Faculty of Computing

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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
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.
A Few-shot custom CNN Model for Retinal Nerve Fibre Layer Thickness Measurement in OCT Images of Epilepsy
(Proc. of International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (, 2024-02-01) Ruqayya Muhammad; Moussa Mahamat Boukar; Steve Adeshina; Senol Dane
This study aims to assess the effectiveness of employing deep learning models for measuring retinal nerve fiber layer (RNFL) thickness in optical coherence tomography (OCT) scans of epilepsy patients. Conventional OCT scan segmentation methods typically rely on supervised learning, demanding substantial data for training and assuming fixed network weights post-training. To mitigate these challenges, we explore the applicability of few-shot learning (FSL) in CNN architectures, allowing dynamic fine-tuning of network weights with minimal additional data. Experimental results demonstrate enhanced segmentation accuracy, with the proposed Few shot Custom CNN achieving a notable 91% accuracy, surpassing both the Custom CNN (86%) and the OCT machine data. This suggests the superiority of the few-shot Custom CNN model in segmentation performance compared to OCT scans.
A framework for Poultry weather control with IoT in sub-Saharan Africa
(15th International Conference on Electronics Computer and Computation (ICECCO 2019), 2019-02-02) Nasiru Afeez; Steve Adeshina; Abdullahi Inci; Moussa Mahamat Boukar
Poultry farming in the sub-Saharan region of Africa is fraught with a lot of challenges among which are high temperature and humidity. In this paper, the authors proposed an Internet of Things (IoT) framework that will help in regulating the various climatic conditions that will help at providing a high yield of poultry products. This framework is aimed at providing proactive and preventive ways to avert or reduce the high mortality rate in a flock of birds as a result of heat stress. IoT which is a connected environment of monitoring sensors with high precision and an accurate decision taken would be presented in managing environmental conditions of poultry house that will gather information, analyze it and effect an action based on the predetermined weather conditions that are suitable for bird’s existence
A Hybrid Approach for Reverse Engineering GUI Model from Android Apps for Automated Testing
(Journal of Telecommunication, Electronic and Computer Engineering, 2017-02-02) Ibrahim Anka Salihu
Nowadays, smartphone users are increasingly relying on mobile applications to complete most of their daily tasks. As such, mobile applications are becoming more and more complex. Therefore, software testers can no longer rely on manual testing methods to test mobile applications. Automated model-based testing techniques are recently used to test mobile applications. However, the models generated by existing techniques are of insufficient quality. This paper proposed a hybrid technique for reverse engineering graphical user interface (GUI) model from mobile applications. It performs static analysis of application’s bytecode to extract GUI information followed by a dynamic crawling to systematically explore and reverse engineer a model of the application under test. A case study was performed on real-world mobile apps to evaluate the effectiveness of the technique. The results showed that the proposed technique can generate a model with high coverage of mobile apps behaviour.
A Machine Learning Led Investigation Predicting the Thermos‑mechanical Properties of Novel Waste‑based Composite in Construction
(Waste and Biomass Valorization, 2024-05-04) Assia Aboubakar Mahamat; Moussa Mahamat Boukar; Ifeyinwa Ijeoma Obianyo; Nurudeen M. Ibrahim
The study explores the potential of machine learning (ML) in predicting the thermal and mechanical properties of earth-based composites reinforced with natural Borassus fruit fiber. The limited availability of large datasets for accurate predictions is a challenge in material science research, which this study addresses. The authors collected data on thermal conductivity, compressive and flexural strength through experiments and employed four ML techniques suitable for small datasets: linear regression (LR), random forest (RF), decision tree regressor (DTR), and gradient boosting (GB). Evaluation metrics were used to assess the performance of the ML techniques. Linear regression emerged as the most efficient, exhibiting significantly lower error values compared to the others (e.g., RMSE of 0.066 for thermal conductivity, 0.119 for compressive strength, and 0.04 for flexural strength), followed by random forest and decision tree. However, gradient boosting showed relatively poor predictive accuracy. This study demonstrates the successful application of ML for predicting the properties of earth-based composites with limited data, which could significantly reduce the cost and time associated with developing new building materials and products. Manufacturers can gain a competitive edge by using ML to streamline material development, leading to lower costs, faster innovation, and the creation of more environmentally friendly building materials for a greener construction sector.
A Multi-Indexes Based Technique for Resolving Collision in a Hash Table
(IJCSNS International Journal of Computer Science and Network Security, 2021-09-20) Saleh Abdullahi; Moussa Mahamat Boukar
The rapid development of various applications in networking system, business, medical, education, and other domains that use basic data access operations such as insert, edit, delete and search makes data structure venerable and crucial in providing an efficient method for day to day operations of those numerous applications. One of the major problems of those applications is achieving constant time to search a key from a collection. A number of different methods which attempt to achieve that have been discovered by researchers over the years with different performance behaviors. This work evaluated these methods, and found out that almost all the existing methods have non-constant time for adding and searching a key. In this work, we designed a multi-indexes hashing algorithm that handles a collision in a hash table T efficiently and achieved constant time O(1) for searching and adding a key. Our method employed two-level of hashing which uses pattern extraction h1(key) and h2(key). The second hash function h2(key) is use for handling collision in T. Here, we eliminated the wasted slots in the search space T which is another problem associated with the existing methods
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
A Review of Fraudulent Practices in Healthcare Insurance and Machine Learning-Based Investigation Approaches
(IEEE, 2023-02-02) Aishat Salau; Nwojo Agwu Nnanna; Moussa Mahamat Boukar
Healthcare insurance fraud is a complex and costly problem that has become a concern globally. Traditional methods of detecting fraudulent claims and requests are time-consuming and often ineffective. Machine learning methods offer potential solutions to this problem by improving fraud investigation and prevention in health insurance systems. This paper presents a comprehensive review of machine learning-based approaches for addressing healthcare insurance fraud, as well as associated challenges and limitations. Despite limitations, our findings suggest that fraud could be effectively tackled by addressing the challenges identified. Areas for further research were also highlighted.
A Static-dynamic Approach for UI Model Generation for Mobile Applications
(IEEE, 2018-02-02) Ibrahim Anka Salihu
Nowadays, smartphone users are increasingly relying on mobile applications to complete most of their daily tasks. To ensure acceptable quality and to meet its specifications, mobile apps need to be tested thoroughly. As testing mobile apps becomes challenging and tedious, test automation can alleviate this process. Model-based testing is an approach for test automation that is popularly used to test mobile applications. In order to benefit from model-based testing, there is a need for technique and tool for automated model generation. Therefore, this paper presents a hybrid approach for automated User Interface (UI) model generation for mobile applications. It performs static analysis of application’s bytecode to extract UI information, followed by a dynamic crawling to systematically explore and reverse engineer a model of the application under test. We then evaluate our approach on several open-source mobile applications. The results showed that our approach can generate a high-quality model from mobile applications.
A User Readiness Model of Social Media for Learning among Polytechnic Students in Nigeria
(Path of Science, 2019-06-30) Ibrahim Anka Salihu
The adoption of Internet resources for learning continues to grow in the world today. Despite the abundant benefit of utilizing social media due to the growth of web 2.0, an internet resource for communication and interaction, its use has not been fully embraced as a teaching tool in Nigeria. Social media is becoming a prominent communication tool and found to be facilitating teaching and learning activities among students. However, the user readiness of social media in learning by the students has been challenging. Despite the wide acceptance of social media (such as Facebook, Twitter, and WhatsApp, e.t.c.) amongst Nigerian polytechnic students, they do not utilize it for academic pursuit. This study examined the Use of Social Media among students in Nigerian Polytechnics. The main objective of this study is to find out the user readiness’ factors that influence the use of social media by the students in Nigerian Polytechnic. The evaluation results show that social media is an indispensable Internet platform among Nigerian Polytechnic students.
Advancing Preauthorization Task in Healthcare: An Application of Deep Active Incremental Learning for Medical Text Classification
(Engineering, Technology & Applied Science Research, 2023-09-29) Nnanna Agwu Nwojo; Moussa Mahamat Boukar
This study presents a novel approach to medical text classification using a deep active incremental learning model, aiming to improve the automation of the preauthorization process in medical health insurance. By automating decision-making for request approval or denial through text classification techniques, the primary focus is on real-time prediction, utilization of limited labeled data, and continuous model improvement. The proposed approach combines a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with active learning, using uncertainty sampling to facilitate expert-based sample selection and online learning for continuous updates. The proposed model demonstrates improved predictive accuracy over a baseline Long Short-Term Memory (LSTM) model. Through active learning iterations, the proposed model achieved a 4% improvement in balanced accuracy over 100 iterations, underscoring its efficiency in continuous refinement using limited labeled data.
Age Estimation from Facial Images Using Custom Convolutional Neural Network (CNN)
(International Conference on Frontiers in Academic Research, 2023-02-23) Gilbert George; Steve Adeshina; Moussa Mahamat Boukar
Given that aging is influenced by a variety of factors, including gender, ethnicity, environment, and others, automatic age assessment of facial images is a difficult challenge in computer vision and image analysis. Additionally, a significant amount of data and a laborious training phase are needed to estimate age from facial photos with near accuracy. In this study, we present a custom convolutional neural network-based age estimator that can almost precisely predict age from facial photos. We use the UTK facial image dataset using about 17475 images. We train the model to group the facial images into three groups which are; Child, Teenager and Adult. Compared to similar efforts, our method uses less training data while maintaining a high accuracy of 95%.
AMOGA: A Static-Dynamic Model Generation Strategy for Mobile Apps Testing
(IEEE, 2019-01-31) Ibrahim Anka Salihu
In the past few years, mobile devices have been increasingly replacing traditional computers, as their capabilities, such as CPU computation, memory, RAM size, and many more, are being enhanced almost to the level of conventional computers. These capabilities are being exploited by mobile apps developers to produce apps that offer more functionalities and optimized performance. To ensure acceptable quality and to meet their specifications (e.g., design), mobile apps need to be tested thoroughly. As the testing process is often tedious, test automation can be the key to alleviating such laborious activities. In the context of the Android-based mobile apps, researchers and practitioners have proposed many approaches to automate the testing process mainly on the creation of the test suite. Although useful, most existing approaches rely on reverse engineering a model of the application under test for test case creation. Often, such approaches exhibit a lack of comprehensiveness, as the application model does not capture the dynamic behavior of the applications extensively due to the incompleteness of reverse engineering approaches. To address this issue, this paper proposes AMOGA, a strategy that uses a hybrid, static-dynamic approach for generating a user interface model from mobile apps for model-based testing. AMOGA implements a novel crawling technique that uses the event list of UI element associated with each event to dynamically exercise the events ordering at the run time to explore the applications’ behavior. An experimental evaluation was performed to assess the effectiveness of our strategy by measuring the code coverage and the fault detection capability through the use of mutation testing concept. The results of the experimental assessment showed that AMOGA represents an alternative approach for model-based testing of mobile apps by generating comprehensive models to improve the coverage of the applications. The strategy proved its effectiveness by achieving high code coverage and mutation score for different applications
An Error Analysis Algorithm for Approximate Solution of Linear Fredholm-Stieltjes Integral Equations with Generalized Trapezium Method
(IEEE, 2017-02-02) Moussa Mahamat Boukar
Integral equations and their solutions are very important for various areas like physics, engineering, biology and other. Fredholm-Stieltjes integral equations are some of the integral equations. Sometimes it is possible to find exact solutions for some of the integral equations.The main purpose of this paper is to propose an error analysis algorithm for approximate solution of linear Fredholm-Stieltjes integral equations of second kind with Generalized Trapezium Method. Firstly, the theory of error analysis is given. Then the implementation of algorithm is done with Maple software and examples are given with graphics.
An Interactive Application (Maplet) for II-Order Ordinary Differential Equations
(IEEE, 2014-02-02) Moussa Mahamat Boukar
The main purpose of this paper is to propose a Maplet interactive application that is used to find general solutions, to find Initial Value Problems (IVPs) and to depict 2-D and 3-D graphics as well of the II-Order Ordinary Homogeneousl Non-homogeneous Differential Equations (ODEs). Furthermore, to make 2-D, 3-D graphing of solutions and how they can be used as effective educational tools for both students and instructors.
Analysis of Bad Roads Using Smart phone
(IEEE, 2019-02-02) Moussa Mahamat Boukar; Steve Adeshina
Developing nations are faced with a lot of bad roads with potholes of different debt ranges, the maintenance and rehabilitation process by government agencies is an ongoing effort that requires periodic bad road inventory to guarantee safety. Bad roads are either identified by government agency’s survey teams or individual who volunteer to report these conditions to the authorities. Our research provided a simple but effective solution to aid in automatically reporting bad roads using smart-phones through measuring the pavement profile based on the vibration of a moving vehicle. In this article, we will explain how we used some a smart-phone in reading the vibration pattern, GPS location, speed and direction of a vehicle that drives through a pothole, these parameters are periodically streamed to a cloud application. We used standard deviation to measure the level of dispersion around a segmented set of streamed vehicle vibration to identify potholes of different sizes, we also used Artificial Intelligence - supervised learning algorithm (classification) to reduce the false positive error rates due to human behaviors. The final results show a distinct vibration levels between small pot-holes, speed bumps and big pot-holes, these values are displayed on map application to visualize the geographical locations of these pot-holes (Google maps)
Analysis of Prostate Cancer DNA Sequences Using Bi-direction Long Short Term Memory Model
(IEEE, 2021-02-02) Yusuf Aleshinloye Abass; Steve Adeshina; Nwojo Nnana Agwu; Moussa Mahamat Boukar
Machine and deep learning-based models are the emerging techniques in addressing prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that requires huge attention in the biomedical domain. These techniques have been shown to provide better accurate results when compared to the conventional regression-based models. Prediction of the gene sequence that leads to cancerous diseases such as prostate cancer is very crucial. Identifying the most important features in a gene sequence is one of the most challenging tasks and extracting the components of the gene sequence that can give an insight into the kind of mutation in the gene is very important, it will lead to effective drug design and promote the new concept of personalized medicine. In this work we have extracted the exons in the various prostate gene sequence that was used in the experiment, we built a bi-LSTM model using a k-mer encoding for the DNA sequence and one- hot encoding for the class label. The bi-LSTM model was evaluated on different classification metrics. Our experimental results show that the model prediction offers a training accuracy and validation accuracy of 95 percent and 91 percent respectively.
Automatic Classification of Equivalent Mutants in Mutation Testing of Android Applications
(MDPI, 2022-04-14) Bilkisu Muhammad-Bello; Ibrahim Anka Salihu
Software and symmetric testing methodologies are primarily used in detecting software defects, but these testing methodologies need to be optimized to mitigate the wasting of resources. As mobile applications are becoming more prevalent in recent times, the need to have mobile applications that satisfy software quality through testing cannot be overemphasized. Testing suites and software quality assurance techniques have also become prevalent, which underscores the need to evaluate the efficacy of these tools in the testing of the applications. Mutation testing is one such technique, which is the process of injecting small changes into the software under test (SUT), thereby creating mutants. These mutants are then tested using mutation testing techniques alongside the SUT to determine the effectiveness of test suites through mutation scoring. Although mutation testing is effective, the cost of implementing it, due to the problem of equivalent mutants, is very high. Many research works gave varying solutions to this problem, but none used a standardized dataset. In this research work, we employed a standard mutant dataset tool called MutantBench to generate our data. Subsequently, an Abstract Syntax Tree (AST) was used in conjunction with a tree-based convolutional neural network (TBCNN) as our deep learning model to automate the classification of the equivalent mutants to reduce the cost of mutation testing in software testing of android applications. The result shows that the proposed model produces a good accuracy rate of 94%, as well as other performance metrics such as recall (96%), precision (89%), F1-score (92%), and Matthew’s correlation coefficients (88%) with fewer False Negatives and False Positives during testing, which is significant as it implies that there is a decrease in the risk of misclassification.
BASIC DEPENDENCY PARSING IN NATURAL LANGUAGE INFERENCE
(IEEE, 2017-02-02) Aleshinloye Abass Yusuf; Nnanna Agwu Nwojo; Moussa Mahamat Boukar
Parsing is the process of analyzing a sentence for it structure, content and meaning, this process uncover the structure, articulate the constituents and the relation between the constituents of the input sentence. This paper described the importance of parsing strategy in achieving entailment in natural language inference. Parsing is the basic task in processing natural language and it is also the basis for all natural language applications such as machine learning, question answering and information retrieval. We have used the parsing strategy in natural language inference to achieve entailment through an approach called normalization approach where entailment is achieved by removing or replacing some nodes as well as relations in a tree. This process requires a detailed understanding of the dependency structure, in order to generate a tree that does not contain nodes and relations that are irrelevant to the inference procedure. In order to achieve this, the dependency trees are transformed by applying some rewrite rules to the dependency tree