ABSTRACT Over the past decade, as demand for cloud services has surged, the strategic selection of these services has become increasingly crucial. The growing complexity within the cloud industry underscores the urgent need for a robust model for choosing cloud services effectively. Users often struggle to make informed decisions due to the dynamic nature and varying quality of available cloud services. In response, this paper introduces a novel decision‐making approach aimed at optimizing the selection process by identifying the most suitable combination of cloud services. The focus is on integrating these services into a cohesive ensemble to better fulfill user requirements. In contrast to existing methodologies, our approach evaluates cloud services on a continuous scale, taking into account critical tasks such as workload balancing, storage management, and network resource handling. We propose a model for selecting optimal composite cloud services, which includes real‐time optimization and addresses the consideration of null values for Quality of Service (QoS)‐based attributes (e.g., response time, cost, availability, and reliability) in the dataset—a factor overlooked by current literature. The proposed algorithm is inspired by computational intelligence and driven by an evolutionary algorithm‐based approach that undergoes evaluation across multiple datasets. The results illustrate its superiority, showcasing its ability to outperform existing optimization‐based methods in terms of execution time.

In today's interconnected society, ensuring the safety and security of the public is crucial for protecting individuals from threats posed by criminals. Equally important is safeguarding individuals' privacy, which requires the development of a robust mechanism capable of withstanding attacks while remaining efficient. Surveillance cameras, such as Closed-Circuit Television (CCTV), have become essential tools in this endeavour. These cameras capture extensive visual data, often processed and stored on remote, centralized cloud servers. However, these methods have several shortcomings and are vulnerable to numerous security and privacy breaches. Various solutions have been proposed to address these challenges, each focusing solely on specific aspects of the system. However, none of them have comprehensively addressed the complete mechanism and discussed its resilience against various cyber threats. This paper proposes a secure and trusted decentralized surveillance mechanism utilizing hashgraph for faster, scalable, and secure consensus. The framework aims to defend against attacks such as DDoS and Sybil Attacks and ensures tamperproof logging of all events securely. For rapid identification, we employ I-frame generation from camera footage and a two-stage detection process: one at the camera end using the MobileNetV3-small model to forward necessary keyframes, and another on a private cloud-based analyzer. To safeguard individuals' privacy and ensure secure I-frame transmission while preventing attacks such as Known-plaintext and Replay attacks, we implement an XORing mechanism alongside AES (Advanced Encryption Standard) for I-frames.

With the expansion of Internet-of-Things (IoT), security of smart devices is becoming major or primary concern in today’s era. The increasing demand of consumer electronics due its recent evolution, the personal information that is shared is becoming valuable. In addition, the next generation of Industrial Internet of Things (IIoT) devices include features such as low cost, automation, intelligence provision, reduced overhead, efficiency, and remote interactions while communicating or transmitting information among themselves. There are very few authors who have focused on next gen IIoT while improving the efficiency along with providing the security among devices in the network. Therefore, we have proposed a hybrid trusted model by integrating objective model and fuzzy evaluation matrix method to ensure a secure and efficient transmission method among devices in the network. The proposed mechanism is simulated and experimented over various parameters such as detection ratio and network-related performance and functional tests compared to state-of-art solutions.

Industrial Internet-of-Things (IIoT) is a powerful IoT application, which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places, and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this article, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the MD to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.

Many IoT-based applications have inherited the artificial intelligence of things (AIoT) techniques to explore new services and benefits of smart recording and monitoring generated information. However, hundreds of hacking incidents caused by highly sophisticated attackers have generated serious risks, where they compromised various IoT sensors for their benefits, impeding the growth of AIoT. Various security schemes have been proposed in the literature; however, it is critical to determine the legitimacy of AIoT devices in real-time scenarios during the initial deployment of the network. Therefore, this article aims to provide a secure, reliable, and trusted decision-making scheme using multiattribute methods in collaborative AIoT. The proposed system uses backpropagation and Bayesian’s rule to ensure a fast and accurate decision. In addition, agent-based modeling and population-based modeling trust schemes are used to compute the legitimacy of the communicating model. Further, the proposed system is validated over various security measures against the various decision-based conventional methods such as Fuzzy c-means, REPTree, and random tree in terms of time, accuracy, replay attack, data falsification attack, recall, region of convergence, and F-Measure. The proposed mechanism achieves 93% improvement over accuracy and attack identification against existing mechanisms.

Industrial Internet of Things (IIoT) has emerged as a new paradigm in the era of smart systems where interactions and transmission of messages among various entities in an industrial ecosystem are done autonomously. This includes manufacturing of products, shipment process, storage of records, and counting the data, to name a few. However, the involvement of multiple cyber threats that accompany the smart devices may lead the organizations on a heavy risk of profits. Even though the number of cybersecurity approaches have been proposed to ensure a secure and efficient communication mechanism in IIoT systems, the identification of cybersecurity issues in IIoT applications while exchanging the data is still at its early stage. The aim of this article is to propose an efficient cybersecurity communication mechanism using the trust evaluation system of each device to make a correct decision during communication. In addition, the evaluated trust of each device is further analyzed and monitored through a blockchain-based mechanism. The proposed approach is validated against an existing scheme in terms of various security metrics, such as sensitivity of devices while communicating, convergence time of transmitting information and percentage, and probability of malicious devices in terms of data delivery rate, false authentication, and trust value computation.

Classical automated schemes in the industrial Internet of Things (IIoT) are challenged by the problems related to huge record storage and the way they respond. To properly manage the manufacturing settings, cognitive systems aim to find a way to efficiently adapt their actions based on uncertainty management and sensory data. However, due to the lack of existing IT integration, cognitive systems are not fully exploited by organizations. In this article, we provide a novel decision-making process in industrial informatics during information transmission, manufacturing, and storing records through the simple additive weighting and analytic hierarchy process. The proposed mechanism is analyzed and validated rigorously using various sensing and decision-making parameters against a baseline solution in industrial parameter settings. The simulation results suggest that the proposed mechanism leads to 87% efficiency in terms of better detection of the sensor node, decision-making, and alteration of transmitted data during analyses of product manufacturing in the IIoT.

The industrial Internet-of-Things (IIoT) is a powerful Internet of Things (IoT) application that enables industrial growth by ensuring transparent communication among the various entities of a company such as the manufacturing locations, design hubs, and packaging units. However, current industrial architectures are unable to efficiently deal with advanced security issues that come with this communication due to the distributed and expandable nature of IIoT networks. Furthermore, from a security perspective, malicious devices with the objective of modifying data from within the premises of the network pose a high risk for the IIoT. Therefore, introducing intelligent decision-making models to the IIoT can enhance our ability to examine any collected data in a more structured, efficient, and secure manner. In this article, we provide a decision-making model for securing IIoT data. The proposed model, based on the Technique for Order Preference by Similarity to the Ideal Solution, can provide secure information transmission and recording/storage using various communicating parameters. The degree of trust of the IoT devices is analyzed using these parameters. Simple additive weighting is integrated into the proposed model to remove inefficient and ill-structured parameters. The proposed model is validated using various spectrum sensing and security parameters against a baseline method for the IIoT. Simulation results show that the proposed model is approximately 85% more efficient in identifying malicious nodes and denial-of-service threats compared to the baseline method.

Cognitive Radio Network (CRN) is considered as a viable solution on Internet of Vehicle (IoV) where objects equipped with cognition make decisions intelligently through the understanding of both social and physical worlds. However, the spectrum availability and data sharing/transferring among vehicles are critical improving services and driving safety metrics where the presence of Malicious Devices (MD) further degrade the network performance. Recently, a blockchain technique in CRN-based IoV has been introduced to prevent data alteration from these MD and allowing the vehicles to track both legal and illegal activities in the network. In this paper, we provide the security to IoV during spectrum sensing and information transmission using CRN by sensing the channels through a decision-making technique known as Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), a technique that evokes the trust of its Cognitive Users (CU) by analyzing certain predefined attributes. Further, blockchain is maintained in the network to trace every activity of stored information. The proposed mechanism is validated rigorously against several security metrics using various spectrum sensing and security parameters against a baseline solution in IoV. Extensive simulations suggest that our proposed mechanism is approximately 70% more efficient in terms of malicious nodes identification and DoS threat against the baseline mechanism.

The automated techniques enabled with Artificial Neural Networks (ANN), Internet of Things (IoT), and cloud‐based services affect the real‐time analysis and processing of information in a variety of applications. In addition, multihoming is a type of network that combines various types of networks into a single environment while managing a huge amount of data. Nowadays, the big data processing and monitoring in multihoming networks provide less attention while reducing the security risk and efficiency during processing or monitoring the information. The use of AI‐based systems in multihoming big data with IoT‐ and AI‐integrated systems may benefit in various aspects. Although multihoming security issues and their analysis have been well studied by various scientists and researchers; however, not much attention is paid towards big data security processing in multihoming especially using automated techniques and systems. The aim of this paper is to propose an IoT‐based artificial network to process and compute big data processing by ensuring a secure communication multihoming network using the Bayesian Rule (BR) and Levenberg‐Marquardt (LM) algorithms. Further, the efficiency and effect on multihoming information processing using an AI‐assisted mechanism are experimented over various parameters such as classification accuracy, classification time, specificity, sensitivity, ROC, and F‐measure.

Recently, Wireless Mesh Network is deliberated as a significant technology due to its self-healing and self-organizing characteristics. In WMN, data is forwarded through multiple hops to the destination node. One of the factors that impact the network performance is the secure communication delay. An authentication technique having significant delay may enhance the possibility of several security threats. A number of authentication protocols have been proposed in the literature but if existing practices are deliberated then to optimize one of the constraint other parameters are affected radically. In this manuscript, a Diffie-Hellman elliptic curve technique is used over WMN which reduces latency and computational overhead issues. Further to prove the integrity of proposed work, the performance is measured theoretically and analyzed experimentally over ns2 simulator.

Recently, Wireless Mesh Network is deliberated as a significant technology due to its self-healing, self-configuring and self-organizing features. The Multi-hop nature of WMN nodes has the potential to forward the data traffic to their intended destination node without the involvement of source node. One of the factors that impact the WMN performance is secure communication process. Various secure authentication mechanisms have been projected however, authentication delay and communication overheads are listed as the main security issues of these approaches. In this article, an efficient and fast authentication protocol have been proposed to reduce the response time, computation overhead during encryption/decryption process of the routing messages. Further various simulations with erratic nodes have been conducted to endorse the enhancement of proposed solution against existing protocols. Moreover, performance comparison between existing and proposed schemes have been analyzed in detail showing that proposed approach has achieved better outcome against convinced consequences.