Users’ privacy on social media platforms continues to be important as users face numerous threats to their personal data. Social media sites such as Facebook store large amounts of users’ personal data which make such sites prime targets for hackers. Research has shown that users have been subjected to privacy attacks in which hacked personal data are sold to online marketers. These incidents have prompted the need to protect users’ privacy against data theft by third parties. We investigated the privacy risks that social media users on Facebook face when online. The privacy awareness of regular users of Facebook was evaluated through the observation of their online activities. Facebook was selected as a case study because it is the largest and most popular social media platform in South Africa. A sample group of Facebook users was selected for this study based on their activeness (or frequency of posting, uploaded or liking) on the site. Findings indicate that users’ personal data can be obtained as they are publicly available on Facebook. The implication of this finding is that users lack adequate awareness on protection tools designed to protect their personal data, and as a result, they risk losing their data and privacy. Significance: • This study serves as an assessment tool for the privacy and security features of the social media site Facebook. This assessment tool can help users of social media sites to evaluate their own behaviour and usage patterns on Facebook. It can also assist social media site designers in considering the effectiveness of current measures, which are designed to ensure that the privacy and safety of users are protected.

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The sink hole attack is a typical wireless sensor network attack. The sink hole node advertises itself as the best route to the sink node or the base station. The nodes in the communication zone of the sink hole node then redirects their observed data to the sink hole node upon receiving the broadcasted supposedly the best route. The sink node receives the packets and then drops them. It can also modify them before relaying them to the sink node or base station. This study proposes a scheme designed to address the effects of the sink hole attack called the Hop Count-Based Detection Scheme for Sinkhole Attack. The performance of the scheme is evaluated using Matlab. The scheme is compared to Ibrahim's algorithm which is the best algorithm in literature. The simulation results show that HCODESSA achieves good results in comparison to Ibrahim's. The results show that the sinkhole attack has adverse effect on the performance of the network and that its severity depends on the number of nodes it can mislead. We further evaluated the two schemes using statistical techniques which proved that HCODESSA performs better.

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A major concern in the recent past was the traditional static spectrum allocation which gave rise to spectrum underutilization and scarcity in wireless networks. In an attempt to solve this challenge, cognitive radios technology was proposed. It allows a spectrum to be accessed dynamically by Cognitive radio users or secondary users (SU). Dynamic access can efficiently be achieved by making necessary adjustment to some Medium access control (MAC) layer functionalities such as sensing and channel allocation. MAC protocols play a central role in scheduling sensing periods and channel allocation which ensure that the interference is reduced to a tolerable level. In order to improve the accuracy of sensing algorithm, necessary adjustments should be made at MAC layer. Sensing delays and errors are major challenges in the design of a more accurate spectrum sensing algorithm. This study focuses on designing a channel selection algorithm to efficiently utilize the spectrum. Channels are ordered and grouped to allow faster discovery of channel access opportunities. The ordering is based on descending order of channel’s idling probabilities. Grouping of channels ensured that channels are sensed simultaneously. These two techniques greatly reduce delays and maximized throughput of SU. Hence, Extended Generalized Predictive Channel Selection Algorithm, a proposed scheme has significantly performed better than its counterpart (Generalized Predictive Channel Selection Algorithm). Matlab simulation tool was used to simulate and plot the results of the proposed channel selection algorithm.

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