Browsing by Author "Kazeem Alagbe Gbolagade"

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    An Enhanced Network Intrusion Detection System using Dimensionality Reduction Techniques
    (KASU Journal of Computer Science, 2024) Olufemi James Akinola; Sulaiman Olaniyi Abdulsalam; Michael Favour Edafeajiroke; Kazeem Alagbe Gbolagade
    Protecting the privacy and confidentiality of data in computer networks is crucial, necessitating reliable intrusion detection methods. The complexity and vast volume of data present challenges to effective intrusion detection, as traditional models struggle with high-dimensional data that reduce accuracy. This study addresses these challenges by integrating Mutual Information (MI) and Linear Discriminant Analysis (LDA) as feature selection and extraction techniques respectively, to enhance the classification performance of Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CIC-IDS 2017) dataset was used to develop an Intrusion Detection System (IDS). The Results findings revealed that integrating MI+LDA with SVM yielded an accuracy of 98.84% while combining MI+LDA with KNN produced an accuracy of 90.17%. The result of the MI+LDA+SVM model based on accuracy, sensitivity, specificity, and F1-score matrixes, outperformed that of MI+LDA with KNN and other existing models. Therefore, the incorporation of MI and LDA dimensionality reduction techniques proved reliable for boosting the performance of an intrusion detection system in computer networks. Future research could apply the result of this study to other types of computer network benchmark datasets and other kinds of machine learning techniques.
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    An Improved Image Scrambling Algorithm Using {2 n -1, 2 n , 2 n +1}
    (University of the West of Scotland, School of Computing, 2018-10-01) Damilare Peter Oyinloye; Kazeem Alagbe Gbolagade
    Traditional iris segmentation methods and strategies regularly contain an exhaustive search of a large parameter space, which is sensitive to noise, time-consuming and no longer secured enough. To address these challenges, this paper proposes a secured iris template. This paper proposes a technique to secure the iris template using steganography. The experimental analysis was carried out on matrix laboratory (MATLABR2015A) environment. The segmented iris region was normalized to decrease the dimensional inconsistencies between iris region areas with the aid of the usage of Hough transform (HT). The features of the iris were encoded by convolving the normalized iris region with 1D Log- Gabor filters in order to generate a bit-wise biometric template. Then, least significant bit (LSB) was used to secure the iris template. The Hamming distance was chosen as a matching metric, which gives the measure of how many bits disagreed between the templates of the iris. The system operated at a very good training time and high level of conditional testing signifying high optimization with recognition accuracy of 92% and error of 1.7%. The proposed system is reliable, secure and efficient with the computational complexity significantly reduced. The proposed technique provides an efficient approach for securing the iris template.
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    Hybrid Algorithm for Symmetric Based Fully Homomorphic Encryption
    (ICAI: International Conference on Applied Informatics, 2021-10-23) Kamaldeen Jimoh Muhammed; Rafiu Mope Isiaka; Ayisat Wuraola Asaju-Gbolagade; Kayode Sakariyah Adewole; Kazeem Alagbe Gbolagade
    Fully Homomorphic Encryption (FHE) supports realistic computations on encrypted data and hence it is widely proposed to be used in cloud computing to protect the integrity and privacy of data stored in the cloud. The existing symmetric-based FHE schemes suffer from insecurity against known plaintext/ciphertext attacks and generate a large ciphertext size that required a large bandwidth to transfer the ciphertext over the network. To ameliorate these weaknesses is the aim of this paper. A hybrid algorithm for symmetric-based Fully Homomorphic Encryption that combines N-prime model and Matrix Operation for Randomization and Encryption with Secret Information Moduli Set (MORESIMS) is proposed. The results show that the proposed hybrid symmetric-based FHE framework satisfied homomorphism properties with robust inbuilt security that resists known plaintext, ciphertext and statistical attacks. The ciphertext size produced by the proposed hybrid framework is less than 4 times of its equivalent plaintext size with a considerable encryption execution time and fast decryption time. Thus, guaranteed to provide optimum performance and reliable solutions for securing integrity and privacy of user’s data in the cloud.