Publications

An increase in growth and use of the internet has also resulted in attacks evolving and more novel attacks having a devastating effect are witnessed. The Intrusion Detection System (IDS) is yet to achieve maximum success due to false positives and low detection. The purpose of the study was to determine the modelling of an intrusion detection system using the Asynchronous Advantage Actor-Critic (A3C) Algorithm. In this paper we look at the following: (i) To evaluate the current machine learning techniques being used in IDS, (ii) To determine the effectiveness of using the Asynchronous Advantage Actor-Critic algorithm in anomaly detection, (iii) To select the appropriate training data set and prepare for use on A3C. A conceptual study was done in looking at these objectives. The UNSW_TRAIN and UNSW_TEST were samples selected by purposive sampling from the whole population of UNSW-NB15 dataset. Analysis of the dataset was done using Python. Key findings were that anomaly detection approach is the best approach due to its ability to detect novel attacks. Also, there is need to continue research on intrusion detection and improve solutions to the problem of false positives and fully optimize on accuracy. The UNSW-NB15 dataset is comprehensive and so all the attack types should be used so as to accurately depict the intrusions and should selected attack types be used, feature selection should be done accurately so as to reflect modern attack types.

Climate change is a major drawback to food security in most developing countries. Promoting minimum tillage and climate-smart crops is essential in mitigating and adapting to climate shocks. However, information on the impacts of minimum tillage on crop productivity under farmers' conditions is limited in Western Kenya. We assessed the effects of minimum tillage adoption on sorghum productivity among smallholder sorghum farmers in Western Kenya. We used household survey data collected from 300 smallholder farmers and performed an endogenous switching regression model to analyze the effects of minimum tillage adoption on sorghum yields. The results revealed that the adoption of minimum tillage increased sorghum yields by 11%, from 1163 to 1146 kg ha−1. The occupation of the household head, acreage, soil fertility perception, and farm credit significantly and positively determined minimum tillage adoption. The remittance, agricultural associations, weather information, and site negatively and significantly determined minimum tillage adoption. Our findings suggest that minimum tillage adoption under drought-tolerant crops such as sorghum could improve community wellbeing through increased crop productivity, notwithstanding the changing climate and associated weather shocks.