Advances in the usage of information and communication technologies (ICT) has given rise to the popularity and success of cloud computing. Cloud computing offers advantages and opportunities for business users to migrate and leverage the scalability of the pay-as-you-go price model. However, outsourcing information and business applications to the cloud or a third party raises security and privacy concerns, which have become critical in adopting cloud implementation and services. Researchers and affected organisations have proposed different security approaches in the literature to tackle the present security flaws. The literature also provides an extensive review of security and privacy issues in cloud computing. Unfortunately, the works provided in the literature lack the flexibility in mitigating multiple threats without conflicting with cloud security objectives. The literature has further focused on only highlighting security and privacy issues without providing adequate technical approaches to mitigate such security and privacy threats. Conversely, studies that offer technical solutions to security threats have failed to explain how such security threats exist. This paper aims to introduce security and privacy issues that demand an adaptive solution approach without conflicting with existing or future cloud security. This paper reviews different works in the literature, taking into account its adaptiveness in mitigating against future reoccurring threats and showing how cloud security conflicts have invalidated their proposed models. The article further presents the security threats surrounding cloud computing from a user perspective using the STRIDE approach. Additionally, it provides an analysis of different inefficient solutions in the literature and offers recommendations in terms of implementing a secure, adaptive cloud environment.

The coronavirus pandemic has overburdened medical institutions, forcing physicians to diagnose and treat their patients remotely. Moreover, COVID-19 has made humans more conscious about their health, resulting in the extensive purchase of IoT-enabled medical devices. The rapid boom in the market worth of the internet of medical things (IoMT) captured cyber attackers’ attention. Like health, medical data is also sensitive and worth a lot on the dark web. Despite the fact that the patient’s health details have not been protected appropriately, letting the trespassers exploit them. The system administrator is unable to fortify security measures due to the limited storage capacity and computation power of the resource-constrained network devices’. Although various supervised and unsupervised machine learning algorithms have been developed to identify anomalies, the primary undertaking is to explore the swift progressing malicious attacks before they deteriorate the wellness system’s integrity. In this paper, a Dew-Cloud based model is designed to enable hierarchical federated learning (HFL). The proposed Dew-Cloud model provides a higher level of data privacy with greater availability of IoMT critical application(s). The hierarchical long-term memory (HLSTM) model is deployed at distributed Dew servers with a backend supported by cloud computing. Data pre-processing feature helps the proposed model achieve high training accuracy ($99.31 \%$) with minimum training loss (0.034). The experiment results demonstrate that the proposed HFL-HLSTM model is superior to existing schemes in terms of performance metrics such as accuracy, precision, recall, and f-score.

Free space optical (FSO) communication technology has established a reputation for itself as a next-generation network capable of delivering high-speed data access to end-users. In light of the upcoming launch of 5G services, this paper aims to evaluate the performance of the FSO system for the implementation of 5G technology in diverse geographical locations of India. The proposed 16x10 Gbps wavelength division multiplexing (WDM) based FSO link uses orthogonal frequency division multiplexing (OFDM) and digital signal processing (DSP) module to mitigate channel-induced limitations. Additionally the proposed FSO design also employs Erbium-doped fiber amplifier (EDFA) with optimized gain characteristics to ensure consistent transmission power levels. Using the data sourced from the Meteorological Department of India, the link analysis reveals that due to weak fog events in coastal areas effective link range of upto 10.75 km can be achieved while maintaining bit error rate (BER) of 10-9. Despite dominant foggy conditions in hilly, plains and desert areas, the proposed FSO link significantly limits the degrading effects of channel. Hence, in pursuit of futuristic smart city models, heterogeneous combination of FSO and 5G infrastructure can conveniently cover diverse geographical locations of India to support high-speed data connectivity.

Fog computing is an emerging technology which enables computing resources accessibility close to the end-users. It overcomes the drawbacks of available network bandwidth and delay in accessing the computing resources as observed in cloud computing environment. Resource allocation plays an important role in resource management in a fog computing environment. However, the existing traditional resource allocation techniques in fog computing do not guarantee less execution time, reduced energy consumption, and low latency requirements which is a pre-requisite for most of the modern fog computing-based applications. The complex fog computing environment requires a robust resource allocation technique to ensure the quality and optimal resource usage. Motivated from the aforementioned challenges and constraints, in this article, we propose a resource allocation technique for SDN-enabled fog computing with Collaborative Machine Learning (CML). The proposed CML model is integrated with the resource allocation technique for the SDN-enabled fog computing environment. The FogBus and iFogSim are deployed to test the results of the proposed technique using various performance evaluation metrics such as bandwidth usage, power consumption, latency, delay, and execution time. The results obtained are compared with other existing state-of-the-art techniques using the aforementioned performance evaluation metrics. The results obtained show that the proposed scheme reduces 19.35% processing time, 18.14% response time, and 25.29% time delay. Moreover, compared to the existing techniques, it reduces 21% execution time, 9% network usage, and 7% energy consumption.

Recently, the growing popularity of the Internet of Things (IoT) presents a promising opportunity not only for the expansion of various home automation systems but also for diverse industrial applications. By leveraging these benefits, automation is being implemented in industries, leading to the Industrial Internet of Things (IIoT). Although IoT simplifies daily activities that benefit human operations, it poses significant security challenges that warrant attention. Consequently, implementing an Intrusion Detection System (IDS) is a vital and effective solution. IDS aims to address the security and privacy challenges by detecting various IoT attacks. Various IDS methodologies, including those using Machine Learning (ML), Deep Learning (DL) and Large Language Models (LLMs), are employed to identify intrusions within the data; however, improvements to the detection systems are still needed. A literature survey on IDS in the IoT domain is provided, focusing primarily on the recent approaches used in the field. The survey aims to evaluate the literature, identify current trends, retest these approaches on recent data, and highlight open problems and future directions.

The Internet of Things (IoT) has gained significant importance due to its applicability in diverse environments. Another reason for the influence of the IoT is its use of a flexible and scalable framework. The extensive and diversified use of the IoT in the past few years has attracted cyber-criminals. They exploit the vulnerabilities of the open-source IoT framework due to the absentia of robust and standard security protocols, hence discouraging existing and potential stakeholders. The authors propose a binary classifier approach developed from a machine learning ensemble method to filter and dump malicious traffic to prevent malicious actors from accessing the IoT network and its peripherals. The gradient boosting machine (GBM) ensemble approach is used to train the binary classifier using pre-processed recorded data packets to detect the anomaly and prevent the IoT networks from zero-day attacks. The positive class performance metrics of the model resulted in an accuracy of 98.27%, a precision of 96.40%, and a recall of 95.70%. The simulation results prove the effectiveness of the proposed model against cyber threats, thus making it suitable for critical applications for the IoT.