Automation and Secure Birth Certificate Registration and Management Process Based on Biometrics and QR Codes

Most developing countries in Africa have civil registration processes with inherent challenges that inhibit their efficiency and accuracy. Attempts at modernization and improvement are on-going. In this article, a biometric framework that can be used to mitigate the challenges inherent in biometric data management for the civil registration system in Zambia is presented. The proposed framework presented is based on ISO models and adapted for most developing countries with low Information and Communication Technologies. A baseline study was undertaken to assess the understanding and appreciation of biometrics within the Zambian community and how this data might be stored or managed. A study of the Zambian civil registration business process has been undertaken and the authors propose an automated civil registration business process. Results obtained show that currently, Zambia does NOT have a biometric framework that supervises use of biometric data. The proposed model once adopted would form a secure framework for civil registration based on biometrics and ISO standards.

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biometric technology offers a great opportunity to identify individuals, authenticate individuals and separate individuals. Using these advantages, an election or voting model can be developed to perform elections for a country such as Zambia. Zambia currently uses a manual based voting or election model that heavily relies on paper presented documents that must be physically verified and or matched to existing prior collected information before an individual is allowed to participate in an election or a voting system. This paper proposes a frontal facial election based biometric model that can be used to rid the current election system of redundancy and introduce a paperless, accurate and efficient identification, authentication and voting process. A baseline study conducted shows that biometric authentication based on this proposed model improves a work related process such as a voting system. We start by introducing the elements that make a biometric model ideal, we then give an insight into the Zambian based election system and then we review various biometric technologies available and then finally introduce our biometric model.

Systems and networks will be compromised, almost always regardless of what network engineers do. It is then paramount to install and manage systems that have a capacity to identify all forms of intrusions and possibly prevent those intrusions and related attacks on a computer network especially bots. Bots are at the midpoint of most network problems because almost all major cybercrimes and breaches can be traced back to them. This paper reviews three ways in which an intrusion may occur on networks with a focus on botnets and ways in which botnet mitigation may be enhanced.

biometric technology offers a great opportunity to identify individuals, authenticate individuals and separate individuals. Using these advantages, an election or voting model can be developed to perform elections for a country such as Zambia. Zambia currently uses a manual based voting or election model that heavily relies on paper presented documents that must be physically verified and or matched to existing prior collected information before an individual is allowed to participate in an election or a voting system. This paper proposes a frontal facial election based biometric model that can be used to rid the current election system of redundancy and introduce a paperless, accurate and efficient identification, authentication and voting process. A baseline study conducted shows that biometric authentication based on this proposed model improves a work related process such as a voting system. We start by introducing the elements that make a biometric model ideal, we then give an insight into the Zambian based election system and then we review various biometric technologies available and then finally introduce our biometric model.

Systems and networks will be compromised, almost always regardless of what network engineers do. It is then paramount to install and manage systems that have a capacity to identify all forms of intrusions and possibly prevent those intrusions and related attacks on a computer network especially bots. Bots are at the midpoint of most network problems because almost all major cybercrimes and breaches can be traced back to them. This paper reviews three ways in which an intrusion may occur on networks with a focus on botnets and ways in which botnet mitigation may be enhanced.

Facial biometrics captures human facial physiological data, converts it into a data item variable so that this stored variable may be used to provide information security services, such as authentication, integrity management or identification that grants privileged access or control to the owner of that data variable. In this paper, we propose a model for student authentication based on facial biometrics. We recommend a secure model that can be used in the authentication and management of student information in the registration and access of resources, such as bursaries, student accommodation and library facilities at the University of Zambia. Since the model is based on biometrics, a baseline study was carried out to collect data from the general public, government entities, commercial banks, students, ICT regulators and schools on their understanding, use and acceptance of biometrics as an authentication tool. Factor analysis has been used to analyze the findings. The study establishes that performance expectancy, effort expectancy, social influence and user privacy are key determinants for application of a biometric multimode authentication. The study further demonstrates that education and work experience are regulating factors on acceptance and expectancy of a biometric authentication system. Based on these results, we then developed a biometric model that can be used to perform authentication for students in higher learning institutions in Zambia. The results of our proposed model show 66% acceptance rate using OpenCV.