Enhancements and extensions in pervasive computing have enabled penetration of cloud computing enabled services into almost all walks of human life. The expansion of computational capabilities into everyday objects and processes optimizes end users requirement to directly interact with computing systems. However, the amalgamation of technologies like Cloud Computing, Internet of Things (IoT), Deep Learning etc are further giving way to creation of smart ecosystem for smart human living. This transformation in the whole pattern of living as well as working in enterprises is generating high expectations as well as performance load on existing cloud implementation as well as cloud services. In this complete scenario, there are simultaneous efforts on optimizing as well as securing cloud services as well as the data available on the cloud.
This manuscript is an attempt at introducing how cloud computing has become pivotal in the current enterprise setting due to its pay-as -you -use character. However, the allurement of using services without having to procure and retain involved hardware and software also has certain risks involved. The main risk involved in choosing cloud is compromising security concerns. Many potential customers avoid migrating towards cloud due to security concerns. Security concerns for the cloud implementations in the recent times have grown exponentially for all the varied stakeholders involved. The aim of this manuscript is to analyze the current security challenges in the existing cloud implementations. We provide a detailed analysis of existing cloud security taxonomies enabling the reader to make an informed decision on what combination of services and technologies could be used or hired to secure their data available on the cloud.

Researchers have developed an innovative approach to ensure seamless connectivity in ubiquitous networks with limited or irregular network coverage. The proposed method leverages advanced network technologies and protocols to seamlessly establish and maintain network connections across various environments. It integrates multiple wireless communication technologies and dynamic network selection algorithms, overcoming issues like poor reliability, limited scalability, and security problems. Compared to existing solutions, the method exhibits improved connection handover efficiency, network throughput, and end-to-end delay. Considering user mobility, network availability, and quality of service needs, it makes informed decisions about the most suitable network connections. The proposed method is expected to significantly impact the development of future ubiquitous networking solutions.

Due to the epidemic, the majority of users and businesses turned to the internet, necessitating the necessity to preserve the populace and safeguard their data. However, after being attacked, the expense of data protection runs into the millions of dollars. The phrase "Protection is better than cure" is true. The paper deals with profiling the node for safeguarding against the cyberattack. There is a lot of research on network nodes. Here, we address the requirement to profile the node before utilizing machine learning to separate the data. In order to scan the nodes for risks and save the nature of threat as a database, node profiling is being investigated. The data is then classified using a machine learning algorithm utilizing the database. This research focuses on the application of machine learning methods, specifically Gaussian Naive Bayes and Decision Trees, for the segmentation of cyberattacks in streaming data. Given the continuous nature of cyberattack data, Gaussian Naive Bayes is introduced as a suitable approach. The research methodology involves the development and comparison of these methods in classifying detected attacks. The Bayesian method is employed to classify detected attacks, emphasizing the use of Gaussian Naive Bayes due to its adaptability to streaming data. Decision Trees are also discussed and used for comparison in the results section. The research explores the theoretical foundations of these methods and their practical implementation in the context of cyberattack classification. After classification, the paper delves into the crucial task of identifying intrusions in the streaming data. The effectiveness of intrusion detection is highlighted, emphasizing the importance of minimizing false negatives and false positives in a real-world cybersecurity setting. The implementation and results section presents empirical findings based on the application of Gaussian Naive Bayes and Decision Trees to a dataset. Precision, recall, and accuracy metrics are used to evaluate the performance of these methods. The research concludes by discussing the implications of the findings and suggests that Gaussian Naive Bayes is a suitable choice for streaming data due to its adaptability and efficiency. It also emphasizes the need for continuous monitoring and detection of cyberattacks to enhance overall cybersecurity. The paper provides insights into the practical applicability of these methods and suggests future work in the field of intrusion detection.

The modern communication networks have evolved from simple-static systems to highly flexible and adaptive systems facilitating dynamic programmability and reconfiguration. This network evolution has influenced the lowest level of packet processing in data plane to highest level of network control and management functions. It has also influenced the overall network design and architecture which is clearly evident from the emergence of SDN and NFV. With the wide-spread acceptance of SDN, a novel networking paradigm, the network programmability has re-appeared as a top research area in networking and numerous programming languages have been proposed. In this paper, we present a systematic review of various state-of-the-art SDN programming languages used to program different network planes. We follow a top-down approach, starting with the high-level or top-tier programming languages followed by the data plane or bottom-tier programming languages. We have provided an in-depth analysis of various top-tier and bottom-tier programming languages and compared them in terms of most prominent features and supported abstractions. In addition to it, we have elaborated various programming models used in different bottom-tier programming languages which provide necessary abstractions for mapping diverse functionalities of data plane algorithms splendidly onto the specialized hardware like ASICs. Lastly, we have highlighted the research challenges in SDN programming languages like cross platform programming, necessary language libraries, support for network verification, NFV, stateful and inline packet processing, which need to be incorporated into existing programming languages to support diverse functions required in next generation networks.