. #
The changing climate has negatively impacted food systems by affecting rainfall patterns and leading to drought, flooding, and higher temperatures which reduce food production. This study examined the ability of communities to cope with food insecurity due to the changing climate in the Serere and Buyende districts, which are two different agro-ecological zones of Uganda. We administered 806 questionnaires to households, a sample size which was determined using Yamane’s formula, with the snowball sampling method used to select the households. The questionnaire sought information, including that regarding the respondents’ resources, the effects of climate change on households, and the coping mechanisms employed to reduce the impact of climate change on food security. The data collected was coded and analyzed using the statistical package for the social sciences (SPSS). Agriculture was found to be the main source of income for 42.4% of male adults and 41.2% of female adults in Serere. In Buyende, 39.9% of males and 33.7% of females rely on selling animal, poultry, and food crops. Aggregate results further showed that 58.3% of females and 42.2% of the males from both districts had suffered from the impacts of climate change, and that the effects were more evident between March and May, when communities experienced crop failure. The study further found that the percentage of households who had three meals a day was reduced from 59.7% to 43.6%, while the number of households with no major meals a day increased from 1.3% to 1.6%. We also found that 34.3% of households reported buying food during periods of crop failure or food scarcity. Moreover, despite reporting an understanding of several coping mechanisms, many households were limited in their ability to implement the coping mechanisms by their low incomes. This reinforced their reliance on affordable mechanisms, such as growing drought-resistant crops (32.7%), rearing drought-resistant livestock breeds (26.1%), and reducing the number of meals a day (14.5%), which are mechanisms that are insufficient for solving all the climate-related food insecurity challenges. We recommend that the government intervenes by revising policies which help farmers cope with the negative effects of climate change, promoting the sensitization of farmers to employing the coping mechanisms, and subsidizing agricultural inputs, such as resistant varieties of crops, for all to afford.
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