MSIT Theses and Dissertations (2021)

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    A Blockchain-based prototype for cybersecurity threat intelligence sharing: a case of Kenyan banking and insurance financial institutions
    (Strathmore University, 2021) Kibuci, Wanjohi Stephen
    Cybersecurity threats to financial institutions have become more sophisticated and challenging to deal with. The growing dependence of financial institutions on cyberspace makes cybersecurity preparedness against threats important to achieve a financial institution's mission and vision. In this context, cybersecurity preparedness is the process in which a financial institution can protect against, prevent, mitigate, respond to, and recover from cyber threats. Traditionally, most organizations share threat intelligence through ad hoc methods such as emails and phone calls but there is a need to automate threat intelligence sharing where possible to improve cybersecurity preparedness. To address this issue, and enhance cybersecurity and trust, a blockchain-based approach can be employed to share threat intelligence. This study aims to leverage blockchain technology by developing a prototype to automate cybersecurity threat intelligence sharing in financial institutions. The study used a quantitative approach in data collection using structured online questionnaires with close-ended questions and open source datasets and data analysis using several analytic tools. The prototype has been developed using the Rapid Application Development software development methodology using open-source Oracle Virtual Box that runs on Linux Operating System
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    An IoT prototype to mitigate human-wildlife conflict
    (Strathmore University, 2021) Thuo, Ng'ang'a Thuo
    Due to the accelerated population growth in Kenya and around the world, pressure on and competition for resources in game parks, ranches, conservancies and game reserves has grown tremendously. This has led to the reduction of wildlife in terms of population due to poaching and territory invasions by both domestic animals and human beings. This problem calls for a technological solution to mitigate the constant conflict between wild animals and human beings. The eventual fallout from this conflict, if it were allowed to persist, would be the extinction of important flora and fauna that contributes a great deal to natural ecosystems vital for the survival of the human race, the habitability of the planet and the sustenance of economies which gain from the existence of unique wild animals and plants through tourism and scientific research into wildlife. The main objective of this research is to mitigate human-wildlife conflict by developing a prototype that combines web, loT and SMS technologies. In addition, this research also applies the Rapid Application Development methodology to develop and test a sensor-based system for monitoring animal activity in human-wildlife conflict prone areas through Internet of Things. Its physical architecture consists of an Arduino microcontroller, a Wi-Fi shield a breadboard and a motion sensor. In this architecture, the Arduino microcontroller powers the Wi-Fi shield which then connects to an available access point and in so doing, the internet. The motion sensor detects motion data and sends it to the Wi-Fi shield. The logic for this process is written and compiled into the Arduino microcontroller and Wi-Fi shield via a C++ program. The data is sent via the HTTP protocol to an A WS lambda function written in python processes this data. Once the data is processed, an SMS is triggered and sent to registered phone numbers to warn them of imminent conflict and advise on steps to take. The SMS message also helps the recipients to plan better and deploy resources in a more organised fashion to areas where conflict is rife. This solution is also low cost, accurate and can be implemented at scale along boundary areas. The results in this research show that it is possible to combine web, SMS and loT technologies to successfully mitigate and reduce human-wildlife conflict.
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    A Model for predicting pre-delinquency of credit card accounts using Extreme Gradient boosting
    (Strathmore University, 2021) Kisengese, Antony Mwawuganga
    Credit risk is one of the significant risks that financial institutions that advance credit in credit cards are exposed to. Credit card accounts are usually classified as “good” or “bad” depending on the propensity of the cardholder to settle their debt on time. The latter usually pose a significant negative impact to the issuer’s books when the credit card account falls into late collections and recoveries are futile resulting to bad debts. Ensemble classifier algorithms have demonstrated greater performance in classification and regression problems due to their ability to trade-off bias and variance factors. In this study, an Extreme Gradient Boosting ensemble classifier was implemented based on cardholder personal characteristics and transaction patterns with the aim to minimize defaults in the late collection stages by identifying credit card accounts that exhibit early signs of delinquency way before the cardholder misses payments. A credit card dataset from the UCI Machine Learning repository was used to train and validate the model, which achieved a prediction accuracy of 81.62% and outperformed a set of single classifiers that were used in benchmarking. Depending on each score, the issuer will make informed decisions of how well to proactively engage the cardholder to identify the best way of intervening in their financial situation and mitigate the risk of missing payments.
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    Twitter sentiment analysis tool for detecting crime hotspots: a case of Nairobi, Kenya
    (Strathmore University, 2021) Onyango, Kevin Omondi
    Insecurity brought about by crime continues to be a major thorn in the flesh of citizens leaving in Kenya's urban centres. Although, incidents of crime are frequently reported from different regions in Kenya, there are more cases being reported from Kenya's urban centres than the rural ones, especially the informal settlement areas. This has been attributed to the rapid urbanisation of Kenya’s major towns. Violent crimes are costly. Murders, rapes, assaults, and robberies impose concrete economic costs on the victims who survive as well as the families of those who lose their lives, in the loss of earnings and their physical and emotional tolls. The Kenyan government has invested heavily in setting up Internet Protocol Circuit Television Cameras (IP CCTVs) in Nairobi's Central Business District in a bid to curb crime. In 2015, the government implemented a community policing strategy at various levels namely, market, estate, house level among others. This community policing is known as Nyumba Kumi. The culture in urban centres especially Nairobi makes it very difficult to implement Nyumaba Kumi. For instance, Nairobi is a city where people are less concerned with the affairs of their neighbours. For Nyumba Kumi to be effective in Nairobi, a culture change has to occur. Culture changes usually take time. On the other hand, CCTVs have proven to be a useful tool in tracking down criminals and bringing them to book. However, maintaining CCTVs is quite expensive and CCTV footages have been reported missing in some cases whenever investigators needed them. Data mining algorithms can be employed to fetch useful patterns on Social Media posts especially Tweets from Twitter to monitor crime. This study proposed a Twitter sentiment analysis tool which was used to detect crime hotspots in Nairobi. The tool employed machine learning techniques to a build binary classifier in detecting crime hotspots. This research fetched sample crime relevant tweets from Twitter which were used to build the corpora. Then a Support Vector Machine model was trained and validated based on the labelled text data using bigram features and term frequency-inverse document frequency weighting. In order to determine what combination of features provided the most desirable performance outcome on the data collected, the SVM model was compared to Naive Bayes, K-nearest neighbour and Random forest machine learning algorithms. Based on the results from the experiments, it was found that the best way to create a model for detecting crime hotspots using Twitter is the use of an SVM machine learning algorithm with bigram features weighted using tf-idf. The SVM model produced an accuracy of 88% making it the most accurate compared to the rest.
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    A Prototype for profit maximization using Apriori data mining algorithm: case of Kula Kona restaurant
    (Strathmore University, 2021) Okeyo, Seth Ouma
    Most businesses use different promotional and pricing methods to improve profits, revenues, and sales volumes. For example, a restaurant manager may change prices to encourage sales of food items. Also, he or she may in a special way advertise or present advertise the items to increase customers’ awareness and demand. This has become cumbersome and has made the management of such businesses difficult and has informed the decisions to develop systems with aims of coming up with a solution to this, most of the systems are complicated in nature and difficult for the users to apply, most of them are also rigid to platforms/system requirements since they were developed in old and probably un-scalable platforms. The aim of this research is to formulate a prototype for profit maximization using Apriori data mining algorithm. This is achieved by applying the algorithm on existing sales knowledge bases with other given parameters, some kept constant and others varying, and the algorithm is able to determine the sales patterns using different internal and external parameters. The prototype then automatically analyzes the patterns and come up with reports and summaries which can aid in decision making and consequently profit maximization with the optimal prices of the goods, which is advantageous to both the restaurant owners and clients. The research site is Kula Kona restaurant located in the Hurlingham area within Nairobi. The research design is used to conduct the scientific study and descriptive approach to demonstrate the effects of adjustments of different variables which help understand the behavior and effects on other variables in relation to sales at a given time. The Data-driven modelling methodology was used in this model development. The methodology was ideal since it relied on retrospective data and it performed at the accuracy level of 93.71% and a mean square error of 0.039. The results were great and showed that Apriori algorithm is the best fit for this type of machine learning prototype.