Engineering Headway Vol. 37

Abstract: The review of literature reveals cybercrime as one of the fastest-growing global security threats, and in emerging economies like Nigeria, the evolution is intricately configured to the rapid growth of internet accessibility, weak regulatory policies, and socio-economic factors. This review analyses the problem of cybercrime in Nigeria with empirical evidence drawn from a case study that attempts to illustrate the complicated growth pattern of cyberspace in Nigeria. The analysis covers three distinct evolution phases: the initial phase of internet penetration through cybercafés (2000 to 2001) and the proliferation of mobile and GSM technology (2001 to 2011), both precursors to the broadband-enabled digital era, which presumably spans 2011 and onwards. These shifts correspond to changes in the level of sophistication of cybercrime which evolved from simple email hoaxes to identity theft and advanced fraud schemes. While adopting these technologies, other issues such as lack of adequate public awareness, regulatory policies, and limited capacity of cybersecurity approaches are equally pressing. Based on these findings, this research introduced the PRAP (Proactive, Reactive, Active, Persuasive)Framework, which can better contextualize Nigeria's cybersecurity needs. The holistic PRAP framework addresses prevention, incident response, threat assessment, prosecution and adapts to Nigeria's social and technical context. This policy is crucial for public and private sector stakeholders concerned with the fight against cybercrime in developing countries.

Abstract: The Internet of Vehicles (IoV) has transformed transportation through seamless communication and intelligent coordination among connected vehicles. This advancement, however, has introduced a broader spectrum of cyber risks, necessitating intelligent and efficient threat detection strategies. This paper introduces a lightweight intrusion detection framework tailored for connected vehicle ecosystems, employing EfficientNet for deep feature extraction and a Particle Swarm Optimization (PSO)-tuned Random Forest (RF) classifier for classification. Transfer learning was utilized to enhance feature compactness and relevance, while PSO refined the RF parameters to maximize detection accuracy. Experimental validation on two publicly available benchmark datasets demonstrated superior performance, achieving perfect classification on the Car-Hacking dataset and 99.89% accuracy on CICIDS2017. The model also sustained high levels of detection precision, sensitivity, and F1 measure across multiple intrusion categories. With an inference latency of just 0.0173 milliseconds per sample, the system processes over 57,000 flows per second—confirming its viability for deployment in real-time, resource-limited vehicular environments.

Abstract: Software that can damage an information system asset is considered a malware, such information systems have been rendered to several destructive attacks mainly due to the emergence of the Internet. Conventional Antimalware software is not effective at eliminating malware due to its many evasion techniques such as polymorphism and code obfuscation. Antimalware software is ineffectual and defenseless against zero-day attacks as it can only eliminate malware for which it has signatures. K Nearest Neighbor, Decision Tree and Support Vector Machine are some of the leading classifiers that has successfully detect and classify Malware but optimal accuracy of detection has not been achieved, in addition, false positives and false negatives persists because the hyperparameters of these classifiers were not optimized and noise was not filtered out of the datasets using feature selection technique. The aim of this research is to develop an optimized malware detection and classification framework employing Principal Components Analysis to mitigate the curse of dimensionality while utilizing optimal hyperparameters of chosen classifiers to boost accuracy of malware detection and classification as well as reduction of false positives and false negatives. This research employed K Nearest Neighbor, Decision Tree, and Support Vector Machine to detect and classify malware with CICMalmem dataset to train the model. Grid search optimization was combined with K-fold cross-validation to optimize the hyperparameters of the selected classifiers in order to boost the model's performance and achieve high detection accuracy as well as low false positives and low false negatives. Machine learning performance metrics such as the F1 Score, Precision, Recall, and Confusion Matrix were used to evaluate the Research Model. K Nearest Neighbor generated Zero False Positives while KNN, Decision Tree and Support Vector Machine achieved Accuracy of 99%, 98.64, and 100% respectively.

Abstract: Advanced Persistent Threats (APTs) pose significant challenges to cybersecurity due to their stealthy, multi-stage nature. Traditional detection methods struggle to identify such complex behaviors. This study investigates the effectiveness of deep learning (DL) techniques in detecting APTs. Two research questions guide the study: (i) How effective is a custom Transformer-based model compared to existing DL models in detecting APTs? (ii) To what extent have DL models contributed to APT detection between 2020 and 2025? A two-fold methodology was adopted: a meta-analysis of 30 peer-reviewed studies and an empirical evaluation of a Transformer model trained on the CIC-IDS2018 dataset. The model achieved 99.71% accuracy with macro-averaged F1-score, precision, and recall values above 0.70, demonstrating strong overall performance but revealing challenges in classifying rare attack types. The meta-analysis further identified key research gaps, including limited use of multi-class classification, underutilization of Transformers, and a need for real-world datasets. Findings show that Transformer-based architectures are not only viable but good at modeling complex feature dependencies and detecting sophisticated APT behaviours. This research highlights both the strengths and limitations of DL for cybersecurity and suggests future directions for improving robustness and scalability in real-world deployment.

Abstract: This review investigates the transformative potential of Virtual Reality (VR) in mechanical and mechatronic engineering education within African settings, focusing on its applications in hands – on training, laboratory simulations and expanding access to quality instruction. By analyzing case studies and empirical data, we demonstrate that VR-based training reduces task completion times by 75 % compared to traditional methods while improving spatial understanding, learner engagement and knowledge retention. Key applications include immersive virtual labs for torsion testing, universal testing machines and safety training for high–risk scenarios. Despite challenges such as hardware costs limited infrastructure and curriculum integration, VR offers scalable and inclusive solutions that democratize access to high – quality engineering education. This work highlights VR’s role as a critical enabler of next-generation pedagogy, in resource–constrained environments, urging educators and institutions in African settings to adopt immersive technologies to bridge the gap between theoretical instruction and industry demands.

Abstract: This paper examines the integration of Artificial Intelligence (AI) and Machine Learning (ML) tools in language learning for novice learners, with a focus on grammar correction, vocabulary acquisition, speech recognition, and enhancing motivation for continued learning. A survey to justify the selection of AI-based tools such as Large Language Models (LLMs), reinforcement learning, adaptive learning systems, and automatic speech recognition (ASR) was performed. Results indicated that LLMs significantly improved grammar accuracy, while reinforcement learning-based vocabulary tools enhance retention. Transformer-based architectures outperform traditional models in contextual language understanding and speech recognition, reducing word error rates. Additionally, AI-driven chatbots and gamification platforms improved engagement. Despite these advancements, challenges such as AI over-reliance, lack of deep personalization, and accent bias in ASR models remain. The research follows a comparative and mixed-methods research design approach with machine learning-based model evaluation, using structured and unstructured datasets. Model performance will be assessed through accuracy, precision, and recall metrics.

Abstract: Cooperative societies at educational institutions often rely on labor-intensive manual processes that lead to transcription errors, delayed loan processing, and limited member visibility into financial activities. This paper presents the design and implementation of a cross-platform mobile Cooperative Management System (CMS) for Nile University’s cooperative society, developed with Flutter and backed by Firebase services (Authentication, Firestore, Cloud Functions, and Storage). Following the Waterfall model, the development began with a comprehensive requirements analysis to identify core functional modules (member registration, share purchases, savings tracking, loan application and disbursement, notifications) and nonfunctional criteria (scalability, offline-sync, security). System design employed Unified Modelling Language diagrams and wireframes to guide implementation of a responsive UI featuring role-based access control and offline synchronization. The backend leverages Firebase for real-time data updates and integrated notifications. Through functional, performance, and usability testing, the system demonstrated a faster loan-approval cycle and reduced data-entry errors compared to the manual process. The proposed CMS enhances transparency, efficiency, and member engagement, offering a scalable template for similar cooperative societies in educational settings.

Abstract: University-Industry Collaboration (UIC) is critical for universities to fulfill their role in providing solutions to societal development challenges and generating scientific knowledge, a function that is significantly improved by government participation, as indicated by the traditional triple-helix model. Though, Nigeria currently demonstrates a low level of performance in university-industry research collaborations. Acknowledging the limitations of existing models and the need for a more robust framework, this study adopted a descriptive research design involving primary data collection from respondents across universities in Nigeria. The research comprehensively assessed the constraints inhibiting sustained and robust UIC, the intensity of current UIC activities, the innovativeness of Nigerian universities, and the strategies employed for accelerating UIC and innovation. Based on the analysis of collected data and a synthesis of previous models, this work proposes an improved model that advocates for universities to engage with a broad range of companies, pursue global corporate-sponsored research, establish clear Intellectual Property Rights (IPR) agreements, involve industry experts in research, seek guidance on practical technology application, and integrate new technology-associated skills into curricula. The enhanced model offers a comprehensive framework aimed at improving UIC and innovativeness in Nigeria, addressing the existing fragmented practices and provides a practical guide for stakeholders and policymakers to foster more effective collaborations, thereby contributing to national development.