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Globally, as the upsurge in dependencies on computer network services, so are the activities of attackers that gain undue access to network resources for selfish interest at the expense of the stakeholders. Attackers threaten integrity, availability, and confidentiality of network resources despite various preventive security measures, hence the need to study ways to detect and minimize attackers’ activities. This paper develops a Network-Based Intrusion Detection System (NBIDS) using the machine learning algorithms capable of detecting and preventing anomaly (attack) network traffic from the Internet, thereby reducing cases of successful network attacks. Gain Ratio and Information Gain are used for features ranking on the UNSW-NB15 benchmark network intrusion dataset. The first fifteen highly ranked features are selected for developing classification models using C4.5 and Naive Bayes (NB), coincidentally the two feature ranking approaches to select the same features but in a different ordering. Empirical results show that the C4.5 algorithm outperformed NB for all simulations based on a different spilled ratio of testing and training sets as it returns the highest accuracy of 90.44% against 75.09% for NB for two-class models on simulation IV. For all the experimental setup, both DT and NB have a constant precision of 91% to 75%, the True Positive value of 90%:75%, and False Positive value of 8.6%:22.8%, respectively. The experiments revealed that accuracy increases as the training ratio increases. The results show that the approach is practicable for real-time network intrusion detection.

Over the years, the vulnerability of the network system has completely revolutionized the security system. Attackers simply exploit these exposures to gain undue access to network resources. It is essential to safeguard network resources with the Intrusion Prevention System (IPS) and Intrusion Detection System (IDS). Hence, to develop an optimized IDS model using ensemble modeling of machine learning technique is paramount. Therefore, this research attempts to build a network IDS using an ensemble of Decision Trees (DT) algorithms to optimize an IDS model. Furthermore, the C4.5 DT classifier used on the University of New South Wales Network-Based 2015 (UNSW-NB15) network data set. Then the ensemble techniques of bagging and AdaBoost are compared during the performance evaluation. The results of this work depict that performance increase as training size increases. Consequently, the results showed that adopting the ensemble model for the C4.5 decision tree classified improved network intrusion classification compared to using the machine learning algorithm in isolation. Moreover, the result also showed an improved classification model for network IDS model. The AdaBoost ensemble model of the C4.5 DT algorithm using partitions of 90% of training and 10% of testing data sets performs the best with 98% accuracy and precision.

This research designed a multigeneration system that meets the energy requirement of a rural community in Nigeria. This system is powered by the biogas produced from maize silage and chicke...

Smart-grid is the adoption of better control, monitoring and remote sensing in power systems while micro-grid is an advance approach to integrate energy resources in the power distribution system. These two technologies have developed over the years and have proven to be a reliable and secured approach in power system. In this paper, the potential utilization of smart micro-grid to solve the power supply challenge in Nigeria is explored. The used of wind and solar PV for electricity generation for 12 different cities in Nigeria is also analyzed. Nigeria’s renewable energy and fossil fuel potential are enormous but the country is faced with epileptic power supply challenge. Only 60% of Nigerians have access to electricity and most of these citizens (55%) reside in the urban areas. The paper presents the prospect of smart micro-grid as an immediate solution to the current challenges in Nigeria power sector.