Network Traffic Analysis for Enhanced Protection in Critical Information Infrastructure
Ibitoye Akinfola Akinrinnola,
Aremu Idris Abiodun,
Odesanya Oluwafunsho Idowu
Issue:
Volume 6, Issue 5, September 2021
Pages:
71-76
Received:
25 April 2021
Accepted:
23 July 2021
Published:
28 October 2021
Abstract: Owing to the surfacing of new technologies which can abet easier and faster processing of data, there has been a increasing need for most organization to develop an online system. Most of these systems are developed using php programming language. This paper shows that most of these web-based systems are vulnerable to different types of attacks resulting from the disregard to security appraise during the development of the system. Security of information asset which are painstaking to any organization is one of the most important tasks which need to be safeguard regularly. Critical information infrastructure protection (CIIP) had become a lingering area of interest in the modern days of information dissemination, from the inauguration of internet to the modern elevated-profile dispersed denial-of-service assail against critical systems. Critical systems rely deeply on information communication; the interruptions of the information communication can obstruct the operation of critical systems. The western nations have advanced CIIP elucidation in place, but these elucidations are not always suitable for growing countries, where exceptional contest and necessities have to be addressed. Meanwhile, the western worlds are familiarizes with unmatched improvement of their information infrastructures. However, the lack of national CIIP efforts creates a situation for developing nations to become launch pads for cyber-attacks. In this paper, I have proposed a Network Traffic Analyzer that will analyze source codes of web based critical infrastructures and detect vulnerability in the codes for better security from attacks.
Abstract: Owing to the surfacing of new technologies which can abet easier and faster processing of data, there has been a increasing need for most organization to develop an online system. Most of these systems are developed using php programming language. This paper shows that most of these web-based systems are vulnerable to different types of attacks res...
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A Comparative Analysis of Mathematical and Linear Regression Models to Predict the Outcomes of COVID-19 Pandemic in Rwanda
Gratien Twagirumukiza,
Edouard Singirankabo,
Leopord Hakizimana
Issue:
Volume 6, Issue 5, September 2021
Pages:
77-82
Received:
30 August 2021
Accepted:
12 October 2021
Published:
28 October 2021
Abstract: The research objective was to investigate the level of COVID-19 outbreak in Rwanda using mathematical and linear models for future prediction of the disease. Both Mathematical model and linear model were used. A sequential mathematical preliminary of COVID-19 was considered to check how it grows within a large number of population. The model diagram was proposed with four compartmental model. The non-linear dynamical system of COVID-19 was derived from the model. The model was checked for positivity and boundedness in system. We found that it’s positively invariant in system. The results also showed that the disease is locally and globally unstable due to the fact that the basic reproduction number is greater than zero i.e., R0 > 0. The basic reproduction number was computed using the next generation Matrix and found that COVID-19 affects a very large population in the system. Method for real data: The study used a sample of 463 COVID-19 daily reports, that is, the available data by 9 April 2021. The data are analyzed using Statistical software (STATA version 13.1). The probability of skewness and kurtosis was P ≤ 0.0001 for New cases, and New deaths. Besides Chi-Square p ≤ 0.0001 for both New cases and New deaths was < 0.05 that means the significance at a 5% level. Results: By comparing the mean and standard deviation, the results show that the number of New cases is higher than that of New deaths, that is 50.00432 with high standard deviation 78.47841, and 0.6781857 with low standard deviation 1.474935; respectively. A spearman rank correlation shows strong correlation between New cases and New deaths. Linear regression analysis model shows that there is a linear relationship of New cases with New deaths. The findings show that the number of deaths will be higher than New cases. Conclusion: The statistics show that COVID-19 is still there within individuals and is moving around. The findings show that in future, the number of new deaths will be higher than that of new cases at a time t. We recommend the government of Rwanda to speed up the Vaccination to the total population to avoid more future deaths due to COVID-19 and to strictly tightening the preventive measures for both Rwandans and incoming travelers. With the above mentioned strategies and the measures, there is a hope that If the whole country is vaccinated, COVID-19 will vanish at time t.
Abstract: The research objective was to investigate the level of COVID-19 outbreak in Rwanda using mathematical and linear models for future prediction of the disease. Both Mathematical model and linear model were used. A sequential mathematical preliminary of COVID-19 was considered to check how it grows within a large number of population. The model diagra...
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