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Voluntary Counselling and Testing (VCT) is recognized as a critical component of effective HIV prevention initiative and has therefore been promoted nationally and within universities in Kenya. Upon successful counselling and testing those found to be HIV negative are informed to take the necessary measures to protect themselves while the infected are advised to start the required medication. This study examined the attitudes toward VCT services among university students in four Kenyan universities. 980 students filled self administered questionnaires. Results showed that 38.5% of the subjects had tested for HIV in the last 12 months and students (55.8%) felt less susceptible to HIV infection. Findings from a factor analysis revealed that the intention to seek the services was associated with five attitude subscales that were ranked as follows (i) people’s and personal concerns, (ii) friends concerns, (iii) value of testing, (iv) confidentiality and support, and (v) perceived susceptibility. The first three items are associated with stigma which was evidenced in the subjects’ report that admitting that one should test for HIV would imply that one has engaged in immoral behaviour. Secondly, subjects felt that their friends would look down on them if they tested for HIV. Knowing the students’ attitudes will therefore assist in the development of appropriate VCT interventions that will promote HIV testing and behaviour change.

There is need to provide resilient security methodologies that do not require enormous computing resources. While entry prevention is the most viable disposition, it is not always possible to stop unauthorised access. Thus, it is critical to investigate the use of machine learning-based intrusion detection to buttress and provide sufficient security against DOS and other attacks in MANETs. Various anomaly-based intrusion detection systems employ varying techniques to identify anomalies in the context of diverse and valid variables. Most of these techniques, however, fail to capture and take account the physiognomies of MANETs. In the intervening time, usage of the internet of things in the provision of smart healthcare is expanding and the inherent risks snowballing. This study designed a model, which used a fusion of machine learning techniques through both simulation and a running prototype to achieve a more resilient intrusion detection system. The study was designed using functional decomposition methodology and implemented using PPDIO and evaluated on a MANET environment on both Linux NS 2 and further implemented on a network of Smart wearable devices and Raspberry Pi.

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