Server-less computing is a novel cloud-based paradigm that is gaining popularity today for running widely distributed applications. When it comes to server-less computing, features are available via subscription. Server-less computing is advantageous to developers since it lets them install and run programs without worrying about the underlying architecture. A common choice for code deployment these days, server-less design is preferred because of its independence, affordability, and simplicity. The healthcare industry is one excellent setting in which server-less computing can shine. In the existing literature, we can see that fewer studies have been put forward or explored in the area of server-less computing with respect to smart healthcare systems. A cloud infrastructure can help deliver services to both users and healthcare providers. The main aim of our research is to cover various topics on the implementation of server-less computing in the current healthcare sector. We have carried out our studies, which are adopted in the healthcare domain and reported on an in-depth analysis in this article. We have listed various issues and challenges, and various recommendations to adopt server-less computing in the healthcare sector.

As Advanced Persistent Threats (APTs) proliferate and evolve, they constitute an increasingly formidable challenge to organizational cybersecurity frameworks. The imperative for innovative, multifaceted detection methodologies has never been more critical. This manuscript elucidates a groundbreaking framework that ingeniously synergizes Central Processing Unit (CPU) utilization metrics with the principles of Zero-Trust architecture. Our approach scrutinizes the nuanced, idiosyncratic patterns of CPU utilization that are indicative of APT activities. Significantly, this method is adept at identifying hallmarks of APTs congruent with criteria delineated in the MITRE ATT&CK framework-specifically in stages antecedent to lateral movement tactics. Parallel to this, the framework assimilates the austere security policies typified by Zero Trust architecture, culminating in a holistic, dynamically adaptive defense mechanism. Rigorous experimental validations conducted in realistic operational environments substantiate the efficacy of our approach, which attained an unparalleled accuracy rate of 99.7% in the detection of APTs. The manifest advantages of this multifaceted strategy extend beyond mere detection efficacy, offering perspicacious insights into the operational modalities of APTs, thereby fostering the capability for preemptive cybersecurity initiatives. The contributions of this study are poised to significantly augment both academic discourse and practical applications in the persistent endeavor to fortify cybersecurity infrastructures against ever-escalating APT threats.

With the increasing demand for new technologies, the speed of delivering a packet is becoming the major focus of various industries. The generation of huge traffic is becoming the bottleneck of the current scenario. By segregating the control plane from the data plane, Software Defined Networking (SDN) is able to many such issues to certain extent. But the traffic engineering problem still remains as an important topic to focus on. Ternary Content Addressable Memory (TCAM) is capable of solving many such issues with the cost of its cost and power issues. For the effective use of TCAM, various algorithms and mechanisms have been proposed by many researchers. Bloom filter is one of the popular ways which is able to provide effective solutions after collaborating with TCAM. An algorithm has been proposed which is able to find the exact match of the entry in TCAM. The use of a CRC generator and multiple hashing tables makes it effective at finding the presence of a flow rule in TCAM, in very little time. The major focus is on the fast search procedure for finding the exact match of the flow rule. The number of false positives (FPs) is reduced to a lower percentage value as compared to the existing approaches.