Research Articles in Computer Science

Permanent URI for this collectionhttps://repository.nileuniversity.edu.ng/handle/123456789/50

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Start date: 2020End date: 2024

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Now showing 1 - 10 of 32
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    A Dynamic and Incremental Graphical Grid Authentication Technique for Mobile and Web Applications
    (2024-08-08) Gong Jiaming; Akande Oluwatobi Noah; Chia-Chen Lin; Agarwal Saurabh
    Knowledge-based authentication techniques remain one of the proven ways of maintaining confidentiality, ensuring integrity, and guaranteeing the availability of an information system. They employ what a user knows (Passwords or PINs) to authorize or grant access to an information system. While passwords employ a fixed combination of characters, Personal Identification Numbers (PINs) are majorly numbers. Existing implementations of these authentication techniques involve the repetitive use of static passwords and PINs at every login instance. These have been exposed to various attacks, such as keyloggers, shoulder surfing, brute force, and dictionary attacks. To overcome these attacks, this study presents an authentication technique where users’ PINs are incremented during successive login attempts. Users are expected to choose a preferred incremental factor, which can be any number they can remember, that will be added to the default 6-digit PIN to produce a dynamic PIN that can be used in subsequent login sessions. Furthermore, an additional layer of security that involves the use of a dynamic 4 by 4 graphical grid was integrated into the proposed incremented PIN technique. At every login session, users are presented with a set of 16 possible PINs to choose from. The security analysis of the proposed authentication technique revealed that the proposed technique could resist existing password attacks, thereby enhancing security. A performance testing and usability analysis was also carried out among 1145 individuals who interacted with the web application that uses the incremental authentication technique. The questionnaire items were structured based on the constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) Model. Statistical analysis of the responses received showed an appreciable level of acceptance in terms of performance expectancy, effort expectancy, social influence, and facilitating conditions. The positive user acceptance results provide reassurance about the practicality and effectiveness of the proposed technique. It is believed that the proposed incremental graphical grid authentication technique will further enhance the security of our growing mobile and web applications
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    A Dynamic Round Triple Data Encryption Standard Cryptographic Technique for Data Security
    (Springer Nature Switzerland AG, 2020-08-08) Akande Oluwatobi Noah; Abikoye Oluwakemi Christiana; Kayode Aderonke Anthonia; Aro Oladele Taye; Ogundokun Oluwaseun Roseline
    Cryptographic techniques have been widely employed to protect sensitive data from unauthorized access and manipulation. Among these cryptographic techniques, Data Encryption Standard (DES) has been widely employed, however, it suffers from key and differential attacks. To overcome these attacks, several DES modifications have been proposed in literatures. Most modifications have focused on enhancing DES encryption key; however, the strength of a cryptographic technique is determined by the encryption key used and the number of encryption rounds. It is a known fact that Advanced Encryption Standard (AES) cryptographic technique with 14 encryption rounds is stronger than AES with 12 rounds while AES with 12 rounds is stronger than AES with 10 rounds. Therefore, this study proposed a DES cryptographic technique whose number of rounds is dynamic. Users are expected to specify the number of encryption and decryption rounds to be employed at run time. Moreover, a predefined number of shifting operations which is left circular shift 2 was chosen for each encryption round. As, a trade-off in complexity, the number of Substitution box (S-box) was also reduced to 4, so that the input to the S-boxes would be arranged in four 12-bit blocks for the X-OR operation and not six 8-bit blocks as in the traditional DES. Finally, three keys were used to encrypt, decrypt and encrypt the plaintext ciphertext as in triple DES. The modified DES yielded a better avalanche effect for rounds greater than 16 though its encryption and decryption time were greater than that of the traditional DES.
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    A novel technique to prevent SQL injection and cross-site scripting attacks using Knuth-Morris-Pratt string match algorithm
    (Springer Open, 2020-08-08) Abikoye Oluwakemi Christiana; Abubakar Abdullahi; Dokoro Ahmed Haruna; Akande Oluwatobi Noah; Kayode Aderonke Anthonia
    Structured Query Language (SQL) injection and cross-site scripting remain a major threat to data-driven web applications. Instances where hackers obtain unrestricted access to back-end database of web applications so as to steal, edit, and destroy confidential data are increasing. Therefore, measures must be put in place to curtail the growing threats of SQL injection and XSS attacks. This study presents a technique for detecting and preventing these threats using Knuth-Morris-Pratt (KMP) string matching algorithm. The algorithm was used to match user’s input string with the stored pattern of the injection string in order to detect any malicious code. The implementation was carried out using PHP scripting language and Apache XAMPP Server. The security level of the technique was measured using different test cases of SQL injection, cross-site scripting (XSS), and encoded injection attacks. Results obtained revealed that the proposed technique was able to successfully detect and prevent the attacks, log the attack entry in the database, block the system using its mac address, and also generate a warning message. Therefore, the proposed technique proved to be more effective in detecting and preventing SQL injection and XSS attacks
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    Ethnicity and Biometric Uniqueness
    (2023-08-08) John Daugman; Cathryn Downing; Akande Oluwatobi Noah; Abikoye Oluwakemi Christiana
    We conducted more than 1.3 million comparisons of iris patterns encoded from images collected at two Nigerian universities, which constitute the newly available African Human Iris (AFHIRIS) database. The purpose was to discover whether ethnic differences in iris structure and appearance such as the textural feature size, as contrasted with an all-Chinese image database or an American database in which only 1.53% were of African-American heritage, made a material difference for iris discrimination. We measured a reduction in entropy for the AFHIRIS database due to the coarser iris features created by the thick anterior layer of melanocytes, and we found stochastic parameters that accurately model the relevant empirical distributions. Quantile-Quantile analysis revealed that a very small change in operational decision thresholds for the African database would compensate for the reduced entropy and generate the same performance in terms of resistance to False Matches. We conclude that despite demographic difference, individuality can be robustly discerned by comparison of iris patterns in this West African population
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    Remote Sensing Image Classification for Land Cover Mapping in Developing Countries
    (IJCSNS International Journal of Computer Science and Network Security, 2022-02-05) Nwojo Agwu Nnanna; Moussa Mahamat Boukar
    Convolutional Neural networks (CNNs) are a category of deep learning networks that have proven very effective in computer vision tasks such as image classification. Notwithstanding, not much has been seen in its use for remote sensing image classification in developing countries. This is majorly due to the scarcity of training data. Recently, transfer learning technique has successfully been used to develop state-of-the art models for remote sensing (RS) image classification tasks using training and testing data from well-known RS data repositories. However, the ability of such model to classify RS test data from a different dataset has not been sufficiently investigated. In this paper, we propose a deep CNN model that can classify RS test data from a dataset different from the training dataset. To achieve our objective, we first, re-trained a ResNet-50 model using EuroSAT, a large-scale RS dataset to develop a base model then we integrated Augmentation and Ensemble learning to improve its generalization ability. We further experimented on the ability of this model to classify a novel dataset (Nig_Images). The final classification results shows that our model achieves a 96% and 80% accuracy on EuroSAT and Nig_Images test data respectively. Adequate knowledge and usage of this framework is expected to encourage research and the usage of deep CNNs for land cover mapping in cases of lack of training data as obtainable in developing countries.
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    Test Case Generation Approach for Android Applications using Reinforcement Learning
    (Engineering, Technology & Applied Science Research, 2024-04-27) Moussa Mahamat Boukar; Muhammed Aliyu Suleiman; Ibrahim Anka Salihu
    Mobile applications can recognize their computational setting and adjust and respond to actions in the context. This is known as context-aware computing. Testing context-aware applications is difficult due to their dynamic nature, as the context is constantly changing. Most mobile testing tools and approaches focus only on GUI events, adding to the deficient coverage of applications throughout testing. Generating test cases for various context events in Android applications can be achieved using reinforcement learning algorithms. This study proposes an approach for generating Android application test cases based on Expected State-Action-Reward-State-Action (E-SARSA), considering GUI and context events for effective testing. The proposed method was experimentally evaluated on eight Android applications, showing 48- 96% line of code coverage across them, which was higher than Q-testing and SARSA.
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    Evaluation of Collision Resolution Methods Using Asymptotic Analysis
    (IEEE, 2021-02-02) Saleh Abdullahi; Moussa Mahamat Boukar; Salisu Ibrahim Yusuf
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    Graphic User Interface for Hausa Text-to-Speech System
    (IEEE, 2022-02-02) Umar Adam Ibrahim; Moussa Mahamat Boukar; Muhammed Aliyu Suleiman
    Natural language processing and Digital signal processing are broadly used methods used to enable systems to understand commands and manipulate speech or text. Most of the Text-to-speech done was for major languages such as English, French and others, with no or little for African languages like Hausa, which are termed under resource languages. In this paper, we developed a graphical user interface for the Hausa Text-to-Speech system. This system converts Hausa text to Hausa audio sound, by processing and analyzing it using natural language processing and Digital Signal Processing. Our graphical user interface, aid in converting entered Hausa language text into Hausa speech.
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    Hepatitis C Stage Classification with hybridization of GA and Chi2 Feature Selection
    (IJCSNS International Journal of Computer Science and Network Security, 2022-02-02) Steve Adeshina; Moussa Mahamat Boukar
    In metaheuristic algorithms such as Genetic Algorithm (GA), initial population has a significant impact as it affects the time such algorithm takes to obtain an optimal solution to the given problem. In addition, it may influence the quality of the solution obtained. In the machine learning field, feature selection is an important process to attaining a good performance model; Genetic algorithm has been utilized for this purpose by scientists. However, the characteristics of Genetic algorithm, namely random initial population generation from a vector of feature elements, may influence solution and execution time. In this paper, the use of a statistical algorithm has been introduced (Chi2) for feature relevant checks where p-values of conditional independence were considered. Features with low p-values were discarded and subject relevant subset of features to Genetic Algorithm. This is to gain a level of certainty of the fitness of features randomly selected. An ensembled-based learning model for Hepatitis has been developed for Hepatitis C stage classification. 1385 samples were used using Egyptian-dataset obtained from UCI repository. The comparative evaluation confirms decreased in execution time and an increase in model performance accuracy from 56% to 63%.
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    Improving the Accuracy of Animal Species Classification in Camera Trap Images Using Transfer Learning
    (International Conference on Artificial Intelligence, Computer, Data Sciences and Application, 2024-02-02) Moussa Mahamat Boukar
    Understanding biodiversity, monitoring endangered species, and estimating the possible effect of climate change on particular regions all rely on animal species identification. Closed-circuit television (CCTV) cameras, which can collect huge volumes of video data, are an excellent environmental monitoring tool. However, manually evaluating these massive datasets is time-consuming, difficult, and expensive, emphasizing the need for automated ecological analysis.Deep learning models have transformed computer vision, handling problems such as object and species detection. Their cutting-edge performance qualifies them for this application. The purpose of this work was to create and test machine learning models for distinguishing diverse animal species using camera trap images. On VGG19, GoogLeNet (InceptionV3), ResNet50, and DenseNet121, we used transfer learning. The best multi- classification accuracy was attained by GoogLeNet (87%), followed by ResNet50 (83%), DenseNet (81%), and VGG19 (53%). This evidence suggests that transfer learning outperforms training models from scratch for this task.