Cloud computing revolutionized the delivery of IT services by providing unparalleled scalability, flexibility, and cost savings. The expansion of cloud computing also attracts Distributed Denial of Service (DDoS) attackers, causing them to shift their targets from traditional server systems to cloud infrastructure. DDoS attacks bombard systems with malicious traffic, creating a significant threat to the availability of cloud services. In the state-of-the-art solutions, we found that resource isolation for legitimate users plays a crucial role in maintaining the service availability under DDoS attacks. By isolating resources, target services are able to maintain their functionality for legitimate users without experiencing substantial interruption, even in the presence of a DDoS attack. In this work, we proposed a robust defense system against DDoS attacks that employs three strategies: categorizing incoming requests based on the frequency of their submissions to different services, allocating resources for distinct services, and implementing a microservice architecture within a cloud infrastructure based on containers. The incoming requests are categorized into four distinct categories: red, orange, yellow, and green. Each category was determined by the number of requests made for a specific service in comparison to threshold values. Subsequently, the requests were served in separate containers. To implement microservice architecture, we deploy each web service on distinct containers. This implies that requests from various users for distinct services get served in separate containers. We tested this approach in three distinct scenarios (E1, E2, and E3) by varying the number of web services at the target infrastructure (2 services on E1, 3 services on E2, and 5 services on E3). By this, we test the scalability of the proposed defense system in the presence of DDoS attacks. The experimental results show that the proposed defense system is highly effective, maintaining service availability up to 90% even under DDoS attacks. This result demonstrates the system's ability to keep services running smoothly for legitimate users, even in the presence of DDoS attacks.

Abstract This article examines the impact of utilizing generative artificial intelligence optimizations in automating the content generation process. This instance involves the identification of fraudulent content, which is often characterized by dynamic patterns, in addition to content production. The generated contents are constrained, which limits their dimensionality. In this scenario, duplicated contents are eliminated from the automatic creations. Furthermore, the generated ratios are utilized to discover current patterns with minimized losses and errors, hence enhancing the accuracy of generative contents. Furthermore, while analysing the created patterns, we detect a significant discrepancy in lead durations, resulting in the generation of high scores for relevant information. In order to test the results using generative tools, the adversarial network codes are employed in four scenarios. These scenarios involve generating large patterns and reducing the dynamic patterns with an enhanced accuracy of 97% in the projected model. This is in contrast to the existing approach, which only provides a content accuracy of 77% after detecting fraud.

Abstract The most important and difficult challenge the digital society has recently faced is ensuring data privacy and security in cloud‐based Internet of Things (IoT) technologies. As a result, many researchers believe that the blockchain's Distributed Ledger Technology (DLT) is a good choice for various clever applications. Nevertheless, it encountered constraints and difficulties with elevated computing expenses, temporal demands, operational intricacy, and diminished security. Therefore, the proposed work aims to develop a Decentralized Identifiable Distributed Ledger Technology‐Blockchain (DIDLT‐BC) framework that is intelligent and effective, requiring the least amount of computing complexity to ensure cloud IoT system safety. In this case, the Rabin algorithm produces the digital signature needed to start the transaction. The public and private keys are then created to verify the transactions. The block is then built using the DIDLT model, which includes the block header information, hash code, timestamp, nonce message, and transaction list. The primary purpose of the Blockchain Consent Algorithm (BCA) is to find solutions for numerous unreliable nodes with varying hash values. The novel contribution of this work is to incorporate the operations of Rabin digital data signature generation, DIDLT‐based blockchain construction, and BCA algorithms for ensuring overall data security in IoT networks. With proper digital signature generation, key generation, blockchain construction and validation operations, secured data storage and retrieval are enabled in the cloud‐IoT systems. By using this integrated DIDLT‐BCA model, the security performance of the proposed system is greatly improved with 98% security, less execution time of up to 150 ms, and reduced mining time of up to 0.98 s.

ABSTRACT Cloud computing (CC) has emerged as a transformative technology, offering customers unprecedented access to extensive computing resources and the diverse services for hosting various applications. However, this environment comes with several challenges. While cloud users seek optimal resources to cater to their specific requirements, the prevalent scenario often involves trading more monetary resources for less computational time. Existing algorithms, mostly focused on optimizing individual variables, lack a holistic approach. Addressing these issues necessitates a new approach to combine these conflicting objectives. This research focuses on developing and improving a dynamic task‐processing framework that can find and use the optimal resources in real‐time. The focus extends to running applications of different types and levels of complexity on virtual machines (VMs) using the multi‐objective adaptive particle swarm optimization (MAPSO) algorithm. The MAPSO handles the multi‐objective problem using the weighted‐sum approach. The system operates within predefined constraints to meet users' specific time limitations. Through comprehensive simulations on a wide range of datasets, the proposed methodology yields a set of non‐dominated optimal solutions. This outcome is instrumental in improving critical quality of service (QoS) metrics, including processing time, execution costs, throughput, and task rejection ratios. The effectiveness of the MAPSO‐based approach are evident in its capacity to improve these numerous QoS aspects, including processing time, execution cost, throughput, and task rejection ratio compared and clearly shows that it is superior to the existing algorithms, such as ant colony optimization (ACO), hybrid version of bat optimization algorithm and particle swarm optimization (BOA+PSO), and hybrid grey wolf optimization and artificial bee colony (GWO+ABC). The time complexity for completing the tasks of the MAPSO algorithm is reduced by 5%, executes each schedule's tasks faster by 5% to 13%, and calculated execution costs also get reduced when compared to ACO, BOA+PSO, and GWO+ABC. Moreover, the suggested methodology convincingly outperforms existing state‐of‐the‐art methods in terms of computational performance. This study pioneers a unique solution in cloud service provisioning by integrating multi‐objective optimization within a real‐time resource allocation framework. The resulting combination of intelligent resource allocation and enhanced QoS metrics promises to change the way cloud‐based application deployment is done. Ultimately, this work establishes a paradigm shift in balancing resource allocation and user‐centric QoS optimization in cloud computing environments.

In this paper the need of biometric authentication with synthetic data is analyzed for increasing the security of data in each transmission systems. Since more biometric patterns are represented the complexity of recognition changes where low security features are enabled in transmission process. Hence the process of increasing security is carried out with image biometric patterns where synthetic data is created with explainable artificial intelligence technique thereby appropriate decisions are made. Further sample data is generated at each case thereby all changing representations are minimized with increase in original image set values. Moreover the data flows at each identified biometric patterns are increased where partial decisive strategies are followed in proposed approach. Further more complete interpretabilities that are present in captured images or biometric patterns are reduced thus generated data is maximized to all end users. To verify the outcome of proposed approach four scenarios with comparative performance metrics are simulated where from the comparative analysis it is found that the proposed approach is less robust and complex at a rate of 4% and 6% respectively.

Identifying objects in aircraft monitoring systems poses significant challenges due to the presence of extreme loading conditions. Despite the presence of several sensor units, the transmission of precise data to multiple data units is hindered by an increase in time intervals. Therefore, the suggested methodology is specifically developed for the purpose of generating digital replicas for aeronautical applications, wherein an aero transfer function is correlated with the digital twins. Mapping functions are utilized in the monitoring of diverse parameters that are associated with the identification of objects inside data transmission networks, with the aim of minimizing uncertainty. The suggested system model is enhanced by incorporating analytical representations and deep learning methods, resulting in the provision of zero point twin functionalities. The present study investigates the aforementioned integrated procedure through the analysis of four different situations. In these settings, an aero communication tool box is employed to transform the device configuration into simulation outputs. The results obtained from the comparison of these scenarios reveal that the projected model significantly enhances the maintenance period while minimizing data errors.

The productivity of agriculture plays a critical role in the Indian economy. Growing crop production is a critical responsibility nowadays to accommodate citizen demand and provide farmers with greater rewards. Therefore, a machine learning (ML) technique is employed to more precisely identify diseases and pests on leaves and other crop parts. This paper introduces a machine learning-based system in early crop disease and pest detection using image processing and optimization. Initially, the data is collected from the CCMT plant disease Dataset. Image augmentation techniques such as rotation, flipping, and zooming are utilized to make the dataset wholesome. After amplification, the pre-processing is carried out on these images. Noise reduction as well as enhancing quality are done by Adaptive Bilateral Filter. Lanczos interpolation technique resized it and normalization is done so that the analysis can proceed. Kapur's Entropy-based Whale Optimization is introduced for the segmentation of the image efficiently by dividing diseased areas into segments. The features are extracted using the Gray Level Co-occurrence Matrix, which assesses relationships among the pixels and produces an appropriate feature matrix for color images. This processed data then feeds into a Moth-Flame Optimized Recurrent Neural Network for crop disease and pest detection. These results achieved high accuracy levels at 98.4% for cashews, 98.3% for cassava, 98.5% for maize, and 96.8% for tomato crops, outperforming all the reported techniques.

In this paper, a realization technique is designed with a unique analytical model for transmitting multimedia data to appropriate end users. Transmission of multimedia data to all end users through a variety of visualization methods is the foundation of future computer systems. Yet, highly limited system resources prevent the updating of the methods used to manage multimedia data. Hence, a high-end visualization technique where uncertainties are eliminated is required for the visualization process with a multimedia system. As a result, the suggested system incorporates a clustering technique utilizing an analytical framework to ensure a high degree of transmission for all multimedia data. The technical contribution of the proposed method depends on a multimedia visualization process that takes place with high security features by including necessary parametric relationships such as occurrence of jitter, data density points, time period, multimedia storage, data smoothness and distance. For the established parametric relationship the validation methodology is integrated with a hierarchical clustering algorithm, thereby transmitting every clustered data with high security feature, thereby the examined outcomes under five scenarios proves that data security which is represented by simulation outcomes is improved to 88% as compared to the existing approach.

Abstract The advancement of information technology, the Internet of Things (IoT), and several miniaturize equipment's enhances the healthcare field that provides real‐time patient monitoring, which helps to provide medication anywhere and anytime. However, accurate detection is still a challenging task for which an effective classification model is introduced in this research. The proposed method is the Enhanced Hunt optimization based Deep convolutional neural network (Enhanced Hunt based‐Deep CNN), in which the Enhanced Hunt optimization algorithm (EHOA) is developed by fusing the hunting habit of the predator and the herding characteristics of herding dog for enhancing the global optimal convergence. Here, the ECG signal from the individuals is collected using the IoT network and stored in the Hospital server, which is accessed by the doctor when requested, the classification is performed using the Enhanced Hunt based‐Deep CNN and the performance revealed the effectiveness with the accuracy, sensitivity, and specificity of 95.33%, 94.92%, and 97.57%.

Abstract Vehicular Adhoc Networks (VANET) facilitate inter‐vehicle communication using their dedicated connection infrastructure. Numerous advantages and applications exist associated with this technology, with road safety particularly noteworthy. Ensuring the transportation and security of information is crucial in the majority of networks, similar to other contexts. The security of VANETs poses a significant challenge due to the presence of various types of attacks that threaten the communication infrastructure of mobile vehicles. This research paper introduces a new security scheme known as the Soft Computing‐based Secure Protocol for VANET Environment (SC‐SPVE) method, which aims to tackle security challenges. The SC‐SPVE technique integrates an adaptive neuro‐fuzzy inference system and particle swarm optimisation to identify different attacks in VANETs efficiently. The proposed SC‐SPVE method yielded the following average outcomes: a throughput of 148.71 kilobits per second, a delay of 23.60 ms, a packet delivery ratio of 95.62%, a precision of 92.80%, an accuracy of 99.55%, a sensitivity of 98.25%, a specificity of 99.65%, and a detection time of 6.76 ms using the Network Simulator NS2.

In the process of drone development, most of the current state systems’ design is based on high-weight functionalities. Due to high-weight functionalities, it is observed that if the drone drops at a particular point, the entire design is fragmented. Also, well-defined functionalities of drones for a specific application can only be designed if radial functionalities are defined at proper angles. Therefore, this article addresses the issues present in the existing method using the CRA algorithm, where radial functions, represented in terms of input and hidden weighting functions, are explored utterly. Additionally, a novel analytical procedure that establishes the coverage area for the data transfer approach has been incorporated into the drones’ architecture. Additionally, employing motion signatures and a special identification system, the developed drone system can function along various paths. To evaluate the effectiveness of the suggested system, three scenarios are organized as a basic functionality model. With the right scattering ratio, the comparison inscriptions show that the proposed approach can achieve an 82% success rate.

Abstract Cloud computing is an environment where everything is provided as a service based on demand. It follows pay as per the used model in which the service consumer needs to pay for what they have consumed. Due to the increased dependence on digitalization, the number of consumers and providers tends to grow tremendously. The consumer who needs the service from the provider is not sure about the specified service outcome, and it is too hard for them to monitor and manage the service. Hence, a trusted third party called a trusted cloud broker (TCB) is introduced for managing the services. The service level agreements (SLA) management and reputation estimation framework is proposed, which includes three phases such as (i) SLA establishment between the three parties, (ii) violation detection by comparing the observed value of the TCB and (iii) the reputation and penalty estimation of the service. The novel TCB is created to monitor the deployed services, ensuring the achievement of SLA. The TCB observes the values and estimates the reputation value for each service. It is compared with the provider log‐based reputation value and found that the proposed model provides a more precise reputation value for the service providers.

The applications such as the remote communication and the control system are in critically integrated arrangement. The controlling of these network is specified by supervisory control and data acquisition (SCADA) systems. This study discusses about the attack prediction and classification process by using an enhanced model of machine learning technology. The attack types are classified by the optimal selection of features extracted from the sensor data. In this, the features are labelled and cluster between the matrixes are extracted. These cluster forms the initial processing of attack identification which prevents the mismatched result. This clustering of data is performed by mean‐shift clustering algorithm. From that clustered data, the features that are irrelevant for classification process is identified and suppressed by using the genetically seeded flora optimisation algorithm. In this optimisation process, the flora seeds are selected genetically to select best features. Then, from that optimally selected clustered data, the relevancy vector is predicted and the types are classified. The classification process is performed by the Boltzmann machine learning algorithm. The classified results of the proposed method for testing SCADA dataset are analysed and the performance metrics are evaluated and compared with the state‐of‐the‐art methods.