Virtual reality (VR) and Augmented reality (AR) are the cutting-edge technological innovations that is going to shape how members of the society live and interact in future. In recent years, such technologies have been successfully implemented in various sectors including, military, education, healthcare, gaming among others. In the same way, its explosion more so in healthcare sector has resulted to various research being done that have revealed potential benefits and challenges in its adoption. This paper aimed at researching and providing an understanding of the role of VR and AR in healthcare systems as well as investigating its applications, potential benefits and challenges. The article applied exploratory research design to review the future applications, benefits and provide solutions to the challenges of VR and AR in healthcare. The review revealed that despite the tremendous growth and potential of such technologies, challenges resulting from cost implication of the technologies, technical capabilities of devices, infrastructural issues have all impacted on adoption of VR and AR in healthcare sector. As a result of advancement in technology over years, most of the challenges have been addressed due to invention of computers with more processing power and screens with better resolution. However, the issues of data privacy and security of both healthcare professionals and patients need to be addressed. This can be achieved by stakeholders developing and implementing universally acceptable standards and procedures that will guide research, development, and implementation of such technologies. This calls for parties involved in the development and usage of this devices to be assured of data privacy and security in healthcare sector.
Due to challenges of COVID -19 pandemic, network is widely used and more networkthreats are evolving, therefore, there is a need to improve network tools in order tocontrol threats. Stateful firewall is a network tool that build up packet filters by keepingrecord of packet passing through the network in a state table, so that when a newpacket arrives, the stateful firewall filtering mechanism first checks to determine whetherthe information is similar to the one in state table, in order to allow or blocked a packet.Although several stateful firewall models have been developed to filter network packets,there is no model that is able to filter the entire parts of a network packet which includethe header, trailer and payloads. In the stateful firewall models developed, mixedresearch methodology have been used. The models are developed in pythonprogramming language; an experimental research design is used, string matching andpattern matching algorithms are used in developing the models.
Firewall (software or hardware device that monitors traffic into and out of the network). It can be classified as stateless or stateful. The existing firewalls are only concerned with filtering packets based on the information contained in the header part of every packet. The most improved stateful inspection firewalls have a state table enabling the storage of header information such as source address, destination address, port, connection status and protocol. Consequently, existing firewalls can be compared to only reading the book tittle and foregoing other essential activities such as evaluating the content of the book. The proposed Deep packet analysis firewall model, not only evaluated the header content of a packet but also open and examines the content in a packet in order to detect and block any threats. In addition, the proposed model will be analyzing the actual content of the traffic that is flowing through packet as opposed to existing firewall which only focuses on analyzing the header content. The model will also locate, detect, categorize, block, or reroute packets having certain data payload and specific codes that are not located, detected, categorized, blocked or redirected by existing firewall. Therefore, deep packet analysis firewall model is a feasible approach to overcome challenges faced in cyberspace today. The proposed Deep packet analysis firewall model will use mixed research method. Quantitative method will include obtaining data from the peer reviewed academic articles in the area of study. Quantitative method will also entail using a simulation by feeding quantitative data into the model to produce quantitative results. Finally, qualitative method will include conducting interviews and use of questionnaires
As the use of software systems permeate diverse areas of the society, there is a need to ensure that not only does the software provide the needed functionality but it is also of high security, providing confidentiality, integrity and availability of the underlying data. Software security testing is one among the approaches towards detecting vulnerabilities and flaws in software which contribute to software insecurity. As machine learning finds success in other areas of computing, it has also gained interest in the field of software security testing. A review of the application of various machine learning techniques, including current trends in software security testing is of high value both to research and practice. This research provides an overview of how machine learning has been applied in software security testing and especially in the different phases of the testing cycle. Basic and recent developments of machine learning application in static analysis testing, dynamic analysis testing, symbolic execution and fuzz testing are discussed. The research followed a literature survey approach where existing literature on the subject were reviewed. A comparative performance of various machine learning techniques in the different phases of security testing is provided.
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