Department of Information Science

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https://scholar.sun.ac.za/handle/10019.1/51

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Browsing Department of Information Science by browse.metadata.advisor "Blaauw, Dewald"

Now showing 1 - 3 of 3
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    Information orientation : a critical analysis of state-owned enterprises in South Africa
    (Stellenbosch : Stellenbosch University, 2023-03) Bekwa, Phindile; Blaauw, Dewald; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Information Science.
    ENGLISH SUMMARY: The South African State-Owned Enterprises (SOEs) generate and use large volumes of information on a daily basis as they operate business processes in documents, emails, websites and IT processes, thereby making information a key organisational asset that can provide a competitive advantage when managed accurately and efficiently. The generation of substantial volumes of information tends to compromise an efficient and effective application of information and knowledge in organisations. This study seeks to analyse and determine the Information Orientation (IO) maturity levels of SOEs, using the Information Orientation Model of Marchand and Kettinger (2011). The study further attempts to understand how the implementation of the Michael Porter’s Competitive Model impacts the Information Technology practices, Information Management practices and Information Behaviours and Value (IBV) of SOEs in South Africa. Additionally, the study further explores the three information capabilities, namely; IT, IM and people’s behaviour and values (IBV), in relation to their current application in SOEs. A sample of SOEs have served as the study population in this research. Data was collected from astute organisational representatives because of their understanding of organisational strategies, processes, culture and climate. Senior managers were the targeted respondents to solicit their perspectives and understanding regarding the management and use of information within their organisations. This research was conducted through a survey administered by means of a questionnaire that was sent to respondents through email. Covid made it impossible to conduct in-person and virtual interviews, as some respondents had no technological means to respond through person-to-person interactions. Many of the SOEs were not reachable, even virtually. During the time of the study it was an abnormal period in SA. A thematic analysis was used. The analysis is framed on the three main capabilities of Information Orientation. The collected data indicates that SOEs embrace the notion that it is important to sense information internally and externally in order to identify areas that might negatively or positively affect business. The study determined that a systematic, standardized and centralised approach is needed in the organising and enhancing of easy access into information. The findings show that most SOEs in South Africa have a high proportion of the IMP and ITP in their decision-making processes but the portion for IBV is comparatively low. The results confirmed that the human element of the IO model is neglected by most organisations, putting more investment into IT infrastructure. Therefore, IBV needs to be mainstreamed and integrated into SOE information strategies in order to improve outputs and to facilitate the achievement of their socioeconomic mandates.
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    An investigation and analysis of vulnerabilities surrounding cryptocurrencies and blockchain technology
    (Stellenbosch : Stellenbosch University, 2024-03) Heyl, Isabelle Christina; Blaauw, Dewald; Watson, Bruce; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Information Science.
    ENGLISH SUMMARY: As individuals search for a safe and alternative payment method, the popularity of cryptocurrencies and blockchain technology continues to grow. Despite their benefits, the increasing prevalence of these technologies also exposes them to a higher risk of encountering potential threats. These threats stem from the vulnerabilities and technical limitations within, which in turn, influences its potential to be adopted across different industries. Through a quantitative approach, this paper aims to detect and mitigate the vulnerabilities and technical limitations within cryptocurrencies and blockchain. A simulated environment provides the means to study distinct parameters within a Bitcoin network, particularly in the event of a double-spending and selfish mining attack. Based on the findings, the success rate and revenue of the attacker were primary influenced by the network’s stale block rate, the attacker’s hash rate and the double-spend value. Moreover, there is a significant difference in the parameters pre- and post-attacks. It is evident that the block size has a considerable effect on the parameters in the network. As such, a single-objective problem is solved to determine what the optimal block size should be to minimize the block generation time and block delay time, seeking to reduce the overall latency and increase the throughput. It can be concluded that using the optimal block size can partially reduce the threat of double-spending attacks, but not eliminate it completely. It becomes evident that other mitigation schemes should be implemented to overcome these vulnerabilities.
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    Towards a supervised machine learning algorithm for cyberattacks detection and prevention in a smart grid cybersecurity system
    (Stellenbosch : Stellenbosch University, 2024-03) Banda, Takudzwa Vincent; Blaauw, Dewald; Watson, Bruce; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Information Science.
    ENGLISH SUMMARY: Critical infrastructure cyberattacks have become a significant threat to national security worldwide. Adversaries exploit vulnerabilities in communication networks, technologies, and protocols of smart grid SCADA networks to gain access and control of power grids, causing blackouts. Despite the need to safeguard the reliable and stable operation of the grid against cyberattacks, simultaneously detecting and preventing attacks presents a significant challenge. To address this, a Kali Linux machine was connected to a smart grid SCADA network simulated in GNS3 to perform common cyberattacks. Wireshark was then deployed to capture network traffic for machine learning. Aiming to improve the detection and prevention of cyberattacks the study proposed a dual-tasked ensemble supervised machine learning model, a combination of Multi-Layer Perceptron Neural Network (MLPNN) and Extreme Gradient Boosting (XGBoost), that had an average accuracy of 99.60% and detection rate of 99.48%. The first task of the model distinguishes between normal state and cyberattack modes of operation. The second task prevents suspicious packets from reaching the network destination devices. Leveraging the PowerShell command-line tool, to success the model dynamically applies packet filtering firewall rules based on its predictions. Therefore, the proposed model is both an Intrusion Detection System (IDS) and Intrusion Prevention System (IPS). The model was tested on new data, producing an accuracy of 99.19% and detection rate of 98.95%. Furthermore, the model's performance was compared to existing proposed cyber-attack detection models and consistently outperforms these proposed models on most datasets, demonstrating its superiority in terms of precision, accuracy, and recall/detection rate. Thus, the proposed model, with its function as a firewall, enhances the overall security capabilities of the smart grid SCADA networks and significantly mitigates potential cyberattacks.