The rapid growth of the Internet of Things (IoT) has led to revolutionary innovations in many fields; however, it has also resulted in significant security and privacy issues due to the resource limitations and distributed nature of IoT networks. Traditional cryptographic techniques or machine learning-based anomaly detection systems do not jointly provide data privacy and resilience to threats in real time. The existing methods, such as Homomorphic Encryption (HE), offer a high computation cost for performing encryption. Furthermore, Long Short-Term Memory (LSTM) networks can predict an anomaly profile instead of performing encryption. To address these shortcomings, this paper proposes NeuroCrypt. This new hybrid system combines Fully Homomorphic Encryption (FHE) with LSTM-based encrypted anomaly detection and supplements it with blockchain-based dynamic key management and multi-factor authentication. The architecture targets edge and fog computing settings using, among other techniques, ciphertext packing, model quantisation, and parallelised encrypted operations. The performance of the proposed framework has been evaluated on a real dataset. The results show that the accuracy in the proposed framework is 99.2% compared to existing techniques such as HE-based DNN, FL-based models, and LSTM IDS. Conclusively, NeuroCrypt provides a privacy-preserving, effective, and scalable solution to real-time threat abatement in IoT networks.

With the rapid growth of cloud services, it is crucial to have strong assessment methods in place to rate these services according to their performance, dependability, and security. This study introduces a holistic methodology that utilizes advanced deep learning (DL) algorithms to prioritize and evaluate cloud services. Our model incorporates many assessment criteria, including latency, throughput, availability, and security measures. These criteria are trained using a varied collection of performance measurements from cloud services. We validate the effectiveness of our methodology by comprehensive experiments, attaining greater precision and significance in ranking compared to conventional approaches. The DL model underwent evaluation using a testing set, resulting in a mean absolute error (MAE) of 0.15 in ranking scores. The algorithm regularly achieved superior results compared to conventional ranking approaches, particularly in situations where performance measures varied. Through the incorporation of security metrics, the model successfully assessed and ranked cloud service providers (CSPs) based not only on their performance, but also on their ability to withstand security threats. The DL technique exhibited more flexibility and contextual awareness in its rankings, hence showcasing its superiority in adjusting to real-time data. The research conducted a comparison between DL-based rankings and conventional methodologies and industry standards, demonstrating its superiority in effectively adjusting to real-time data. The study technique entails gathering data from many CSPs to construct a resilient framework for evaluating cloud services using DL models. The data is obtained from publicly available performance statistics, cloud monitoring tools, user evaluations, and problem reports. The collection comprises both structured and unstructured data, including essential performance and accuracy indicators.

Service selection has emerged as a prominent challenge due to the flourishing demand for computing services and the dynamic nature of its resources. The increasing demand for cloud services makes it challenging to choose a provider offering equal services and facilities at costs that match those of competing providers. Apart from educating customers in the process of choosing cloud services, trust mechanisms include user reviews, reputation systems, and certifications assist to boost consumers’ confidence in cloud services. The service measurement index (SMI) offers a disciplined framework combining both functional and non-functional quality of service indicators concurrently, therefore easing decision-making. The main emphasis of the research is on the fundamental elements influencing the choice of cloud services in the present environment, the identification of extra characteristics of cloud services transcending SMI, and the identification of the most suitable approach for some services. By means of the measurement of customer enjoyment and experience, QoS traits provide some insight on the impact of trust mechanisms on service acceptance. Comparisons of SMI and QoS measurements before and after trust mechanism deployment provide insightful analysis. Empirical research guides these comparisons. This study aims to clarify the interactions among QoS, trust mechanisms, and cloud service adoption as well as highlight the implications these elements have for customers and service providers. Furthermore, presented in this paper is an algorithm using a comprehensive method to trust estimation in order to ascertain the degree of confidence worthiness of certain people.