Shakirat Aderonke Salihu
@unilorin.edu.ng
Lecturer, Faculty of Communication and Information Sciences
University of Ilorin, Ilorin
Shakirat A. Salihu is a lecturer at the Department of Computer Science, Faculty of Communication and Information Sciences, University of Ilorin, Ilorin, Nigeria. She received her B.Sc. in Computer Science in 2006 from the University of Ilorin, and subsequently obtained her M. Sc and Ph.D. degrees from the same University in 2011 and 2022 respectively. Her academic career began at Federal Polytechnic, Mubi, Adamawa State, Nigeria as an Assistant Lecturer in 2010 and rose to the level of Lecturer III in 2013. She later joined the service of the University of Ilorin as a lecturer in the Department of Computer Science in 2014. Her research interest includes Software Maintenance, Information Retrieval, Natural Language Processing and Machine Learning.
EDUCATION
B. Sc, M.Sc. and Ph.D Computer Science
Postgraduate Diploma in Education
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Artificial Intelligence, Information Systems, Software
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Akande Hakeem Babalola, Oloyede Ayopo Abdulkarim, Shakirat Aderonke Salihu, and Taibat O. Adebakin
Springer Nature Switzerland
Fatima E. Usman-Hamza, Abdullateef O. Balogun, Ramoni T. Amosa, Luiz Fernando Capretz, Hammed A. Mojeed, Shakirat A. Salihu, Abimbola G. Akintola, and Modinat A. Mabayoje
Elsevier BV
Fatima E. Usman-Hamza, Abdullateef O. Balogun, Salahdeen K. Nasiru, Luiz Fernando Capretz, Hammed A. Mojeed, Shakirat A. Salihu, Abimbola G. Akintola, Modinat A. Mabayoje, and Joseph B. Awotunde
Elsevier BV
Hakeem Babalola Akande, Oluwatobi Noah Akande, and Shakirat Aderonke Salihu
IEEE
The invention of the 5G network, which has facilitated novel innovations and applications was prompted by the escalating need for higher data rates and larger capacity. Nevertheless, this rising demand can lead to improper control of data and control traffic, which would make the 5G network more difficult to scale and flexible. These issues can be resolved with Software-Defined Networking (SDN), which separates the network's control and data planes for simpler management. As a result, this article suggested a 5G architecture built on SDN and loaded with load balancing. This paper's specific goal is to simulate and assess the suggested SDN architecture for the 5G core network. Simulation tools such as Mininet, Wireshark, and MATLAB were utilized. Experimental results showed that the proposed system performed better than the conventional system in terms of throughput, latency, arrival time, and resource allocation.
Fatima Enehezei Usman-Hamza, Abdullateef Oluwagbemiga Balogun, Luiz Fernando Capretz, Hammed Adeleye Mojeed, Saipunidzam Mahamad, Shakirat Aderonke Salihu, Abimbola Ganiyat Akintola, Shuib Basri, Ramoni Tirimisiyu Amosa, and Nasiru Kehinde Salahdeen
MDPI AG
Customer churn is a critical issue impacting enterprises and organizations, particularly in the emerging and highly competitive telecommunications industry. It is important to researchers and industry analysts interested in projecting customer behavior to separate churn from non-churn consumers. The fundamental incentive is a firm’s intent desire to keep current consumers, along with the exorbitant expense of gaining new ones. Many solutions have been developed to address customer churn prediction (CCP), such as rule-based and machine learning (ML) solutions. However, the issue of scalability and robustness of rule-based customer churn solutions is a critical drawback, while the imbalanced nature of churn datasets has a detrimental impact on the prediction efficacy of conventional ML techniques in CCP. As a result, in this study, we developed intelligent decision forest (DF) models for CCP in telecommunication. Specifically, we investigated the prediction performances of the logistic model tree (LMT), random forest (RF), and Functional Trees (FT) as DF models and enhanced DF (LMT, RF, and FT) models based on weighted soft voting and weighted stacking methods. Extensive experimentation was performed to ascertain the efficacy of the suggested DF models utilizing publicly accessible benchmark telecom CCP datasets. The suggested DF models efficiently distinguish churn from non-churn consumers in the presence of the class imbalance problem. In addition, when compared to baseline and existing ML-based CCP methods, comparative findings showed that the proposed DF models provided superior prediction performances and optimal solutions for CCP in the telecom industry. Hence, the development and deployment of DF-based models for CCP and applicable ML tasks are recommended.
Abimbola G. Akintola, Abdullateef O. Balogun, Luiz Fernando Capretz, Hammed A. Mojeed, Shuib Basri, Shakirat A. Salihu, Fatima E. Usman-Hamza, Peter O. Sadiku, Ghaniyyat B. Balogun, and Zubair O. Alanamu
MDPI AG
As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware. The most extensively used method for identifying Android malware is signature-based detection. The drawback of this method, however, is that it is unable to detect unknown malware. As a consequence of this problem, machine learning (ML) methods for detecting and classifying malware applications were developed. The goal of conventional ML approaches is to improve classification accuracy. However, owing to imbalanced real-world datasets, the traditional classification algorithms perform poorly in detecting malicious apps. As a result, in this study, we developed a meta-learning approach based on the forest penalizing attribute (FPA) classification algorithm for detecting malware applications. In other words, with this research, we investigated how to improve Android malware detection by applying empirical analysis of FPA and its enhanced variants (Cas_FPA and RoF_FPA). The proposed FPA and its enhanced variants were tested using the Malgenome and Drebin Android malware datasets, which contain features gathered from both static and dynamic Android malware analysis. Furthermore, the findings obtained using the proposed technique were compared with baseline classifiers and existing malware detection methods to validate their effectiveness in detecting malware application families. Based on the findings, FPA outperforms the baseline classifiers and existing ML-based Android malware detection models in dealing with the unbalanced family categorization of Android malware apps, with an accuracy of 98.94% and an area under curve (AUC) value of 0.999. Hence, further development and deployment of FPA-based meta-learners for Android malware detection and other cybersecurity threats is recommended.
Shakirat Aderonke Salihu, Idowu Dauda Oladipo, Abdul Afeez Wojuade, Muyideen Abdulraheem, Abdulrauph Olanrewaju Babatunde, Adeleke Raheem Ajiboye, and Ghaniyyat Bolanle Balogun
IEEE
Phishing is one of the types of cybercrime in which the attacker poses as a trustworthy entity with a view to obtaining sensitive information or data from the victim, this occurs usually through email. In the process, the victim may release information such as login credentials, credit card details, and other personally identifiable information that normally should not be revealed. The existing approaches used for phishing detection, therefore, need to be enhanced to effectively detect phishing. This study proposed a novel method for detecting phishing based on some heuristic features by extracting some relevant attributes, filtering these attributes, and classifying the same according to their impact on a website. The data explored for this study was retrieved from PhishTank and Alexa, which was later preprocessed for smooth model creation in python. The model created was evaluated and consistently gives a true positive rate of 85% based on the threshold set and an accuracy of 95.52%. The resulting output of this study has shown its reliability in the detection of phishing and could serve as a good benchmark for similar studies.
Babajide J. Odejide, Amos O. Bajeh, Abdullateef O. Balogun, Zubair O. Alanamu, Kayode S. Adewole, Abimbola G. Akintola, Shakirat A. Salihu, Fatima E. Usman-Hamza, and Hammed A. Mojeed
Springer International Publishing
Abimbola Ganiyat Akintola, Abdullateef Balogun, Hammed Adeleke Mojeed, Fatima Usman-Hamza, Shakirat Aderonke Salihu, Kayode Sakariyau Adewole, Ghaniyyat Bolanale Balogun, and Peter Ogirima Sadiku
International Association of Online Engineering (IAOE)
Due to the exponential rise of mobile technology, a slew of new mobile security concerns has surfaced recently. To address the hazards connected with malware, many approaches have been developed. Signature-based detection is the most widely used approach for detecting Android malware. This approach has the disadvantage of being unable to identify unknown malware. As a result of this issue, machine learning (ML) for identifying and categorising malware apps was created. Conventional ML methods are concerned with increasing classification accuracy. However, the standard classification method performs poorly in recognising malware applications due to the unbalanced real-world datasets. In this study, an empirical analysis of the detection performance of ML methods in the presence of class imbalance is conducted. Specifically, eleven (11) ML methods with diverse computational complexities were investigated. Also, a synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) are deployed to address the class imbalance in the Android malware datasets. The experimented ML methods are tested using the Malgenome and Drebin Android malware datasets that contain features gathered from both static and dynamic malware approaches. According to the experimental findings, the performance of each experimented ML method varies across the datasets. Moreover, the presence of class imbalance deteriorated the performance of the ML methods as their performances were amplified with the deployment of data sampling methods (SMOTE and RUS) used to alleviate the class imbalance problem. Besides, ML models with SMOTE technique are superior to other experimented methods. It is therefore recommended to address the inherent class imbalance problem in Android Malware detection.
Abdullateef O. Balogun, Hammed A. Mojeed, Kayode S. Adewole, Abimbola G. Akintola, Shakirat A. Salihu, Amos O. Bajeh, and Rasheed G. Jimoh
Springer International Publishing
Oluwakemi Christiana Abikoye, Amos Orenyi Bajeh, Joseph Bamidele Awotunde, Ahmed Oloduowo Ameen, Hammed Adeleye Mojeed, Muyideen Abdulraheem, Idowu Dauda Oladipo, and Shakirat Aderonke Salihu
Springer International Publishing
Amos Orenyi Bajeh, Hammed Adeleye Mojeed, Ahmed Oloduowo Ameen, Oluwakemi Christiana Abikoye, Shakirat Aderonke Salihu, Muyideen Abdulraheem, Idowu Dauda Oladipo, and Joseph Bamidele Awotunde
Springer International Publishing
Amos Orenyi Bajeh, Oluwakemi Christiana Abikoye, Hammed Adeleye Mojeed, Shakirat Aderonke Salihu, Idowu Dauda Oladipo, Muyideen Abdulraheem, Joseph Bamidele Awotunde, Arun Kumar Sangaiah, and Kayode S. Adewole
Elsevier
Hammed A. Mojeed, Ummu Sanoh, Shakirat A. Salihu, Abdullateef O. Balogun, Amos O. Bajeh, Abimbola G. Akintola, Modinat A. Mabayoje, and Fatimah E. Usman-Hamzah
Springer International Publishing
Kayode S. Adewole, Abimbola G. Akintola, Shakirat A. Salihu, Nasir Faruk, and Rasheed G. Jimoh
Springer International Publishing