Journal of Advances in Mathematics and Computer Science

In March of 2018, about 500,000 desktop computers were infected with cryptocurrency mining malware in less than 24 hours. In addition to attacking desktop computers, malware also attacks laptops, tablets, mobile phones. That is, any device connected via the Internet, or a network is at risk of being attacked. In recent years, mobile phones have become extremely popular that places them as a big target of malware infections. In this study, the effectiveness of treatment for infected mobile devices is examined using compartmental modeling. Many studies have considered malware infections which also include treatment effectiveness. However, in this study we examine the treatment effectiveness of mobile devices based on the type of malware infections accrued (hostile or malicious malware). This model considers six classes of mobile devices based on their epidemiological status: susceptible, exposed, infected by hostile malware, infected by malicious malware, quarantined, and recovered. The malware reproduction number, R_M, was identied to discover the threshold values for the dynamics of malware infections to become both prevalent or absent among mobile devices. Numerical simulations of the model give insights of various strategies that can be implemented to control malware epidemic in a mobile network.

In this paper, explicit determinants and inverses of skew symmetric generalized Foeplitz matrices are given by constructing the special transformation matrices.

A new compound distribution called the Marshall-Olkin Inverse Lomax distribution (MO-ILD) was proposed, extending the inverse Lomax distribution by adding a new parameter to the existing distribution, leading to greater flexibility in modeling various data types. Its basic statistical properties were derived and model parameters estimated using the method of maximum likelihood. The Proposed distribution was applied to Cancer Stem Cell data and compared to the Marshall Olkin Flexible Weibull Extension Distribution (MO-FWED), and the Marshall-Olkin exponential Weibull distribution (MO-EWD). The Marshall-Olkin Inverse Lomax distribution provided a better fit than the Marshall Olkin Flexible Weibull Extension Distribution, and the Marshall-Olkin exponential Weibull distribution based on log-likelihood AIC, CAIC, BIC and HQIC values.

Online Social Network (OSN) is like a virtual community where people build social networks and relations with one another. The open access to the Internet has increased the growth of OSN which has attracted intruders to exploit the weaknesses of the Internet and OSN to their own gain. The rise in the usage of OSN has posed security threats to OSN users as they share personal and sensitive information online which could be exploited by these intruders by creating profiles to carry out a series of malicious activities on the social network. In fact, it is no gain saying that the intent of creating fake accounts has adverse effect and the Internet has made it quite easy to concede one’s identity; and this makes it difficult to detect fake accounts as they try to imitate real accounts. In this study, a model that can accurately identify fake profiles in OSN which uses Natural Language Processing Technique to eliminate or reduce the size of the dataset thereby improving the overall performance of the model was proposed.  Principal Component Analysis was used for appropriate feature selection. After extraction, six attributes/features that influenced the classifier were found. Support Vector Machine (SVM), Naïve Bayes and Improved Support Vector Machine (ISVM) were used as Classifiers. ISVM introduced a penalty parameter to the standard SVM objective function to reduce the inequality constraints between the slack variables. This gave a better result of 90% than the SVM and Naïve Bayes which gave 77.4% and 77.3% respectively.

One of the most reliable biometrics when issues of access control and security is been considered is face recognition. An integral part of a face recognition system is the feature extraction stage, which becomes a critical problem where is a need to obtain the best feature with minimum classification error and low running time. Many of the existing face recognition systems have adopted different linear discriminant-based algorithms independently for feature extraction in which excellent performance were achieved, but identifying the best most suitable of these variants of linear discriminant-based algorithms for face recognition systems remains a subject open for research. Therefore, this paper carried out a comparative analysis of the performance of the basic Linear Discriminant Algorithm (LDA) and two of its variants which are Kernel Linear Discriminant Analysis (KLDA) and Multiclass Linear Discriminant Analysis (MLDA) in face recognition application for access control.

Three Hundred and forty (340) face images were locally acquired with default size of 1200 x 1200. Two hundred and forty (240) images were used for training while the remaining hundred (100) images were used for testing purpose. The image enhancement involves converting into grayscale and normalizing the acquired images using histogram equalization method. Feature extraction and dimension reduction of the images were done using each of LDA, KLDA and MLDA algorithms individually. The extracted feature subsets of the images from each of LDA, KLDA and MLDA algorithm were individually classified using Euclidian distance. This technique was implemented using Matrix Laboratory (R2015a). The performance of LDA, KLDA and MLDA was evaluated and compared at 200 x 200 pixel resolution and 0.57 threshold value using recognition accuracy, sensitivity, specificity, false positive rate, training time and recognition time.

The evaluation result shows that the LDA algorithm yielded recognition accuracy, sensitivity, specificity, false positive rate, training time and recognition time of 93.00%, 92.86%, 93.33%, 6.67%, 1311.76 seconds and 67.98 seconds respectively. Also, KLDA recorded recognition accuracy, sensitivity, specificity, false positive rate, training time and recognition time of 95.00%, 95.71%, 93.33%, 6.67%, 1393.24 seconds and 63.67 seconds respectively. Furthermore, MLDA algorithm yielded recognition accuracy, sensitivity, specificity, false positive rate, training time and recognition time of 97.00%, 97.14%, 96.67%, 3.33%, 1191.55 seconds and 58.65 seconds respectively. The t-test measured between the accuracies of MLDA algorithm and KLDA reveals that MLDA algorithm was statistically significant at . Also, the t-test measured between the accuracies of MLDA algorithm and LDA reveals that MLDA algorithm was statistically significant at .