http://repository.aust.edu.ng/xmlui/handle/123456789/5106 This collection contains the research work of Systems Engineering MSc Student's Theses from 2021-2022 2026-04-09T12:01:59Z http://repository.aust.edu.ng/xmlui/handle/123456789/5158 Performance Analysis of Machine Learning Models for the Detection of Cyber Threats Against Satellite Networks Araromi, Haonat Olajumoke The proliferation of satellite networks for various critical applications in the space sector has heightened the need for robust cybersecurity measures to safeguard these systems from malicious intrusions. An intrusion detection system serves as the backbone for providing high-level network security. Different network attacks have been discovered and are gradually becoming more sophisticated and complicated. Despite the availability of many existing intrusion detection systems, intuitive cybersecurity systems are needed due to alarmingly increasing intrusion attacks. Furthermore, with new intrusion attacks, the efficacy of existing systems is depleted unless they evolve. This study conducts experiments that compare three types of supervised machine learning algorithms, including Decision Tree (CART), SVM (Black-box), and KNN (Lazy learner). Thus, these different algorithms were compared using various evaluation metrics, which are accuracy, recall, false alarm rate, and precision, and manual feature selections were done to select important features from the dataset that increase relevance and reduce complexity along with the Training time complexity on three intrusion datasets (STIN, UNSW-NB15, and CIC IDS2017(Wednesday)). CART DT achieves an accuracy of 93.42% with 31 features of the STIN dataset in 63.63 seconds, an accuracy of 93.13% with 8 features in 6.22 seconds, 76.63% with 42 features of the UNSW-NB15 dataset in 48.5 seconds, 76.63% with 6 features in 3.71 seconds, 99.87% with 68 features of the CIC-IDS 2017 Wednesday dataset in 59.29 seconds, 95.80% with 16 features in 4.96 seconds. SVM achieves an accuracy of 87.41% with 31 features of the STIN dataset in 559.51 seconds, an accuracy of 87.04% with 8 features in 286.78 seconds, 81.51% with 194 features of the UNSW-NB15 dataset in 421.06 seconds, 78.90% with 6 features in 176.05 seconds, 98.48% with 68 features of the CIC-IDS 2017 Wednesday dataset in 505.13 seconds, 96.92% with 16 features in 209.45 seconds. KNN achieves an accuracy of 86.28% with 194 features of the UNSW-NB15 dataset in 236.09 seconds. The results of this experiment give valuable insight for machine learning researchers into building a time-efficient and effective IDS using supervised machine learning for the Space sector. Although the secondary datasets used in this study provided good results, the use of primary datasets is suggested to enhance and improve the accuracy, integrity, and real-timeliness of the threat intelligence and resilience of the satellite networks in the space industry. Main Thesis 2024-08-15T00:00:00Z http://repository.aust.edu.ng/xmlui/handle/123456789/5157 Fault Detection in Electrical Power Transmission Systems Using Artificial Neural Network Algorithms Abbott, Temple Olotu The reliability and stability of electrical power transmission systems are critical for ensuring uninterrupted power supply. Fault detection in these systems is a significant challenge due to the complexity and dynamic nature of the electrical grid. This thesis presents a novel approach for fault detection in electrical power transmission systems using an Artificial Neural Network (ANN) algorithms. The proposed method leverages the pattern recognition capabilities of ANNs to accurately identify and classify various types of faults, including Voltage and Frequency anomalies. By analyzing these anomalies at one end of the transmission line, the ANN model is trained to detect anomalies and predict fault conditions with high precision. The performance of the ANN-based fault detection system is evaluated and validated through extensive simulations in the MATLAB environment, demonstrating its effectiveness in real-time fault detection and classification. The results indicate that the ANN algorithm not only enhances the speed and accuracy of fault detection but also reduces the risk of power outages and equipment damage. This research contributes to the development of more resilient and intelligent Power Transmission Systems, paving the way for future advancements in Smart Grid technologies. Main Thesis 2024-02-10T00:00:00Z http://repository.aust.edu.ng/xmlui/handle/123456789/5107 Optimization of Battery Management System for Nano Satellite Edet, David Kokoette A Battery Management system (BMS) is tasked to provide optimum and efficient control over the battery in any satellite EPS. Along with Efficiency, these systems also require intelligent safety measures to avoid catastrophic failure when working in the space environment. For a large-scale battery pack, the accumulation of the heat generated during the charging and discharging processes might increase the battery pack's temperature, which will posses a faster acceleration of electrochemical reaction that may cause battery damage. Thus, this study aims to optimize the charging current to minimize the charging time for fast battery charging before the satellite approaches eclipse. The BMS will be utilizing a Social Group Optimization Algorithm on MATLAB Simulink to overcome the state of charge (SOC) problem, improving the battery lifespan. The result shows that the total time taken for the algorithm to converge is 86.18s, having an optimized current at 2500mA to fast charge a lithiumion battery. This produces 524s decrease in the charging time without affecting the capacity and the life cycle for the battery life. The approach accounts for charge time reduction with an efficiency of 95.51%, having an improvement of 2.41% compared to the previous technique used. This result entitles that this method performed best over the previous technique and is easy to implement on Nano-Satellite, considering all the charging processes, allowing maximum battery protection from overvoltage, overcharging, and overheating conditions. 2021-09-12T00:00:00Z