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This paper examined the impact of a network attack on a congested transmission session. The research is motivated by the fact that the previous research community has neglected to evaluate security issues related to network congestion environments, and has instead concentrated on resolving congestion issues only. At any point in time, attackers can take advantage of the congestion problem, exploit the attack surface, and inject attack vectors. In order to circumvent this issue, a machine learning algorithm is trained to correlate attack vectors from the attack surface in a network congestion signals environment with the value of decisions over time in order to maximise expected attack vectors from the attack surface. Experimental scenario that dwell on transmission rate overwhelming transmission session, resulting in a standing queue was used. The experiment produced a dataset in which a TCP transmission through bursting transmission were capture. The data was acquired using a variety of experimental scenarios. Nave Bayes, and K-Nearest Neighbours prediction analyses demonstrate strong prediction performance. As a result, this study re-establishes the association between attack surface and vectors with network attack prediction. [ABSTRACT FROM AUTHOR]

COVID-19 and other neighbouring diseases spread widely, resulting in a global epidemic that was impossible to manage and control. While numerous measures have been put in place to detect an infected person and protect uninfected areas from contracting these contagious diseases, the spread of diseases like COVID-19 continues to be rapid. As of the time of writing this paper, the number of affected people has continued to rise, and there is no clear indication of the number of people who are infected but have gone undiscovered and are spreading the infections. That is why, in order to combat the threat of contiguous disease spread, this research presented an upgraded autonomous socio-contact tracing system on a mobile platform. As a result, a generic system development process was used to create a system that allows an infected person who has been tested positive to track their electromagnetic ID card in order to determine their exact location and the risk of spreading contagious diseases. by an autonomous smart assistant that assists in describing symptoms. As a result, the system is equipped with notifications alerts for the stages of social group identification, processing, and control in order to avoid the spread of contagious diseases. The development of this system is critical for controlling epidemic diseases that are spreading over the world (particularly COVID-19) and posing a threat to people's lives. Furthermore, it contributes to a greater understanding of the seriousness of epidemic diseases and how to avoid them. [ABSTRACT FROM AUTHOR]

With the tremendous technological progress and the widespread use of a variety of technologies, we note how smart cities are providing services efficiently by using technologies. The aim of this project is to build a Smart Traffic Control System (STCS) to facilitate and optimize traffic flow, minimize traffic congestion, and reduce the waiting time by detecting the density on each street. This work has been carried on four phases. Firstly, collecting data by a questionnaire and we received 331 responses. Secondly, using Proteus simulation. Thirdly, building a low fidelity prototype, and fourthly: building the STCS model by using hardware (Arduino tools) and software (Arduino Software IDE). Finally, we learned how to build a system and we recommend using such a system in busy roads to reduced congestion and making traffic flow more efficient. [ABSTRACT FROM AUTHOR]