Department of Computer Sciences

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https://repository.run.edu.ng/handle/123456789/72

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Browsing Department of Computer Sciences by Author "Gbeminiyi Falowo"

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    Blockchain Mechanism Approach to Smothering of Denial of Service (DoS) Spikes: A Focus on Internet of Things (IoT) Technologies
    (International Journal of Research and Scientific Innovation, 2024-09) Gbeminiyi Falowo
    Denial of Service (DoS) is a cybercrime that attempts to impede electronic consumers from accessing websites and online services by saturating a server with internet traffic. Cyber-spikers use a network of infected computers, tools like bots, and other machines they can access remotely. A decade ago, businesses and financial institutions lost approximately half a trillion dollars due to DOS spikes. DoS savages would triple in number before the closure of the year 2023 from about eight million less than five years ago. This study uses a blockchain-based decentralized authentication technique to guard against DoS attacks on the application layer of Internet of Things (IoT) technologies. This secured mechanism involves starting the communication process, developing the system, and suggesting an intelligent contract. Performance evaluation of the developed model was carried out by comparing the approaches’ temporal complexity. The recommended method was also used on two processors operating at two distinct speeds while utilizing the SolarWinds application, an online CPU stress test, and usage with a deduction that the second is preferred. An Intelligent contract for IoT machine usage is established to authorize the blockchain level.
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    Development Of A Machine Learning Model For Brand And Audience Segmentation Using Demographic Data
    (Corpus Intellectual, 2025) Gbeminiyi Falowo
    The expansion of the global business landscape, a highimpact factor in eCommerce, has resulted in identifying potential customers and their positive reactions to products or services offered by companies that use the internet to promote their electronic business. With a high increase in audience using social media, there is a need for brand and audience segmentation and targeting for profit-making; thus, this study developed a machine learning model for brand and audience segmentation using the Social Media Advertising Dataset. The dataset includes comprehensive data on social media advertising campaigns across Facebook, Instagram, Pinterest, and Twitter, featuring ad impressions, clicks, spending, demographic targeting, and conversion rates. With 16 columns and 300,000 rows, the dataset offered substantial data for analysis. The study compared the performance of a Naive Bayes model with a Random Forest algorithm in two existing literature; the Naive Bayes model achieved an accuracy of 35%, the Random Forest model achieved an accuracy of 89.6%, and the Random Forest model in the current study's model reached 97% accuracy. The Random Forest model's superior performance in both studies demonstrates its effectiveness in consumer group segmentation, indicating its practical utility in optimizing marketing strategies and improving customer targeting. An implementation of the developed model of the study was in Python and deployed on a website using the Flask framework, providing an accessible tool for practical applications.
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    Latency-Aware Load Distribution Model for Vehicular Adhoc Networks (VANETS)
    (International Journal of Innovative Science and Research Technology, 2022-03) Gbeminiyi Falowo
    Vehicular Adhoc Network (VANET) is a network which prominent and fluid features have helped in drawing contiguous attention by researchers for more than thirty years. More often than none, routing and securing the network are of utmost priority to researchers while little focus is given to ensuring the effective distribution of load in Vehicular communications in order to ensure an infinitesimal experience of latency. However, with the advent of Intelligent Transport System (ITS) in pair with the possibility of cloud and Internet of Things (IoT) in VANET that support a mickle spectrum of mobile distributed software, there is need for a system that will evenly distribute load in the network. This study thereby introduces a latency-aware model via a 3-tier load distribution mechanism that reduces delay in message transmission and also helps in addressing traffic congestion faster.