Browsing by Author "Balogun, Abdullateef Oluwagbemiga"
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Item Anomaly Intrusion Detection Using An Hybrid Of Decision Tree And K-Nearest Neighbor(Journal of Advances in Scientific Research & Applications (JASRA), Faculty of Science, Adeleke University, Ede, Osun state., 2015-03-30) Balogun, Abdullateef Oluwagbemiga; Jimoh, Rasheed GbengaWith the dictate of the 21st century making the economy to be information-driven, securing this valuable asset becomes interesting engagement. Intrusion detection plays a vital role in this regard by allowing prospective attacks or threats to information resources by unauthorized person(s) be detected and prevented. Previous researchers have identified the need for a robust approach to intrusion detection. This paper presents an overview of intrusion detection and a hybrid classification algorithm based on decision tree and K Nearest neighbour. The data set is first passed through the decision tree and node information is generated. Node information is determined according to the rules generated by the decision tree. This node information (as an additional attribute) along with the original set of attributes is passed through the KNN to obtain the final output. The key idea here is to investigate whether the node information provided by the decision tree will improve the performance of the KNN. A performance evaluation is performed using a 10-fold cross validation technique on the individual base classifiers (decision tree and KNN) and the proposed hybrid classifier (DT-KNN) using the KDD Cup 1999 dataset on WEKA tool. Experimental results show that the hybrid classifier (DT-KNN) gives the best result in terms of accuracy and efficiency compared with the individual base classifiers (decision tree and KNN).Item Hybridization of El-Gamal and Blow-Fish Algorithm for Data Security(Faculty of Communication and Information Sciences, University of Ilorin, Ilorin, Nigeria., 2017) Bajeh, Amos Orenyi; Olatunde, Yusuf Olanrewaju; Akintola, Abimbola Ganiyat; Balogun, Abdullateef Oluwagbemiga; Wasiu, M. O; Sulaiman, A. T.The internet connects all part of the world together and thus, makes it very easy and fast to communicate from different location. Reports by internet user on security issues such as hacking of e-mail account, SQL injection, unauthorized access to transaction details makes users to migrate from one platform to another. The increasing activities of hackers to overcome the existing security measures have raised the need for more and improving security technique.Therefore,this study demonstrates hybridization of Blowfish and El-Gamal algorithm to improve data security and improve the performance of El-Gamal algorithm. In the hybrid system, Blowfish is used to encrypt message containing private data using a secret key after which the secret key is encrypted by El-Gamal algorithm using public and private key which are mathematically related.The cipher text produced contains the mixture of private data and secret key. A simulation program is developed using java in order to experiment the difference between the hybrid system, blowfish and El-Gamal algorithm. The result showed that the developed hybrid system is more secure and faster compared to El-Gamal algorithm.Item A Network Intrusion Detection System: Enhanced Classification via Clustering Model(Research Nexus Africa’s Networks in Conjunction with The African Institute of Development Informatics & Policy (AIDIP) Ghana & The International Centre for Information Technology & Development (ICITD), USA, 2015-12-10) Balogun, Abdullateef Oluwagbemiga; Balogun, Adedayo Miftaudeen; Adeyemo, Victor Elijah; Sadiku, Peter OgirimaThe aim of developing an IDS is to build a system that oversee the general protection of a network from attacks both from withinand without, and doing so accurately. Optimization of IDS has also being receiving attention from the research community due toits large volumes of security audit data. In developing an IDS, most dataset used have high dimension in which only few attributes are needed for building an IDS – feature selection is used to solve this little problem. In this paper, we present and analyze the performance of some machine learning algorithm which performs classification via clustering using the KDDcup’99 dataset. Using the WEKA tool, simulations were ran and results was deduced after applying the proposed models to the dataset containing all the type of attacksItem PERFORMANCE EVALUATION OF SELECT DATA MINING SOFTWARE TOOLS FOR DATA CLUSTERING(Federal University Wukari, Taraba State, Nigeria., 2018-09-10) Ameen, Ahmed Oloduowo; Bajeh, Amos Orenyi; Adesiji, Boluwatife Aderinsola; Balogun, Abdullateef Oluwagbemiga; Mabayoje, Modinat AboloreData mining is used to discover knowledge from information system. Clustering is one of the techniques used for data mining. It can be defined as a technique of grouping un-labelled data objects such that objects belonging to one cluster are not similar to the objects belonging to another cluster. Data mining tools refer to the software that are used for the process of efficiently analysing, summarizing and extracting useful information from different perspectives of data. This paper presents a comparative analysis of four open-source data mining software tools (WEKA, KNIME, Tanagra and Orange) in the context of data clustering, specifically K-Means and Hierarchical clustering methods. The results of the performance analysis based on the execution time and quality of clusters showed that WEKA tool outperforms the other tools with the lowest SSE of 199.7308 with an average execution time of 1.535 seconds. Knime has SSE of 222.217 but with an average execution time of 7.13 seconds, and then Tanagra with SSE of 269.3902 and average execution time of 2.01 seconds, Orange has the poorest performance with SSE of 388.78.Item Solving the Next Release Problem using a Hybrid Metaheuristic(Computers and Applied Computer Science Faculty in "Tibiscus" University of Timişoara, Romania., 2016) Balogun, Abdullateef Oluwagbemiga; Mabayoje, Modinat Abolore; Makinwa, Sayo Michael; Bajeh, Amos OrenyiThe Next Release Problem is characterized by the need to determine the features that are to be included in a particular software system to make up the next release. These features are to be selected, such that users’ demands and needs are satisfied as much as possible, given a limited resources, by ensuring that the available resources are used to develop the most important features first. This work applies a hybrid of Variable Neighbourhood Search (VNS) and Tabu Search (TS) for solving bi-objective NRP, using a cost-value model for requirements. Experiments showed the hybrid metaheuristics to produce a Pareto optimal set with a controllable dynamic number of options whose score and cost value range can be controlled via parameters that can be modified without a significant effect on execution time.