http://196.220.128.81:8080/xmlui/handle/123456789/166 Mon, 27 Apr 2026 01:45:50 GMT 2026-04-27T01:45:50Z http://196.220.128.81:8080/xmlui/handle/123456789/2973 MULTILEVEL AUTHENTICATION SYSTEM FOR STEMMING CRIME IN ONLINE BANKING OLADELE, BLESSING EMMANUEL Multi-level authentication is a system in which two or more different factors are used in conjunction to access resources. Cybercrime is any criminal activity involving computers and networks, and it ranges from fraud to unsolicited emails, theft of government or corporate secrets through criminal trespass into remote systems around the globe. However, the wide use of online banking and technological advancement has attracted the interest of malicious and criminal users with more sophisticated attacks. Therefore, banks need to adapt their security systems to effectively stem threats posed by imposters and hackers, and also provide higher security standards that assures customers of a secured environment to perform their financial transactions. The aim of this research work is to stem all possible criminal threats when performing an online financial transaction. The authentication techniques employed include the mutual secure socket layer authentication embedded with the user ID and password verification, icon recognition, colour recognition, operator challenge and soft token authentication. The system was developed using interface design tools which include Adobe Dreamweaver CS6,Hypertext Markup Language (HTML) and Cascading Style Sheet (CSS), Server side scripting language-Hypertext Preprocessor (PHP) and Database management system – MySQL on Windows 7 platform. The analysis from the multilevel authentication system shows that online banking services have significant advantages over branch premises banking which encourage customers to engage in online transaction. However, the multilevel authentication techniques developed in this research, when compared with the existing authentication system used in online banking system, combined different authentication techniques on a single platform which when implemented enabled a more secured environment with minimal requirement from the customer, no additional hardware required with ease of management. Therefore, this research work is an approach made towards providing a more reliable solution for stemming cyber crime in online banking through the implementation of user multi-level authentication system. M.TECH THESIS Mon, 01 May 2017 00:00:00 GMT http://196.220.128.81:8080/xmlui/handle/123456789/2973 2017-05-01T00:00:00Z http://196.220.128.81:8080/xmlui/handle/123456789/2916 INTRUSION DETECTION USING ASSOCIATION RULE AND SUPPORT VECTOR MACHINE ADETOYE, FADEKEMI ADEDIWURA In this modern technology-driven age, protecting our personal information from being accessed by unauthorized users is becoming more difficult. Highly classified details are becoming more available to public databases, because we are more interconnected than ever. Thus, our data is available for almost anyone to sift through due to this interconnectivity, and this creates a negative mindset that the use of technology is dangerous, unreliable and highly unprotective because practically anyone can access the private information of others for a price. Intrusion detection systems (IDS) are the key solution for detecting these attacks so that the network remains reliable. Different classification approaches were used to evaluate intrusion detection system basically on rule-based and non-rule-based. The weaknesses discovered from the previous work are the key motivation for this research work. These includes: The work done on Network Intrusion Detection using Association Rules which generated an incomprehensive set of attack rules due to the small percentage of KDD’99 dataset used for training set, proposed wrapper method for feature selection in multiple class dataset using a sequential backward elimination method which is more computationally expensive and time consuming, and development of a Denial of Service attack detection using machine learning technique in which the Significant features of the dataset were not extracted, and the extraction was done using only one extraction technique which results in high level of FAR (False Alarm Rate) due to poor detection of attacks. This research makes use of NSL-KDD and UNSW-NB15 datasets, with Mutual Information and ANOVA (Analysis of Variance) as the feature selection techniques. In addition, an intrusion detection model was developed based on association rule and support vector machine and consequently, the performance of the model was evaluated. From the results obtained, the features selected from NSL-KDD dataset using Mutual Information gives 72% accuracy and 79% accuracy with ANOVA, and the features selected from NUSW-NB15 dataset using Mutual Information gives 90% accuracy and 85% accuracy with ANOVA when trained with SVM. Also, the features selected from NSL-KDD dataset using Mutual Information gives 67% accuracy and 68% accuracy with ANOVA, and the features selected from NUSW-NB15 dataset using Mutual Information gives 67% accuracy and 40% accuracy with ANOVA when trained with Association Rule. In conclusion, SVM (non-rule based machine learning) with both Mutual Information and ANOVA perform excellently in terms of accuracy than Association rule a rule-based machine learning technique. M.TECH THESIS Sun, 01 Mar 2020 00:00:00 GMT http://196.220.128.81:8080/xmlui/handle/123456789/2916 2020-03-01T00:00:00Z http://196.220.128.81:8080/xmlui/handle/123456789/1091 Development of a Stacked Ensemble Network Intrusion Detection System Olasehinde, Olayemi Oladimeji Securing important data and documents from unauthorized access and maintaining authorized privileges access control have been a major concern in every organization and industry; hence, a research focus for a very long time. Intrusion refers to set of activities that violate the laid down security objectives and procedures of any organization, intrusion detection system analyze network incoming traffic, in order to detect any form of security violation in it and generate an alert. This thesis focuses on the application of Stacked Ensemble Approach to Intrusion Detection Systems(IDS), Stacked ensemble exploits the strengths of various based-level models predictions and learn from them to build a more robust meta-classifier that improves classification accuracy and reduces false alarm rate. Three (3) filter-based feature selection methods comprising consistency-based, Information Gain-based and correlation-based methods were used to identify relevant features of the network traffic that identifies it as either a normal / attacks or attacks categories. Three (3) Supervised based-level machine learning algorithms comprising K Nearest Neighbor , Naïve Bayes’ and Decision Tree algorithms were used to build the base-predictive models with all the features and reduced selected features. Information Gain-based method identifies the strongest and most efficient features for the network traffic and Decision Tree models gives the highest classification accuracy on evaluation with the testing dataset, the predictions of the base-level models were used to train the three (3) meta-level learning algorithms, namely; Meta Decision Tree (MDT), Multi Response Linear Regression (MLR) and Multiple Model Trees (MMT) to build the Stacked Ensemble models. The ensemble systems were implemented with R programming language for features selection and Python programming language for the base-level and meta-level models building, it was evaluated on Core i5, 6GB RAM and 500GB HDD laptop computer. The stacked ensemble records accuracy improvement of 3.0% and 5.11% over the best and least predictions of the base-level models respectively and a reduction of 0.89% and 3.29% over base-model least and highest false alarm rate respectively . The evaluation of our work shows a great improvement over reviewed work in literature PH.D THESIS Mon, 01 Jul 2019 00:00:00 GMT http://196.220.128.81:8080/xmlui/handle/123456789/1091 2019-07-01T00:00:00Z