V.I Osubor

The rapid growth of digital technology and e-commerce has transformed the way goods and services are exchanged, creating new opportunities for businesses and consumers. Despite these advancements, many small businesses within the University of Benin (UNIBEN) community still rely on traditional advertising methods such as word-of-mouth referrals and printed flyers, which limit their visibility and growth potential. The absence of a centralized platform also makes it difficult for students, staff, and faculty to easily discover and access available services within the university environment.

This project proposes the development of an online marketplace specifically designed for the University of Benin community. The platform aims to provide a digital space where small business owners can showcase their products and services, while enabling users to conveniently search, compare, and engage with service providers. The proposed system integrates features such as business listings, service categorization, customer reviews and ratings, communication tools, and performance analytics to improve user experience and business management.

The online marketplace is expected to enhance the visibility of campus-based businesses, increase customer engagement, and promote economic activities within the university community. Furthermore, it will provide a structured and accessible platform that connects buyers and sellers efficiently, fostering convenience, transparency, and trust. The study concludes that implementing a dedicated online marketplace for UNIBEN can significantly contribute to the digital transformation of campus commerce and support the growth and sustainability of small businesses within the institution.

The security and reliability of cloud computing environments face significant threats from the escalating frequency and sophistication of Distributed Denial of Service (DDOS) attacks, which cause substantial financial losses and service disruptions while often serving as entry points for further system compromise. This research addresses this critical challenge by developing and evaluating deep learning-based detection models using two contemporary datasets: CICDDOS2019 (254,797 normal and 51,404 attack instances with 78 features) and IDS_ISCX_2012. To mitigate class imbalance, a balanced subset of 50,000 instances per class was created through random under-sampling, with optimal feature selection performed using the K-best method. Two advanced recurrent neural network architectures were implemented and compared: Bidirectional Long Short-Term Memory (BI-LSTM) and Gated Recurrent Unit (GRU), both enhanced with temporal attention mechanisms to focus on critical attack patterns within sequential network traffic. Experimental results demonstrated that GRU outperformed BI-LSTM across both datasets, achieving accuracies of 0.93 and 0.65 on IDS_ISCX_2012 and CICDDOS2019 respectively, compared to BI-LSTM's 0.91 and 0.61. The GRU model's simplified architecture proved more computationally efficient while effectively addressing the vanishing gradient problem common in recurrent networks. This study successfully establishes a robust framework for DDOS attack detection in cloud environments, contributing to enhanced network security through improved accuracy, reduced false positives, and practical implement ability for real-time threat mitigation